polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

scansani-etal-2017-enhancing

Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7901/

Scansani, Randy and Bernardini, Silvia and Ferraresi, Adriano and Gaspari, Federico and Soffritti, Marcello

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

1--10

This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each language combination, an in-domain bilingual corpus including course unit and degree program descriptions is used to train an MT engine, whose output is then compared to a baseline engine trained on the Europarl corpus. In a subsequent experiment, a bilingual terminology database is added to the training sets in both engines and its impact on the output quality is evaluated based on BLEU and post-editing score. Results suggest that the use of domain-specific corpora boosts the engines quality for both language combinations, especially for German-English, whereas adding terminological resources does not seem to bring notable benefits.

null

10.26615/978-954-452-042-7_001

null

56,019

inproceedings

toledo-baez-etal-2017-experiments

Experiments in Non-Coherent Post-editing

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7902/

Toledo B{\'a}ez, Cristina and Schaeffer, Moritz and Carl, Michael

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

11--20

Market pressure on translation productivity joined with technological innovation is likely to fragment and decontextualise translation jobs even more than is cur-rently the case. Many different translators increasingly work on one document at different places, collaboratively working in the cloud. This paper investigates the effect of decontextualised source texts on behaviour by comparing post-editing of sequentially ordered sentences with shuffled sentences from two different texts. The findings suggest that there is little or no effect of the decontextualised source texts on behaviour.

null

10.26615/978-954-452-042-7_002

null

56,020

inproceedings

ahrenberg-2017-comparing

Comparing Machine Translation and Human Translation: A Case Study

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7903/

Ahrenberg, Lars

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

21--28

As machine translation technology improves comparisons to human performance are often made in quite general and exaggerated terms. Thus, it is important to be able to account for differences accurately. This paper reports a simple, descriptive scheme for comparing translations and applies it to two translations of a British opinion article published in March, 2017. One is a human translation (HT) into Swedish, and the other a machine translation (MT). While the comparison is limited to one text, the results are indicative of current limitations in MT.

null

10.26615/978-954-452-042-7_003

null

56,021

inproceedings

ustaszewski-stauder-2017-transbank

{T}rans{B}ank: Metadata as the Missing Link between {NLP} and Traditional Translation Studies

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7904/

Ustaszewski, Michael and Stauder, Andy

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

29--35

Despite the growing importance of data in translation, there is no data repository that equally meets the requirements of translation industry and academia alike. Therefore, we plan to develop a freely available, multilingual and expandable bank of translations and their source texts aligned at the sentence level. Special emphasis will be placed on the labelling of metadata that precisely describe the relations between translated texts and their originals. This metadata-centric approach gives users the opportunity to compile and download custom corpora on demand. Such a general-purpose data repository may help to bridge the gap between translation theory and the language industry, including translation technology providers and NLP.

null

10.26615/978-954-452-042-7_004

null

56,022

inproceedings

temnikova-etal-2017-interpreting

Interpreting Strategies Annotation in the {WAW} Corpus

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7905/

Temnikova, Irina and Abdelali, Ahmed and Hedaya, Samy and Vogel, Stephan and Al Daher, Aishah

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

36--43

With the aim to teach our automatic speech-to-text translation system human interpreting strategies, our first step is to identify which interpreting strategies are most often used in the language pair of our interest (English-Arabic). In this article we run an automatic analysis of a corpus of parallel speeches and their human interpretations, and provide the results of manually annotating the human interpreting strategies in a sample of the corpus. We give a glimpse of the corpus, whose value surpasses the fact that it contains a high number of scientific speeches with their interpretations from English into Arabic, as it also provides rich information about the interpreters. We also discuss the difficulties, which we encountered on our way, as well as our solutions to them: our methodology for manual re-segmentation and alignment of parallel segments, the choice of annotation tool, and the annotation procedure. Our annotation findings explain the previously extracted specific statistical features of the interpreted corpus (compared with a translation one) as well as the quality of interpretation provided by different interpreters.

null

10.26615/978-954-452-042-7_005

null

56,023

inproceedings

silvestre-baquero-mitkov-2017-translation

Translation Memory Systems Have a Long Way to Go

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7906/

Silvestre Baquero, Andrea and Mitkov, Ruslan

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

44--51

The TM memory systems changed the work of translators and now the translators not benefiting from these tools are a tiny minority. These tools operate on fuzzy (surface) matching mostly and cannot benefit from already translated texts which are synonymous to (or paraphrased versions of) the text to be translated. The match score is mostly based on character-string similarity, calculated through Levenshtein distance. The TM tools have difficulties with detecting similarities even in sentences which represent a minor revision of sentences already available in the translation memory. This shortcoming of the current TM systems was the subject of the present study and was empirically proven in the experiments we conducted. To this end, we compiled a small translation memory (English-Spanish) and applied several lexical and syntactic transformation rules to the source sentences with both English and Spanish being the source language. The results of this study show that current TM systems have a long way to go and highlight the need for TM systems equipped with NLP capabilities which will offer the translator the advantage of he/she not having to translate a sentence again if an almost identical sentence has already been already translated.

null

10.26615/978-954-452-042-7_006

null

56,024

inproceedings

elgabou-kazakov-2017-building

Building Dialectal {A}rabic Corpora

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7907/

Elgabou, Hani and Kazakov, Dimitar

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

52--57

The aim of this research is to identify local Arabic dialects in texts from social media (Twitter) and link them to specific geographic areas. Dialect identification is studied as a subset of the task of language identification. The proposed method is based on unsupervised learning using simultaneously lexical and geographic distance. While this study focusses on Libyan dialects, the approach is general, and could produce resources to support human translators and interpreters when dealing with vernaculars rather than standard Arabic.

null

10.26615/978-954-452-042-7_007

null

56,025

inproceedings

mrini-benjamin-2017-towards

Towards Producing Human-Validated Translation Resources for the {F}ula language through {W}ord{N}et Linking

Temnikova, Irina and Orasan, Constantin and Pastor, Gloria Corpas and Vogel, Stephan

sep

2017

Varna, Bulgaria

Association for Computational Linguistics, Shoumen, Bulgaria

https://aclanthology.org/W17-7908/

Mrini, Khalil and Benjamin, Martin

Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

58--64

We propose methods to link automatically parsed linguistic data to the WordNet. We apply these methods on a trilingual dictionary in Fula, English and French. Dictionary entry parsing is used to collect the linguistic data. Then we connect it to the Open Multilingual WordNet (OMW) through two attempts, and use confidence scores to quantify accuracy. We obtained 11,000 entries in parsing and linked about 58{\%} to the OMW on the first attempt, and an additional 14{\%} in the second one. These links are due to be validated by Fula speakers before being added to the Kamusi Project`s database.

null

10.26615/978-954-452-042-7_008

null

56,026

inproceedings

eric-2017-document

Document retrieval and question answering in medical documents. A large-scale corpus challenge.

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8001/

Eric, Curea

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

1--7

Whenever employed on large datasets, information retrieval works by isolating a subset of documents from the larger dataset and then proceeding with low-level processing of the text. This is usually carried out by means of adding index-terms to each document in the collection. In this paper we deal with automatic document classification and index-term detection applied on large-scale medical corpora. In our methodology we employ a linear classifier and we test our results on the BioASQ training corpora, which is a collection of 12 million MeSH-indexed medical abstracts. We cover both term-indexing, result retrieval and result ranking based on distributed word representations.

null

10.26615/978-954-452-044-1_001

null

56,028

inproceedings

mitrofan-ion-2017-adapting

Adapting the {TTL} {R}omanian {POS} Tagger to the Biomedical Domain

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8002/

Mitrofan, Maria and Ion, Radu

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

8--14

This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97{\%} when TTL`s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.

