Titles
stringlengths 6
220
| Abstracts
stringlengths 37
3.26k
| Years
int64 1.99k
2.02k
| Categories
stringclasses 1
value |
|---|---|---|---|
Skip-Thought Vectors | We describe an approach for unsupervised learning of a generic, distributed
sentence encoder. Using the continuity of text from books, we train an
encoder-decoder model that tries to reconstruct the surrounding sentences of an
encoded passage. Sentences that share semantic and syntactic properties are
thus mapped to similar vector representations. We next introduce a simple
vocabulary expansion method to encode words that were not seen as part of
training, allowing us to expand our vocabulary to a million words. After
training our model, we extract and evaluate our vectors with linear models on 8
tasks: semantic relatedness, paraphrase detection, image-sentence ranking,
question-type classification and 4 benchmark sentiment and subjectivity
datasets. The end result is an off-the-shelf encoder that can produce highly
generic sentence representations that are robust and perform well in practice.
We will make our encoder publicly available.
| 2,015 | Computation and Language |
A Survey of Current Datasets for Vision and Language Research | Integrating vision and language has long been a dream in work on artificial
intelligence (AI). In the past two years, we have witnessed an explosion of
work that brings together vision and language from images to videos and beyond.
The available corpora have played a crucial role in advancing this area of
research. In this paper, we propose a set of quality metrics for evaluating and
analyzing the vision & language datasets and categorize them accordingly. Our
analyses show that the most recent datasets have been using more complex
language and more abstract concepts, however, there are different strengths and
weaknesses in each.
| 2,021 | Computation and Language |
deltaBLEU: A Discriminative Metric for Generation Tasks with
Intrinsically Diverse Targets | We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic
evaluation of generated text in tasks that admit a diverse range of possible
outputs. Reference strings are scored for quality by human raters on a scale of
[-1, +1] to weight multi-reference BLEU. In tasks involving generation of
conversational responses, deltaBLEU correlates reasonably with human judgments
and outperforms sentence-level and IBM BLEU in terms of both Spearman's rho and
Kendall's tau.
| 2,015 | Computation and Language |
New Approach to translation of Isolated Units in English-Korean Machine
Translation | It is the most effective way for quick translation of tremendous amount of
explosively increasing science and technique information material to develop a
practicable machine translation system and introduce it into translation
practice. This essay treats problems arising from translation of isolated units
on the basis of the practical materials and experiments obtained in the
development and introduction of English-Korean machine translation system. In
other words, this essay considers establishment of information for isolated
units and their Korean equivalents and word order.
| 2,015 | Computation and Language |
Multi-domain Dialog State Tracking using Recurrent Neural Networks | Dialog state tracking is a key component of many modern dialog systems, most
of which are designed with a single, well-defined domain in mind. This paper
shows that dialog data drawn from different dialog domains can be used to train
a general belief tracking model which can operate across all of these domains,
exhibiting superior performance to each of the domain-specific models. We
propose a training procedure which uses out-of-domain data to initialise belief
tracking models for entirely new domains. This procedure leads to improvements
in belief tracking performance regardless of the amount of in-domain data
available for training the model.
| 2,015 | Computation and Language |
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | Most tasks in natural language processing can be cast into question answering
(QA) problems over language input. We introduce the dynamic memory network
(DMN), a neural network architecture which processes input sequences and
questions, forms episodic memories, and generates relevant answers. Questions
trigger an iterative attention process which allows the model to condition its
attention on the inputs and the result of previous iterations. These results
are then reasoned over in a hierarchical recurrent sequence model to generate
answers. The DMN can be trained end-to-end and obtains state-of-the-art results
on several types of tasks and datasets: question answering (Facebook's bAbI
dataset), text classification for sentiment analysis (Stanford Sentiment
Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The
training for these different tasks relies exclusively on trained word vector
representations and input-question-answer triplets.
| 2,016 | Computation and Language |
Attention-Based Models for Speech Recognition | Recurrent sequence generators conditioned on input data through an attention
mechanism have recently shown very good performance on a range of tasks in-
cluding machine translation, handwriting synthesis and image caption gen-
eration. We extend the attention-mechanism with features needed for speech
recognition. We show that while an adaptation of the model used for machine
translation in reaches a competitive 18.7% phoneme error rate (PER) on the
TIMIT phoneme recognition task, it can only be applied to utterances which are
roughly as long as the ones it was trained on. We offer a qualitative
explanation of this failure and propose a novel and generic method of adding
location-awareness to the attention mechanism to alleviate this issue. The new
method yields a model that is robust to long inputs and achieves 18% PER in
single utterances and 20% in 10-times longer (repeated) utterances. Finally, we
propose a change to the at- tention mechanism that prevents it from
concentrating too much on single frames, which further reduces PER to 17.6%
level.
| 2,015 | Computation and Language |
Semantic Relation Classification via Convolutional Neural Networks with
Simple Negative Sampling | Syntactic features play an essential role in identifying relationship in a
sentence. Previous neural network models often suffer from irrelevant
information introduced when subjects and objects are in a long distance. In
this paper, we propose to learn more robust relation representations from the
shortest dependency path through a convolution neural network. We further
propose a straightforward negative sampling strategy to improve the assignment
of subjects and objects. Experimental results show that our method outperforms
the state-of-the-art methods on the SemEval-2010 Task 8 dataset.
| 2,015 | Computation and Language |
Automagically encoding Adverse Drug Reactions in MedDRA | Pharmacovigilance is the field of science devoted to the collection, analysis
and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the
extraction of information about ADRs from free text resources are essential to
support the work of experts, employed in the crucial task of detecting and
classifying unexpected pathologies possibly related to drug assumptions.
Narrative ADR descriptions may be collected in several way, e.g. by monitoring
social networks or through the so called spontaneous reporting, the main method
pharmacovigilance adopts in order to identify ADRs. The encoding of free-text
ADR descriptions according to MedDRA standard terminology is central for report
analysis. It is a complex work, which has to be manually implemented by the
pharmacovigilance experts. The manual encoding is expensive (in terms of time).
Moreover, a problem about the accuracy of the encoding may occur, since the
number of reports is growing up day by day. In this paper, we propose
MagiCoder, an efficient Natural Language Processing algorithm able to
automatically derive MedDRA terminologies from free-text ADR descriptions.
MagiCoder is part of VigiWork, a web application for online ADR reporting and
analysis. From a practical view-point, MagiCoder radically reduces the revision
time of ADR reports: the pharmacologist has simply to revise and validate the
automatic solution versus the hard task of choosing solutions in the 70k terms
of MedDRA. This improvement of the expert work efficiency has a meaningful
impact on the quality of data analysis. Moreover, our procedure is general
purpose. We developed MagiCoder for the Italian pharmacovigilance language, but
preliminarily analyses show that it is robust to language and dictionary
changes.
| 2,017 | Computation and Language |
Humor in Collective Discourse: Unsupervised Funniness Detection in the
New Yorker Cartoon Caption Contest | The New Yorker publishes a weekly captionless cartoon. More than 5,000
readers submit captions for it. The editors select three of them and ask the
readers to pick the funniest one. We describe an experiment that compares a
dozen automatic methods for selecting the funniest caption. We show that
negative sentiment, human-centeredness, and lexical centrality most strongly
match the funniest captions, followed by positive sentiment. These results are
useful for understanding humor and also in the design of more engaging
conversational agents in text and multimodal (vision+text) systems. As part of
this work, a large set of cartoons and captions is being made available to the
community.
| 2,015 | Computation and Language |
Twitter User Geolocation Using a Unified Text and Network Prediction
Model | We propose a label propagation approach to geolocation prediction based on
Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes
to increase location homophily and boost tractability, and (2) he incorporation
of text-based geolocation priors for test users. Experiments over three Twitter
benchmark datasets achieve state-of-the-art results, and demonstrate the
effectiveness of the enhancements.
| 2,015 | Computation and Language |
Improved Deep Speaker Feature Learning for Text-Dependent Speaker
Recognition | A deep learning approach has been proposed recently to derive speaker
identifies (d-vector) by a deep neural network (DNN). This approach has been
applied to text-dependent speaker recognition tasks and shows reasonable
performance gains when combined with the conventional i-vector approach.
Although promising, the existing d-vector implementation still can not compete
with the i-vector baseline. This paper presents two improvements for the deep
learning approach: a phonedependent DNN structure to normalize phone variation,
and a new scoring approach based on dynamic time warping (DTW). Experiments on
a text-dependent speaker recognition task demonstrated that the proposed
methods can provide considerable performance improvement over the existing
d-vector implementation.
| 2,015 | Computation and Language |
Topic2Vec: Learning Distributed Representations of Topics | Latent Dirichlet Allocation (LDA) mining thematic structure of documents
plays an important role in nature language processing and machine learning
areas. However, the probability distribution from LDA only describes the
statistical relationship of occurrences in the corpus and usually in practice,
probability is not the best choice for feature representations. Recently,
embedding methods have been proposed to represent words and documents by
learning essential concepts and representations, such as Word2Vec and Doc2Vec.