null

10.26615/978-954-452-044-1_002

null

56,029

inproceedings

freitag-etal-2017-discourse

Discourse-Wide Extraction of Assay Frames from the Biological Literature

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8003/

Freitag, Dayne and Kalmar, Paul and Yeh, Eric

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

15--23

We consider the problem of populating multi-part knowledge frames from textual information distributed over multiple sentences in a document. We present a corpus constructed by aligning papers from the cellular signaling literature to a collection of approximately 50,000 reference frames curated by hand as part of a decade-long project. We present and evaluate two approaches to the challenging problem of reconstructing these frames, which formalize biological assays described in the literature. One approach is based on classifying candidate records nominated by sentence-local entity co-occurrence. In the second approach, we introduce a novel virtual register machine traverses an article and generates frames, trained on our reference data. Our evaluations show that success in the task ultimately hinges on an integration of evidence spread across the discourse.

null

10.26615/978-954-452-044-1_003

null

56,030

inproceedings

zubke-2017-classification

Classification based extraction of numeric values from clinical narratives

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8004/

Zubke, Maximilian

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

24--31

The robust extraction of numeric values from clinical narratives is a well known problem in clinical data warehouses. In this paper we describe a dynamic and domain-independent approach to deliver numerical described values from clinical narratives. In contrast to alternative systems, we neither use manual defined rules nor any kind of ontologies or nomenclatures. Instead we propose a topic-based system, that tackles the information extraction as a text classification problem. Hence we use machine learning to identify the crucial context features of a topic-specific numeric value by a given set of example sentences, so that the manual effort reduces to the selection of appropriate sample sentences. We describe context features of a certain numeric value by term frequency vectors which are generated by multiple document segmentation procedures. Due to this simultaneous segmentation approaches, there can be more than one context vector for a numeric value. In those cases, we choose the context vector with the highest classification confidence and suppress the rest. To test our approach, we used a dataset from a german hospital containing 12,743 narrative reports about laboratory results of Leukemia patients. We used Support Vector Machines (SVM) for classification and achieved an average accuracy of 96{\%} on a manually labeled subset of 2073 documents, using 10-fold cross validation. This is a significant improvement over an alternative rule based system.

null

10.26615/978-954-452-044-1_004

null

56,031

inproceedings

grabar-hamon-2017-understanding

Understanding of unknown medical words

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8005/

Grabar, Natalia and Hamon, Thierry

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

32--41

We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some of them distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.

null

10.26615/978-954-452-044-1_005

null

56,032

inproceedings

yimam-etal-2017-entity

Entity-Centric Information Access with Human in the Loop for the Biomedical Domain

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8006/

Yimam, Seid Muhie and Remus, Steffen and Panchenko, Alexander and Holzinger, Andreas and Biemann, Chris

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

42--48

In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.

null

10.26615/978-954-452-044-1_006

null

56,033

inproceedings

bellon-rodriguez-esteban-2017-one

One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8007/

Bellon, Victor and Rodriguez-Esteban, Raul

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

49--54

We explored a new approach to named entity recognition based on hundreds of machine learning models, each trained to distinguish a single entity, and showed its application to gene name identification (GNI). The rationale for our approach, which we named {\textquotedblleft}one model per entity{\textquotedblright} (OMPE), was that increasing the number of models would make the learning task easier for each individual model. Our training strategy leveraged freely-available database annotations instead of manually-annotated corpora. While its performance in our proof-of-concept was disappointing, we believe that there is enough room for improvement that such approaches could reach competitive performance while eliminating the cost of creating costly training corpora.

null

10.26615/978-954-452-044-1_007

null

56,034

inproceedings

thorne-klinger-2017-towards

Towards Confidence Estimation for Typed Protein-Protein Relation Extraction

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8008/

Thorne, Camilo and Klinger, Roman

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

55--63

Systems which build on top of information extraction are typically challenged to extract knowledge that, while correct, is not yet well-known. We hypothesize that a good confidence measure for relational information has the property that such interesting information is found between information extracted with very high confidence and very low confidence. We discuss confidence estimation for the domain of biomedical protein-protein relation discovery in biomedical literature. As facts reported in papers take some time to be validated and recorded in biomedical databases, such task gives rise to large quantities of unknown but potentially true candidate relations. It is thus important to rank them based on supporting evidence rather than discard them. In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods. We show that the most straight-forward approach, a combination of different confidence measures from pipeline modules seems not to work well. We discuss this negative result and pinpoint potential future research directions.

null

10.26615/978-954-452-044-1_008

null

56,035

inproceedings

boytcheva-etal-2017-identification

Identification of Risk Factors in Clinical Texts through Association Rules

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8009/

Boytcheva, Svetla and Nikolova, Ivelina and Angelova, Galia and Angelov, Zhivko

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

64--72

We describe a method which extracts Association Rules from texts in order to recognise verbalisations of risk factors. Usually some basic vocabulary about risk factors is known but medical conditions are expressed in clinical narratives with much higher variety. We propose an approach for data-driven learning of specialised medical vocabulary which, once collected, enables early alerting of potentially affected patients. The method is illustrated by experimens with clinical records of patients with Chronic Obstructive Pulmonary Disease (COPD) and comorbidity of CORD, Diabetes Melitus and Schizophrenia. Our input data come from the Bulgarian Diabetic Register, which is built using a pseudonymised collection of outpatient records for about 500,000 diabetic patients. The generated Association Rules for CORD are analysed in the context of demographic, gender, and age information. Valuable anounts of meaningful words, signalling risk factors, are discovered with high precision and confidence.

null

10.26615/978-954-452-044-1_009

null

56,036

inproceedings

hamon-etal-2017-pomelo

{POMELO}: {M}edline corpus with manually annotated food-drug interactions

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8010/

Hamon, Thierry and Tabanou, Vincent and Mougin, Fleur and Grabar, Natalia and Thiessard, Frantz

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

73--80

When patients take more than one medication, they may be at risk of drug interactions, which means that a given drug can cause unexpected effects when taken in combination with other drugs. Similar effects may occur when drugs are taken together with some food or beverages. For instance, grapefruit has interactions with several drugs, because its active ingredients inhibit enzymes involved in the drugs metabolism and can then cause an excessive dosage of these drugs. Yet, information on food/drug interactions is poorly researched. The current research is mainly provided by the medical domain and a very tentative work is provided by computer sciences and NLP domains. One factor that motivates the research is related to the availability of the annotated corpora and the reference data. The purpose of our work is to describe the rationale and approach for creation and annotation of scientific corpus with information on food/drug interactions. This corpus contains 639 MEDLINE citations (titles and abstracts), corresponding to 5,752 sentences. It is manually annotated by two experts. The corpus is named POMELO. This annotated corpus will be made available for the research purposes.

null

10.26615/978-954-452-044-1_010

null

56,037

inproceedings

radev-etal-2017-annotation

Annotation of Clinical Narratives in {B}ulgarian language

Boytcheva, Svetla and Cohen, Kevin Bretonnel and Savova, Guergana and Angelova, Galia

sep

2017

Varna, Bulgaria

INCOMA Ltd.

https://aclanthology.org/W17-8011/

Radev, Ivajlo and Simov, Kiril and Angelova, Galia and Boytcheva, Svetla

Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017

81--87

In this paper we describe annotation process of clinical texts with morphosyntactic and semantic information. The corpus contains 1,300 discharge letters in Bulgarian language for patients with Endocrinology and Metabolic disorders. The annotated corpus will be used as a Gold standard for information extraction evaluation of test corpus of 6,200 discharge letters. The annotation is performed within Clark system {---} an XML Based System For Corpora Development. It provides mechanism for semi-automatic annotation first running a pipeline for Bulgarian morphosyntactic annotation and a cascaded regular grammar for semantic annotation is run, then rules for cleaning of frequent errors are applied. At the end the result is manually checked. At the end we hope also to be able to adapted the morphosyntactic tagger to the domain of clinical narratives as well.

null

10.26615/978-954-452-044-1_011

null

56,038

inproceedings

malahov-etal-2017-diachronic

A Diachronic Corpus for {R}omanian ({R}o{D}ia)

Dinu, Anca and Osenova, Petya and Vertan, Cristina

sep

2017

Varna

INCOMA Inc.

https://aclanthology.org/W17-8101/

Malahov, Ludmila and M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina and Colesnicov, Alexandru

Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope

1--9

This paper describes a Romanian Dependency Treebank, built at the Al. I. Cuza University (UAIC), and a special OCR techniques used to build it. The corpus has rich morphological and syntactic annotation. There are few annotated representative corpora in Romanian, and the existent ones are mainly focused on the contemporary Romanian standard. The corpus described below is focused on the non-standard aspects of the language, the Regional and the Old Romanian. Having the intention to participate at the PROIEL project, which aligns oldest New Testaments, we annotate the first printed Romanian New Testament (Alba Iulia, 1648). We began by applying the UAIC tools for the morphological and syntactic processing of Contemporary Romanian over the book`s first quarter (second edition). By carefully manually correcting the result of the automated annotation (having a modest accuracy) we obtained a sub-corpus for the training of tools for the Old Romanian processing. But the first edition of the New Testament is written in Cyrillic letters. The existence of books printed in the Old Cyrillic alphabet is a common problem for Romania and The Republic of Moldova, countries where the Romanian is spoken; a problem to solve by the joint efforts of the NLP researchers in the two countries.

null

0.26615/978-954-452-046-5_001

null

56,040

inproceedings

bobicev-etal-2017-tools

Tools for Building a Corpus to Study the Historical and Geographical Variation of the {R}omanian Language

Dinu, Anca and Osenova, Petya and Vertan, Cristina

sep

2017

Varna

INCOMA Inc.

https://aclanthology.org/W17-8102/

Bobicev, Victoria and M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina and Perez, Cenel Augusto

Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope

10--19

Contemporary standard language corpora are ideal for NLP. There are few morphologically and syntactically annotated corpora for Romanian, and those existing or in progress only deal with the Contemporary Romanian standard. However, the necessity to study the dynamics of natural languages gave rise to balanced corpora, containing non-standard texts. In this paper, we describe the creation of tools for processing non-standard Romanian to build a big balanced corpus. We want to preserve in annotated form as many early stages of language as possible. We have already built a corpus in Old Romanian. We also intend to include the South-Danube dialects, remote to the standard language, along with regional forms closer to the standard. We try to preserve data about endangered idioms such as Aromanian, Meglenoromanian and Istroromanian dialects, and calculate the distance between different regional variants, including the language spoken in the Republic of Moldova. This distance, as well as the mutual understanding between the speakers, is the correct criterion for the classification of idioms as different languages, or as dialects, or as regional variants close to the standard.

null

0.26615/978-954-452-046-5_002

null

56,041

inproceedings

declerck-etal-2017-multilingual

Multilingual Ontologies for the Representation and Processing of Folktales

Dinu, Anca and Osenova, Petya and Vertan, Cristina

sep

2017

Varna

INCOMA Inc.

https://aclanthology.org/W17-8103/

Declerck, Thierry and Aman, Anastasija and Banzer, Martin and Mach{\'a{\v{cek, Dominik and Sch{\"afer, Lisa and Skachkova, Natalia

Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope

20--23

We describe work done in the field of folkloristics and consisting in creating ontologies based on well-established studies proposed by {\textquotedblleft}classical{\textquotedblright} folklorists. This work is supporting the availability of a huge amount of digital and structured knowledge on folktales to digital humanists. The ontological encoding of past and current motif-indexation and classification systems for folktales was in the first step limited to English language data. This led us to focus on making those newly generated formal knowledge sources available in a few more languages, like German, Russian and Bulgarian. We stress the importance of achieving this multilingual extension of our ontologies at a larger scale, in order for example to support the automated analysis and classification of such narratives in a large variety of languages, as those are getting more and more accessible on the Web.

null

0.26615/978-954-452-046-5_003

null

56,042

inproceedings

dinu-etal-2017-annotation

On the annotation of vague expressions: a case study on {R}omanian historical texts

Dinu, Anca and Osenova, Petya and Vertan, Cristina

sep

2017

Varna

INCOMA Inc.

https://aclanthology.org/W17-8104/

Dinu, Anca and von Hahn, Walther and Vertan, Cristina

Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope

24--31

Current approaches in Digital .Humanities tend to ignore a central as-pect of any hermeneutic introspection: the intrinsic vagueness of analyzed texts. Especially when dealing with his-torical documents neglecting vague-ness has important implications on the interpretation of the results. In this pa-per we present current limitation of an-notation approaches and describe a current methodology for annotating vagueness for historical Romanian texts.

null

0.26615/978-954-452-046-5_004

null

56,043

inproceedings

stambolieva-etal-2017-language

Language Technologies in Teaching Bugarian at Primary and Secondary School Level: the {NBU} Platform of Language Teaching ({PLT})

Dinu, Anca and Osenova, Petya and Vertan, Cristina

sep

2017

Varna

INCOMA Inc.

https://aclanthology.org/W17-8105/

Stambolieva, Maria and Ivanova, Valentina and Raykova, Mariana and Hadjikoteva, Milka and Neykova, Mariya

Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope

32--38

The NBU Language Teaching Platform (PLT) was initially designed for teaching foreign languages for specific purposes; at a second stage, some of its functionalities were extended to answer the needs of teaching general foreign language. New functionalities have now been created for the purpose of providing e-support for Bulgarian language and literature teaching at primary and secondary school level. The article presents the general structure of the platform and the functionalities specifically developed to match the standards and expected results set by the Ministry of Education. The E-platform integrates: 1/ an environment for creating, organizing and maintaining electronic text archives, for extracting text corpora and aligning corpora; 2/ a linguistic database; 3/ a concordancer; 4/ a set of modules for the generation and editing of practice exercises for each text or corpus; 5/ functionalities for export from the platform and import to other educational platforms. For Moodle, modules were created for test generation, performance assessment and feedback. The PLT allows centralized presentation of abundant teaching content, control of the educational process, fast and reliable feedback on performance.

null

0.26615/978-954-452-046-5_005

null

56,044

inproceedings

finley-etal-2017-analogies

What Analogies Reveal about Word Vectors and their Compositionality

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1001/

Finley, Gregory and Farmer, Stephanie and Pakhomov, Serguei

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

1--11

Analogy completion via vector arithmetic has become a common means of demonstrating the compositionality of word embeddings. Previous work have shown that this strategy works more reliably for certain types of analogical word relationships than for others, but these studies have not offered a convincing account for why this is the case. We arrive at such an account through an experiment that targets a wide variety of analogy questions and defines a baseline condition to more accurately measure the efficacy of our system. We find that the most reliably solvable analogy categories involve either 1) the application of a morpheme with clear syntactic effects, 2) male{--}female alternations, or 3) named entities. These broader types do not pattern cleanly along a syntactic{--}semantic divide. We suggest instead that their commonality is distributional, in that the difference between the distributions of two words in any given pair encompasses a relatively small number of word types. Our study offers a needed explanation for why analogy tests succeed and fail where they do and provides nuanced insight into the relationship between word distributions and the theoretical linguistic domains of syntax and semantics.

null

10.18653/v1/S17-1001

null

56,064

inproceedings

rajana-etal-2017-learning

Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1002/

Rajana, Sneha and Callison-Burch, Chris and Apidianaki, Marianna and Shwartz, Vered

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

12--21

Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.

null

10.18653/v1/S17-1002

null

56,065

inproceedings

ponti-etal-2017-decoding

Decoding Sentiment from Distributed Representations of Sentences

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1003/

Ponti, Edoardo Maria and Vuli{\'c}, Ivan and Korhonen, Anna

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

22--32

Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no {\textquoteleft}one-size-fits-all' representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bi-directional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.

null

10.18653/v1/S17-1003

null

56,066

inproceedings

vyas-carpuat-2017-detecting

Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1004/

Vyas, Yogarshi and Carpuat, Marine

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

33--43

We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric relation between words. While previous work has focused on detecting hypernymy between word types, we ground the meaning of words in specific contexts drawn from WordNet examples, and require predictions to be sensitive to changes in contexts. WHiC lets us analyze complementary properties of two approaches of inducing vector representations of word meaning in context. We show that such contextualized word representations also improve detection of a wider range of semantic relations in context.

null

10.18653/v1/S17-1004

null

56,067

inproceedings

chen-sun-2017-domain

Domain-Specific New Words Detection in {C}hinese

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1005/

Chen, Ao and Sun, Maosong

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

44--53

With the explosive growth of Internet, more and more domain-specific environments appear, such as forums, blogs, MOOCs and etc. Domain-specific words appear in these areas and always play a critical role in the domain-specific NLP tasks. This paper aims at extracting Chinese domain-specific new words automatically. The extraction of domain-specific new words has two parts including both new words in this domain and the especially important words. In this work, we propose a joint statistical model to perform these two works simultaneously. Compared to traditional new words detection models, our model doesn`t need handcraft features which are labor intensive. Experimental results demonstrate that our joint model achieves a better performance compared with the state-of-the-art methods.

null

10.18653/v1/S17-1005

null

56,068

inproceedings

gharbieh-etal-2017-deep

Deep Learning Models For Multiword Expression Identification

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1006/

Gharbieh, Waseem and Bhavsar, Virendrakumar and Cook, Paul

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

54--64

Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.

null

10.18653/v1/S17-1006

null

56,069

inproceedings

mohammad-bravo-marquez-2017-emotion

Emotion Intensities in Tweets

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1007/

Mohammad, Saif and Bravo-Marquez, Felipe

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

65--77

This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best{--}worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language.

null

10.18653/v1/S17-1007

null

56,070

inproceedings

asghar-etal-2017-deep

Deep Active Learning for Dialogue Generation

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1008/

Asghar, Nabiha and Poupart, Pascal and Jiang, Xin and Li, Hang

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

78--83

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

null

10.18653/v1/S17-1008

null

56,071

inproceedings

cocos-etal-2017-mapping

Mapping the Paraphrase Database to {W}ord{N}et

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1009/

Cocos, Anne and Apidianaki, Marianna and Callison-Burch, Chris

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

84--90

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89{\%} accuracy. The mapping also lays important groundwork for incorporating WordNet`s relations into PPDB so as to increase its utility for semantic reasoning in applications.

null

10.18653/v1/S17-1009

null

56,072

inproceedings

feng-etal-2017-semantic

Semantic Frame Labeling with Target-based Neural Model

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1010/

Feng, Yukun and Yu, Dong and Xu, Jian and Liu, Chunhua

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

91--96

This paper explores the automatic learning of distributed representations of the target`s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model`s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.

null

10.18653/v1/S17-1010

null

56,073

inproceedings

gupta-etal-2017-distributed

Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1012/

Gupta, Abhijeet and Boleda, Gemma and Pad{\'o}, Sebastian

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

104--109

Word embeddings are supposed to provide easy access to semantic relations such as {\textquotedblleft}male of{\textquotedblright} (man{--}woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.

null

10.18653/v1/S17-1012

null

56,075

inproceedings

maredia-etal-2017-comparing

Comparing Approaches for Automatic Question Identification

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1013/

Maredia, Angel and Schechtman, Kara and Levitan, Sarah Ita and Hirschberg, Julia

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

110--114

Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia. Typically, these corpora are labeled by human annotators, either in the lab or through crowd-sourcing; however, this is cumbersome and time-consuming for large corpora. We present four different approaches to automatically tagging a corpus when general topics of the conversations are known. We develop these approaches on the Columbia X-Cultural Deception corpus and find accuracy that significantly exceeds the baseline. Finally, we conduct a cross-corpus evaluation by testing the best performing approach on the Columbia/SRI/Colorado corpus.

null

10.18653/v1/S17-1013

null

56,076

inproceedings

medic-etal-2017-free

Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for {C}roatian

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1014/

Medi{\'c}, Zoran and {\v{S}}najder, Jan and Pad{\'o}, Sebastian

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

115--120

The Practical Lexical Function (PLF) model is a model of computational distributional semantics that attempts to strike a balance between expressivity and learnability in predicting phrase meaning and shows competitive results. We investigate how well the PLF carries over to free word order languages, given that it builds on observations of predicate-argument combinations that are harder to recover in free word order languages. We evaluate variants of the PLF for Croatian, using a new lexical substitution dataset. We find that the PLF works about as well for Croatian as for English, but demonstrate that its strength lies in modeling verbs, and that the free word order affects the less robust PLF variant.

null

10.18653/v1/S17-1014

null

56,077

inproceedings

nguyen-etal-2017-mixture

A Mixture Model for Learning Multi-Sense Word Embeddings

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1015/

Nguyen, Dai Quoc and Nguyen, Dat Quoc and Modi, Ashutosh and Thater, Stefan and Pinkal, Manfred

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

121--127

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.

null

10.18653/v1/S17-1015

null

56,078

inproceedings

ostermann-etal-2017-aligning

Aligning Script Events with Narrative Texts

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1016/

Ostermann, Simon and Roth, Michael and Thater, Stefan and Pinkal, Manfred

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

128--134

Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets. We introduce the task of mapping event mentions in narrative texts to such script event types, and present a model for this task that exploits rich linguistic representations as well as information on temporal ordering. The results of our experiments demonstrate that this complex task is indeed feasible.

null

10.18653/v1/S17-1016

null

56,079

inproceedings

rogers-etal-2017-many

The (too Many) Problems of Analogical Reasoning with Word Vectors

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1017/

Rogers, Anna and Drozd, Aleksandr and Li, Bofang

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

135--148

This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such {\textquotedblleft}linguistic regularities{\textquotedblright} as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity.

null

10.18653/v1/S17-1017

null

56,080

inproceedings

shutova-etal-2017-semantic

Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1018/

Shutova, Ekaterina and Wundsam, Andreas and Yannakoudakis, Helen

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

149--154

Frame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP. However, to date these methods have relied on a predefined inventory of semantic roles. In this paper, we present a method to automatically learn argument role inventories for verbs from large corpora of text, images and videos. We evaluate the method against manually constructed role inventories in FrameNet and show that the visual model outperforms the language-only model and operates with a high precision.

null

10.18653/v1/S17-1018

null

56,081

inproceedings

shwartz-etal-2017-acquiring

Acquiring Predicate Paraphrases from News Tweets

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1019/

Shwartz, Vered and Stanovsky, Gabriel and Dagan, Ido

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

155--160

We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter. Analysis of the output of ten weeks of collection shows that the accuracy of paraphrases with different support levels is estimated between 60-86{\%}. We also demonstrate that our resource is to a large extent complementary to existing resources, providing many novel paraphrases. Our resource is publicly available, continuously expanding based on daily news.

null

10.18653/v1/S17-1019

null

56,082

inproceedings

talmor-etal-2017-evaluating

Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1020/

Talmor, Alon and Geva, Mor and Berant, Jonathan

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

161--167

Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be answered from a single web document. In this paper, we propose to evaluate semantic parsing-based question answering models by comparing them to a question answering baseline that queries the web and extracts the answer only from web snippets, without access to the target knowledge-base. We investigate this approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional language, and find that our model obtains reasonable performance ({\ensuremath{\sim}}35 F1 compared to 41 F1 of state-of-the-art). We find in our analysis that our model performs well on complex questions involving conjunctions, but struggles on questions that involve relation composition and superlatives.

null

10.18653/v1/S17-1020

null

56,083

inproceedings

chersoni-etal-2017-logical

Logical Metonymy in a Distributional Model of Sentence Comprehension

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1021/

Chersoni, Emmanuele and Lenci, Alessandro and Blache, Philippe

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

168--177

In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e.g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e.g., writing). Psycholinguistic studies have revealed extra processing costs for logical metonymy, a phenomenon generally explained with the introduction of new semantic structure. In this paper, we present a general distributional model for sentence comprehension inspired by the Memory, Unification and Control model by Hagoort (2013,2016). We show that our distributional framework can account for the extra processing costs of logical metonymy and can identify the covert event in a classification task.

null

10.18653/v1/S17-1021

null

56,084

inproceedings

hwang-etal-2017-double

Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1022/

Hwang, Jena D. and Bhatia, Archna and Han, Na-Rae and O{'}Gorman, Tim and Srikumar, Vivek and Schneider, Nathan

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

178--188

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition`s lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition`s lexical function so they can be annotated at scale{---}supporting automatic, statistical processing of domain-general language{---}and discuss how this representation would allow for a simpler inventory of labels.

null

10.18653/v1/S17-1022

null

56,085

inproceedings

kiss-etal-2017-issues

Issues of Mass and Count: Dealing with {\textquoteleft}Dual-Life' Nouns

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1023/

Kiss, Tibor and Pelletier, Francis Jeffry and Husi{\'c}, Halima and Poppek, Johanna

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

189--198

The topics of mass and count have been studied for many decades in philosophy (e.g., Quine, 1960; Pelletier, 1975), linguistics (e.g., McCawley, 1975; Allen, 1980; Krifka, 1991) and psychology (e.g., Middleton et al, 2004; Barner et al, 2009). More recently, interest from within computational linguistics has studied the issues involved (e.g., Pustejovsky, 1991; Bond, 2005; Schmidtke {\&} Kuperman, 2016), to name just a few. As is pointed out in these works, there are many difficult conceptual issues involved in the study of this contrast. In this article we study one of these issues {--} the {\textquotedblleft}Dual-Life{\textquotedblright} of being simultaneously +mass and +count {--} by means of an unusual combination of human annotation, online lexical resources, and online corpora.

null

10.18653/v1/S17-1023

null

56,086

inproceedings

gilroy-etal-2017-parsing

Parsing Graphs with Regular Graph Grammars

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1024/

Gilroy, Sorcha and Lopez, Adam and Maneth, Sebastian

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

199--208

Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages (HRL) have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. (2013) prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL are more expressive than is necessary to represent semantic graphs and we propose the use of Regular Graph Languages (RGL; Courcelle 1991), which is a subfamily of HRL, as a possible alternative. We provide a top-down parsing algorithm for RGL that runs in time linear in the size of the input graph.

null

10.18653/v1/S17-1024

null

56,087

inproceedings

jauhar-hovy-2017-embedded

Embedded Semantic Lexicon Induction with Joint Global and Local Optimization

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1025/

Jauhar, Sujay Kumar and Hovy, Eduard

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

209--219

Creating annotated frame lexicons such as PropBank and FrameNet is expensive and labor intensive. We present a method to induce an embedded frame lexicon in an minimally supervised fashion using nothing more than unlabeled predicate-argument word pairs. We hypothesize that aggregating such pair selectional preferences across training leads us to a global understanding that captures predicate-argument frame structure. Our approach revolves around a novel integration between a predictive embedding model and an Indian Buffet Process posterior regularizer. We show, through our experimental evaluation, that we outperform baselines on two tasks and can learn an embedded frame lexicon that is able to capture some interesting generalities in relation to hand-crafted semantic frames.

null

10.18653/v1/S17-1025

null

56,088

inproceedings

eichler-etal-2017-generating

Generating Pattern-Based Entailment Graphs for Relation Extraction

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1026/

Eichler, Kathrin and Xu, Feiyu and Uszkoreit, Hans and Krause, Sebastian

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

220--229

Relation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions. Deriving relation patterns automatically usually results in large numbers of candidates, which need to be filtered to derive a subset of patterns that reliably extract correct relation mentions. We address the pattern selection task by exploiting the knowledge represented by entailment graphs, which capture semantic relationships holding among the learned pattern candidates. This is motivated by the fact that a pattern may not express the target relation explicitly, but still be useful for extracting instances for which the relation holds, because its meaning entails the meaning of the target relation. We evaluate the usage of both automatically generated and gold-standard entailment graphs in a relation extraction scenario and present favorable experimental results, exhibiting the benefits of structuring and selecting patterns based on entailment graphs.

null

10.18653/v1/S17-1026

null

56,089

inproceedings

becker-etal-2017-classifying

Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1027/

Becker, Maria and Staniek, Michael and Nastase, Vivi and Palmer, Alexis and Frank, Anette

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

230--240

Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.

null

10.18653/v1/S17-1027

null

56,090

inproceedings

sarioglu-kayi-etal-2017-predictive

Predictive Linguistic Features of Schizophrenia

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1028/

Sarioglu Kayi, Efsun and Diab, Mona and Pauselli, Luca and Compton, Michael and Coppersmith, Glen

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

241--250

Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.

null

10.18653/v1/S17-1028

null

56,091

inproceedings

sachan-xing-2017-learning

Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1029/

Sachan, Mrinmaya and Xing, Eric

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

251--261

Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.

null

10.18653/v1/S17-1029

null

56,092

inproceedings

antonio-rodrigues-etal-2017-ways

Ways of Asking and Replying in Duplicate Question Detection

Ide, Nancy and Herbelot, Aur{\'e}lie and M{\`a}rquez, Llu{\'i}s

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-1030/

Ant{\'o}nio Rodrigues, Jo{\~a}o and Saedi, Chakaveh and Maraev, Vladislav and Silva, Jo{\~a}o and Branco, Ant{\'o}nio

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)

262--270

This paper presents the results of systematic experimentation on the impact in duplicate question detection of different types of questions across both a number of established approaches and a novel, superior one used to address this language processing task. This study permits to gain a novel insight on the different levels of robustness of the diverse detection methods with respect to different conditions of their application, including the ones that approximate real usage scenarios.

null

10.18653/v1/S17-1030

null

56,093

inproceedings

cer-etal-2017-semeval

{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2001/

Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, I{\~n}igo and Specia, Lucia

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

1--14

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in \textit{all language tracks}. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the \textit{STS Benchmark} is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).

null

10.18653/v1/S17-2001

null

56,095

inproceedings

camacho-collados-etal-2017-semeval

{S}em{E}val-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2002/

Camacho-Collados, Jose and Pilehvar, Mohammad Taher and Collier, Nigel and Navigli, Roberto

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

15--26

This paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: \url{http://alt.qcri.org/semeval2017/task2/}

null

10.18653/v1/S17-2002

null

56,096

inproceedings

nakov-etal-2017-semeval

{S}em{E}val-2017 Task 3: Community Question Answering

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2003/

Nakov, Preslav and Hoogeveen, Doris and M{\`a}rquez, Llu{\'i}s and Moschitti, Alessandro and Mubarak, Hamdy and Baldwin, Timothy and Verspoor, Karin

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

27--48

We describe SemEval{--}2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016: (A) Question{--}Comment Similarity, (B) Question{--}Question Similarity, (C) Question{--}External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A{--}D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A{--}C.

null

10.18653/v1/S17-2003

null

56,097

inproceedings

potash-etal-2017-semeval

{S}em{E}val-2017 Task 6: {\#}{H}ashtag{W}ars: Learning a Sense of Humor

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2004/

Potash, Peter and Romanov, Alexey and Rumshisky, Anna

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

49--57

This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the {\textquoteleft}Hashtag Wars' segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872.

null

10.18653/v1/S17-2004

null

56,098

inproceedings

miller-etal-2017-semeval

{S}em{E}val-2017 Task 7: Detection and Interpretation of {E}nglish Puns

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2005/

Miller, Tristan and Hempelmann, Christian and Gurevych, Iryna

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

58--68

A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems' methodologies, resources, and results.

null

10.18653/v1/S17-2005

null

56,099

inproceedings

derczynski-etal-2017-semeval

{S}em{E}val-2017 Task 8: {R}umour{E}val: Determining rumour veracity and support for rumours

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2006/

Derczynski, Leon and Bontcheva, Kalina and Liakata, Maria and Procter, Rob and Wong Sak Hoi, Geraldine and Zubiaga, Arkaitz

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

69--76

Media is full of false claims. Even Oxford Dictionaries named {\textquotedblleft}post-truth{\textquotedblright} as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics {--} each having their own families of claims and replies {--} and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.

null

10.18653/v1/S17-2006

null

56,100

inproceedings

wu-etal-2017-bit

{BIT} at {S}em{E}val-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2007/

Wu, Hao and Huang, Heyan and Jian, Ping and Guo, Yuhang and Su, Chao

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

77--84

This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised one. All our systems mainly depend on the (SIS), which is constructed based on the semantic hierarchical taxonomy in WordNet, to compute non-overlapping information content (IC) of sentences. Our team ranked 2nd among 31 participating teams by the primary score of Pearson correlation coefficient (PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR) dataset.

null

10.18653/v1/S17-2007

null

56,101

inproceedings

speer-lowry-duda-2017-conceptnet

{C}oncept{N}et at {S}em{E}val-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2008/

Speer, Robyn and Lowry-Duda, Joanna

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

85--89

This paper describes Luminoso`s participation in SemEval 2017 Task 2, {\textquotedblleft}Multilingual and Cross-lingual Semantic Word Similarity{\textquotedblright}, with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work that builds high-quality, multilingual word embeddings from a combination of ConceptNet and distributional semantics. Our system took first place in both subtasks. It ranked first in 4 out of 5 of the separate languages, and also ranked first in all 10 of the cross-lingual language pairs.

null

10.18653/v1/S17-2008

null

56,102

inproceedings

nandi-etal-2017-iit

{IIT}-{UHH} at {S}em{E}val-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2009/

Nandi, Titas and Biemann, Chris and Yimam, Seid Muhie and Gupta, Deepak and Kohail, Sarah and Ekbal, Asif and Bhattacharyya, Pushpak

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

90--97

In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment threads. Our primary submission won subtask C, outperforming other systems in all the primary evaluation metrics. We performed well in other English subtasks, ranking third in subtask A and eighth in subtask B. We also developed open source toolkits for all the three English subtasks by the name cQARank [\url{https://github.com/TitasNandi/cQARank}].

null

10.18653/v1/S17-2009

null

56,103

inproceedings

donahue-etal-2017-humorhawk

{H}umor{H}awk at {S}em{E}val-2017 Task 6: Mixing Meaning and Sound for Humor Recognition

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2010/

Donahue, David and Romanov, Alexey and Rumshisky, Anna

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

98--102

This paper describes the winning system for SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. Humor detection has up until now been predominantly addressed using feature-based approaches. Our system utilizes recurrent deep learning methods with dense embeddings to predict humorous tweets from the @midnight show {\#}HashtagWars. In order to include both meaning and sound in the analysis, GloVe embeddings are combined with a novel phonetic representation to serve as input to an LSTM component. The output is combined with a character-based CNN model, and an XGBoost component in an ensemble model which achieves 0.675 accuracy on the evaluation data.

null

10.18653/v1/S17-2010

null

56,104

inproceedings

doogan-etal-2017-idiom

Idiom Savant at {S}emeval-2017 Task 7: Detection and Interpretation of {E}nglish Puns

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2011/

Doogan, Samuel and Ghosh, Aniruddha and Chen, Hanyang and Veale, Tony

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

103--108

This paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, {\textquotedblleft}Detection and interpretation of English Puns{\textquotedblright}. Our system consists of two probabilistic models for each type of puns using Google n-gram and Word2Vec. Our system achieved f-score of calculating, 0.663, and 0.07 in homographic puns and 0.8439, 0.6631, and 0.0806 in heterographic puns in task 1, task 2, and task 3 respectively.

null

10.18653/v1/S17-2011

null

56,105

inproceedings

ferrero-etal-2017-compilig

{C}ompi{LIG} at {S}em{E}val-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2012/

Ferrero, J{\'e}r{\'e}my and Besacier, Laurent and Schwab, Didier and Agn{\`e}s, Fr{\'e}d{\'e}ric

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

109--114

We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based, dictionary-based, context-based, and MT-based methods. We also combine these methods in unsupervised and supervised way. Our best run ranked 1st on track 4a with a correlation of 83.02{\%} with human annotations.

null

10.18653/v1/S17-2012

null

56,106

inproceedings

al-natsheh-etal-2017-udl

{U}d{L} at {S}em{E}val-2017 Task 1: Semantic Textual Similarity Estimation of {E}nglish Sentence Pairs Using Regression Model over Pairwise Features

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2013/

Al-Natsheh, Hussein T. and Martinet, Lucie and Muhlenbach, Fabrice and Zighed, Djamel Abdelkader

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

115--119

This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three submitted runs of our model achieved a Pearson correlation score of 0.8004 compared to a hidden human annotation of 250 pairs. We used random forest ensemble learning to map an expandable set of extracted pairwise features into a semantic similarity estimated value bounded between 0 and 5. Most of these features were calculated using word embedding vectors similarity to align Part of Speech (PoS) and Name Entities (NE) tagged tokens of each sentence pair. Among other pairwise features, we experimented a classical tf-idf weighted Bag of Words (BoW) vector model but with character-based range of n-grams instead of words. This sentence vector BoW-based feature gave a relatively high importance value percentage in the feature importances analysis of the ensemble learning.

null

10.18653/v1/S17-2013

null

56,107

inproceedings

maharjan-etal-2017-dt

{DT}{\_}{T}eam at {S}em{E}val-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and {G}aussian Mixture Model Output

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2014/

Maharjan, Nabin and Banjade, Rajendra and Gautam, Dipesh and Tamang, Lasang J. and Rus, Vasile

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

120--124

We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity scores calculated using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture Model(GMM). The correlation between our system`s output and the human judgments were up to 0.8536, which is more than 10{\%} above baseline, and almost as good as the best performing system which was at 0.8547 correlation (the difference is just about 0.1{\%}). Also, our system produced leading results when evaluated with a separate STS benchmark dataset. The word alignment and sentence embeddings based features were found to be very effective.

null

10.18653/v1/S17-2014

null

56,108

inproceedings

hassan-etal-2017-fcicu

{FCICU} at {S}em{E}val-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2015/

Hassan, Basma and AbdelRahman, Samir and Bahgat, Reem and Farag, Ibrahim

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

125--129

This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integration between sense-based and sur-face-based semantic textual similarity approach is applied together with our proposed approach. For all the tracks in Task1, Run1 is a string kernel with alignments metric and Run2 is a sense-based alignment similarity method. The first run is ranked 10th, and the second is ranked 12th in the primary track, with correlation 0.619 and 0.617 respectively

null

10.18653/v1/S17-2015

null

56,109

inproceedings

shao-2017-hcti

{HCTI} at {S}em{E}val-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2016/

Shao, Yang

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

130--133

This paper describes our convolutional neural network (CNN) system for Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated semantic vector of every sentence by max pooling every dimension of their word vectors. There are mainly two trick points in our system. One is that we trained a CNN to transfer GloVe word vectors to a more proper form for STS task before pooling. Another is that we trained a fully-connected neural network (FCNN) to transfer difference of two semantic vectors to probability of every similarity score. We decided all hyper parameters empirically. In spite of the simplicity of our neural network system, we achieved a good accuracy and ranked 3rd in primary track of SemEval 2017.

null

10.18653/v1/S17-2016

null

56,110

inproceedings

nagoudi-etal-2017-lim

{LIM}-{LIG} at {S}em{E}val-2017 Task1: Enhancing the Semantic Similarity for {A}rabic Sentences with Vectors Weighting

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2017/

Nagoudi, El Moatez Billah and Ferrero, J{\'e}r{\'e}my and Schwab, Didier

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

134--138

This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearson`s correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the SemEval 2017 evaluation campaign

null

10.18653/v1/S17-2017

null

56,111

inproceedings

spiewak-etal-2017-opi

{OPI}-{JSA} at {S}em{E}val-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2018/

{\'S}piewak, Martyna and Sobecki, Piotr and Kara{\'s}, Daniel

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

139--143

Semantic Textual Similarity (STS) evaluation assesses the degree to which two parts of texts are similar, based on their semantic evaluation. In this paper, we describe three models submitted to STS SemEval 2017. Given two English parts of a text, each of proposed methods outputs the assessment of their semantic similarity. We propose an approach for computing monolingual semantic textual similarity based on an ensemble of three distinct methods. Our model consists of recursive neural network (RNN) text auto-encoders ensemble with supervised a model of vectorized sentences using reduced part of speech (PoS) weighted word embeddings as well as unsupervised a method based on word coverage (TakeLab). Additionally, we enrich our model with additional features that allow disambiguation of ensemble methods based on their efficiency. We have used Multi-Layer Perceptron as an ensemble classifier basing on estimations of trained Gradient Boosting Regressors. Results of our research proves that using such ensemble leads to a higher accuracy due to a fact that each member-algorithm tends to specialize in particular type of sentences. Simple model based on PoS weighted Word2Vec word embeddings seem to improve performance of more complex RNN based auto-encoders in the ensemble. In the monolingual English-English STS subtask our Ensemble based model achieved mean Pearson correlation of .785 compared with human annotators.

null

10.18653/v1/S17-2018

null

56,112

inproceedings

espana-bonet-barron-cedeno-2017-lump

Lump at {S}em{E}val-2017 Task 1: Towards an Interlingua Semantic Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2019/

Espa{\~n}a-Bonet, Cristina and Barr{\'o}n-Cede{\~n}o, Alberto

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

144--149

This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity. Our supervised model relies on features which are multilingual or interlingual in nature. We include lexical similarities, cross-language explicit semantic analysis, internal representations of multilingual neural networks and interlingual word embeddings. Our representations allow to use large datasets in language pairs with many instances to better classify instances in smaller language pairs avoiding the necessity of translating into a single language. Hence we can deal with all the languages in the task: Arabic, English, Spanish, and Turkish.

null

10.18653/v1/S17-2019

null

56,113

inproceedings

meng-etal-2017-qlut

{QLUT} at {S}em{E}val-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2020/

Meng, Fanqing and Lu, Wenpeng and Zhang, Yuteng and Cheng, Jinyong and Du, Yuehan and Han, Shuwang

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

150--153

This paper reports the details of our submissions in the task 1 of SemEval 2017. This task aims at assessing the semantic textual similarity of two sentences or texts. We submit three unsupervised systems based on word embeddings. The differences between these runs are the various preprocessing on evaluation data. The best performance of these systems on the evaluation of Pearson correlation is 0.6887. Unsurprisingly, results of our runs demonstrate that data preprocessing, such as tokenization, lemmatization, extraction of content words and removing stop words, is helpful and plays a significant role in improving the performance of models.

null

10.18653/v1/S17-2020

null

56,114

inproceedings

bjerva-ostling-2017-ressim

{R}es{S}im at {S}em{E}val-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2021/

Bjerva, Johannes and {\"Ostling, Robert

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

154--158

Shared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages. In our system submission, we employ multilingual word representations, in which similar words in different languages are close to one another. Using such representations is advantageous, since the increasing amount of available parallel data allows for the application of such methods to many of the languages in the world. Hence, semantic similarity can be inferred even for languages for which no annotated data exists. Our system is trained and evaluated on all language pairs included in the shared task (English, Spanish, Arabic, and Turkish). Although development results are promising, our system does not yield high performance on the shared task test sets.

null

10.18653/v1/S17-2021

null

56,115

inproceedings

liu-etal-2017-itnlp

{ITNLP}-{A}i{KF} at {S}em{E}val-2017 Task 1: Rich Features Based {SVR} for Semantic Textual Similarity Computing

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2022/

Liu, Wenjie and Sun, Chengjie and Lin, Lei and Liu, Bingquan

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

159--163

Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including Ontology based, word embedding based, Corpus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Support Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a competitive result in the contest of this track.

null

10.18653/v1/S17-2022

null

56,116

inproceedings

zhuang-chang-2017-neobility

Neobility at {S}em{E}val-2017 Task 1: An Attention-based Sentence Similarity Model

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2023/

Zhuang, WenLi and Chang, Ernie

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

164--169

This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.

null

10.18653/v1/S17-2023

null

56,117

inproceedings

duma-menzel-2017-sef

{SEF}@{UHH} at {S}em{E}val-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2024/

Duma, Mirela-Stefania and Menzel, Wolfgang

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

170--174

This paper describes our unsupervised knowledge-free approach to the SemEval-2017 Task 1 Competition. The proposed method makes use of Paragraph Vector for assessing the semantic similarity between pairs of sentences. We experimented with various dimensions of the vector and three state-of-the-art similarity metrics. Given a cross-lingual task, we trained models corresponding to its two languages and combined the models by averaging the similarity scores. The results of our submitted runs are above the median scores for five out of seven test sets by means of Pearson Correlation. Moreover, one of our system runs performed best on the Spanish-English-WMT test set ranking first out of 53 runs submitted in total by all participants.

null

10.18653/v1/S17-2024

null

56,118

inproceedings

kohail-etal-2017-sts

{STS}-{UHH} at {S}em{E}val-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2025/

Kohail, Sarah and Salama, Amr Rekaby and Biemann, Chris

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

175--179

This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams.

null

10.18653/v1/S17-2025

null

56,119

inproceedings

barrow-peskov-2017-umdeep

{UMD}eep at {S}em{E}val-2017 Task 1: End-to-End Shared Weight {LSTM} Model for Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2026/

Barrow, Joe and Peskov, Denis

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

180--184

We describe a modified shared-LSTM network for the Semantic Textual Similarity (STS) task at SemEval-2017. The network builds on previously explored Siamese network architectures. We treat max sentence length as an additional hyperparameter to be tuned (beyond learning rate, regularization, and dropout). Our results demonstrate that hand-tuning max sentence training length significantly improves final accuracy. After optimizing hyperparameters, we train the network on the multilingual semantic similarity task using pre-translated sentences. We achieved a correlation of 0.4792 for all the subtasks. We achieved the fourth highest team correlation for Task 4b, which was our best relative placement.

null

10.18653/v1/S17-2026

null

56,120

inproceedings

henderson-etal-2017-mitre

{MITRE} at {S}em{E}val-2017 Task 1: Simple Semantic Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2027/

Henderson, John and Merkhofer, Elizabeth and Strickhart, Laura and Zarrella, Guido

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

185--190

This paper describes MITRE`s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.

null

10.18653/v1/S17-2027

null

56,121

inproceedings

tian-etal-2017-ecnu

{ECNU} at {S}em{E}val-2017 Task 1: Leverage Kernel-based Traditional {NLP} features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2028/

Tian, Junfeng and Zhou, Zhiheng and Lan, Man and Wu, Yuanbin

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

191--197

To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16{\%} in primary track.

null

10.18653/v1/S17-2028

null

56,122

inproceedings

lee-etal-2017-purduenlp

{P}urdue{NLP} at {S}em{E}val-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2029/

Lee, I-Ta and Goindani, Mahak and Li, Chang and Jin, Di and Johnson, Kristen Marie and Zhang, Xiao and Pacheco, Maria Leonor and Goldwasser, Dan

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

198--202

This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.

null

10.18653/v1/S17-2029

null

56,123

inproceedings

bicici-2017-rtm

{RTM} at {S}em{E}val-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2030/

Bi{\c{c}}ici, Ergun

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

203--207

We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.

null

10.18653/v1/S17-2030

null

56,124

inproceedings

arroyo-fernandez-meza-ruiz-2017-lipn

{LIPN}-{IIMAS} at {S}em{E}val-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2031/

Arroyo-Fern{\'a}ndez, Ignacio and Meza Ruiz, Ivan Vladimir

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

208--212

In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).

null

10.18653/v1/S17-2031

null

56,125

inproceedings

fialho-etal-2017-l2f

{L}2{F}/{INESC}-{ID} at {S}em{E}val-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2032/

Fialho, Pedro and Patinho Rodrigues, Hugo and Coheur, Lu{\'i}sa and Quaresma, Paulo

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

213--219

This paper describes our approach to the SemEval-2017 {\textquotedblleft}Semantic Textual Similarity{\textquotedblright} and {\textquotedblleft}Multilingual Word Similarity{\textquotedblright} tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.

null

10.18653/v1/S17-2032

null

56,126

inproceedings

he-etal-2017-hccl

{HCCL} at {S}em{E}val-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2033/

He, Junqing and Wu, Long and Zhao, Xuemin and Yan, Yonghong

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

220--225

In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask.

null

10.18653/v1/S17-2033

null

56,127

inproceedings

gamallo-2017-citius

{C}itius at {S}em{E}val-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2034/

Gamallo, Pablo

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

226--229

This article describes the distributional strategy submitted by the Citius team to the SemEval 2017 Task 2. Even though the team participated in two subtasks, namely monolingual and cross-lingual word similarity, the article is mainly focused on the cross-lingual subtask. Our method uses comparable corpora and syntactic dependencies to extract count-based and transparent bilingual distributional contexts. The evaluation of the results show that our method is competitive with other cross-lingual strategies, even those using aligned and parallel texts.

null

10.18653/v1/S17-2034

null

56,128

inproceedings

melka-bernard-2017-jmp8

Jmp8 at {S}em{E}val-2017 Task 2: A simple and general distributional approach to estimate word similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2035/

Melka, Josu{\'e} and Bernard, Gilles

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

230--234

We have built a simple corpus-based system to estimate words similarity in multiple languages with a count-based approach. After training on Wikipedia corpora, our system was evaluated on the multilingual subtask of SemEval-2017 Task 2 and achieved a good level of performance, despite its great simplicity. Our results tend to demonstrate the power of the distributional approach in semantic similarity tasks, even without knowledge of the underlying language. We also show that dimensionality reduction has a considerable impact on the results.

null

10.18653/v1/S17-2035

null

56,129

inproceedings

meng-etal-2017-qlut-semeval

{QLUT} at {S}em{E}val-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2036/

Meng, Fanqing and Lu, Wenpeng and Zhang, Yuteng and Jian, Ping and Shi, Shumin and Huang, Heyan

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

235--238

This paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English monolingual subtask. This task is designed to evaluate the semantic word similarity of two linguistic items. The results of runs are assessed by standard Pearson and Spearman correlation, contrast with official gold standard set. The best performance of our runs is 0.781 (Final). The techniques of our runs mainly make use of the word embeddings and the knowledge-based method. The results demonstrate that the combined method is effective for the computation of word similarity, while the word embeddings and the knowledge-based technique, respectively, needs more deeply improvement in details.

null

10.18653/v1/S17-2036

null

56,130

inproceedings

jimenez-etal-2017-rufino

{RUFINO} at {S}em{E}val-2017 Task 2: Cross-lingual lexical similarity by extending {PMI} and word embeddings systems with a {S}wadesh`s-like list

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2037/

Jimenez, Sergio and Due{\~n}as, George and Gaitan, Lorena and Segura, Jorge

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

239--244

The RUFINO team proposed a non-supervised, conceptually-simple and low-cost approach for addressing the Multilingual and Cross-lingual Semantic Word Similarity challenge at SemEval 2017. The proposed systems were cross-lingual extensions of popular monolingual lexical similarity approaches such as PMI and word2vec. The extensions were possible by means of a small parallel list of concepts similar to the Swadesh`s list, which we obtained in a semi-automatic way. In spite of its simplicity, our approach showed to be effective obtaining statistically-significant and consistent results in all datasets proposed for the task. Besides, we provide some research directions for improving this novel and affordable approach.

null

10.18653/v1/S17-2037

null

56,131

inproceedings

mensa-etal-2017-merali

{MERALI} at {S}em{E}val-2017 Task 2 Subtask 1: a Cognitively Inspired approach

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2038/

Mensa, Enrico and Radicioni, Daniele P. and Lieto, Antonio

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

245--249

In this paper we report on the participation of the MERALI system to the SemEval Task 2 Subtask 1. The MERALI system approaches conceptual similarity through a simple, cognitively inspired, heuristics; it builds on a linguistic resource, the TTCS-e, that relies on BabelNet, NASARI and ConceptNet. The linguistic resource in fact contains a novel mixture of common-sense and encyclopedic knowledge. The obtained results point out that there is ample room for improvement, so that they are used to elaborate on present limitations and on future steps.

null

10.18653/v1/S17-2038

null

56,132

inproceedings

qasemizadeh-kallmeyer-2017-hhu

{HHU} at {S}em{E}val-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2039/

QasemiZadeh, Behrang and Kallmeyer, Laura

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

250--255

This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task`s organizers.

null

10.18653/v1/S17-2039

null

56,133

inproceedings

ranjbar-etal-2017-mahtab

Mahtab at {S}em{E}val-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2040/

Ranjbar, Niloofar and Mashhadirajab, Fatemeh and Shamsfard, Mehrnoush and Hosseini pour, Rayeheh and Vahid pour, Aryan

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

256--260

In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.

null

10.18653/v1/S17-2040

null

56,134

inproceedings

delli-bovi-raganato-2017-sew

Sew-Embed at {S}em{E}val-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched {W}ikipedia

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2041/

Delli Bovi, Claudio and Raganato, Alessandro

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

261--266

This paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato et al. (2016), and propose an embedded augmentation of their explicit high-dimensional vectors, which we obtain by plugging in an arbitrary word (or sense) embedding representation, and computing a weighted average in the continuous vector space. We evaluate Sew-Embed with two different off-the-shelf embedding representations, and report their performances across all monolingual and cross-lingual benchmarks available for the task. Despite its simplicity, especially compared with supervised or overly tuned approaches, Sew-Embed achieves competitive results in the cross-lingual setting (3rd best result in the global ranking of subtask 2, score 0.56).

null

10.18653/v1/S17-2041

null

56,135

inproceedings

rotari-etal-2017-wild

Wild Devs' at {S}em{E}val-2017 Task 2: Using Neural Networks to Discover Word Similarity

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2042/

Rotari, R{\u{a}}zvan-Gabriel and Hulub, Ionuț and Oprea, Ștefan and Pl{\u{a}}mad{\u{a}}-Onofrei, Mihaela and Loren{\c{t}}, Alina Beatrice and Preisler, Raluca and Iftene, Adrian and Trandab{\u{a}}ț, Diana

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

267--270

This paper presents Wild Devs' participation in the SemEval-2017 Task 2 {\textquotedblleft}Multi-lingual and Cross-lingual Semantic Word Similarity{\textquotedblright}, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.

null

10.18653/v1/S17-2042

null

56,136

inproceedings

qwaider-etal-2017-trentoteam

{T}rento{T}eam at {S}em{E}val-2017 Task 3: An application of {G}rice Maxims in Ranking Community Question Answers

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2043/

Qwaider, Mohammed R. H. and Freihat, Abed Alhakim and Giunchiglia, Fausto

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

271--274

In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy. Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis. Our system obtained a comparable resultsto Machine learning systems.

null

10.18653/v1/S17-2043

null

56,137

inproceedings

el-adlouni-etal-2017-upc

{UPC}-{USMBA} at {S}em{E}val-2017 Task 3: Combining multiple approaches for {CQA} for {A}rabic

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2044/

El Adlouni, Yassine and Lahbari, Imane and Rodr{\'i}guez, Horacio and Meknassi, Mohammed and El Alaoui, Said Ouatik and Ennahnahi, Noureddine

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

275--279

This paper presents a description of the participation of the UPC-USMBA team in the SemEval 2017 Task 3, subtask D, Arabic. Our approach for facing the task is based on a combination of a set of atomic classifiers. The atomic classifiers include lexical string based, based on vectorial representations and rulebased. Several combination approaches have been tried.

null

10.18653/v1/S17-2044

null

56,138

inproceedings

feng-etal-2017-beihang

Beihang-{MSRA} at {S}em{E}val-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2045/

Feng, Wenzheng and Wu, Yu and Wu, Wei and Li, Zhoujun and Zhou, Ming

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

280--286

This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval.

null

10.18653/v1/S17-2045

null

56,139

inproceedings

rodrigues-couto-2017-mors

{M}o{RS} at {S}em{E}val-2017 Task 3: Easy to use {SVM} in Ranking Tasks

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2046/

Rodrigues, Miguel J. and Couto, Francisco M.

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

287--291

This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced {\textquoteleft}Morse', which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.

null

10.18653/v1/S17-2046

null

56,140

inproceedings

xie-etal-2017-eica

{EICA} Team at {S}em{E}val-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel and Jurgens, David

aug

2017

Vancouver, Canada

Association for Computational Linguistics

https://aclanthology.org/S17-2047/

Xie, Yufei and Wang, Maoquan and Ma, Jing and Jiang, Jian and Lu, Zhao

Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)

292--298

We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50{\%}.

null

10.18653/v1/S17-2047

null

56,141