The embedded representations have shown more effectiveness than LDA-style
representations in many tasks. In this paper, we propose the Topic2Vec approach
which can learn topic representations in the same semantic vector space with
words, as an alternative to probability. The experimental results show that
Topic2Vec achieves interesting and meaningful results.
| 2,015 | Computation and Language |
WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Visual
Information Extraction | The visual layout of a webpage can provide valuable clues for certain types
of Information Extraction (IE) tasks. In traditional rule based IE frameworks,
these layout cues are mapped to rules that operate on the HTML source of the
webpages. In contrast, we have developed a framework in which the rules can be
specified directly at the layout level. This has many advantages, since the
higher level of abstraction leads to simpler extraction rules that are largely
independent of the source code of the page, and, therefore, more robust. It can
also enable specification of new types of rules that are not otherwise
possible. To the best of our knowledge, there is no general framework that
allows declarative specification of information extraction rules based on
spatial layout. Our framework is complementary to traditional text based rules
framework and allows a seamless combination of spatial layout based rules with
traditional text based rules. We describe the algebra that enables such a
system and its efficient implementation using standard relational and text
indexing features of a relational database. We demonstrate the simplicity and
efficiency of this system for a task involving the extraction of software
system requirements from software product pages.
| 2,016 | Computation and Language |
Linguistics and some aspects of its underlying dynamics | In recent years, central components of a new approach to linguistics, the
Minimalist Program (MP) have come closer to physics. Features of the Minimalist
Program, such as the unconstrained nature of recursive Merge, the operation of
the Labeling Algorithm that only operates at the interface of Narrow Syntax
with the Conceptual-Intentional and the Sensory-Motor interfaces, the
difference between pronounced and un-pronounced copies of elements in a
sentence and the build-up of the Fibonacci sequence in the syntactic derivation
of sentence structures, are directly accessible to representation in terms of
algebraic formalism. Although in our scheme linguistic structures are classical
ones, we find that an interesting and productive isomorphism can be established
between the MP structure, algebraic structures and many-body field theory
opening new avenues of inquiry on the dynamics underlying some central aspects
of linguistics.
| 2,015 | Computation and Language |
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems | This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost
1 million multi-turn dialogues, with a total of over 7 million utterances and
100 million words. This provides a unique resource for research into building
dialogue managers based on neural language models that can make use of large
amounts of unlabeled data. The dataset has both the multi-turn property of
conversations in the Dialog State Tracking Challenge datasets, and the
unstructured nature of interactions from microblog services such as Twitter. We
also describe two neural learning architectures suitable for analyzing this
dataset, and provide benchmark performance on the task of selecting the best
next response.
| 2,016 | Computation and Language |
Language Understanding for Text-based Games Using Deep Reinforcement
Learning | In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforcement learning framework to jointly learn state
representations and action policies using game rewards as feedback. This
framework enables us to map text descriptions into vector representations that
capture the semantics of the game states. We evaluate our approach on two game
worlds, comparing against baselines using bag-of-words and bag-of-bigrams for
state representations. Our algorithm outperforms the baselines on both worlds
demonstrating the importance of learning expressive representations.
| 2,015 | Computation and Language |
A complex network approach to stylometry | Statistical methods have been widely employed to study the fundamental
properties of language. In recent years, methods from complex and dynamical
systems proved useful to create several language models. Despite the large
amount of studies devoted to represent texts with physical models, only a
limited number of studies have shown how the properties of the underlying
physical systems can be employed to improve the performance of natural language
processing tasks. In this paper, I address this problem by devising complex
networks methods that are able to improve the performance of current
statistical methods. Using a fuzzy classification strategy, I show that the
topological properties extracted from texts complement the traditional textual
description. In several cases, the performance obtained with hybrid approaches
outperformed the results obtained when only traditional or networked methods
were used. Because the proposed model is generic, the framework devised here
could be straightforwardly used to study similar textual applications where the
topology plays a pivotal role in the description of the interacting agents.
| 2,015 | Computation and Language |
Prior Polarity Lexical Resources for the Italian Language | In this paper we present SABRINA (Sentiment Analysis: a Broad Resource for
Italian Natural language Applications) a manually annotated prior polarity
lexical resource for Italian natural language applications in the field of
opinion mining and sentiment induction. The resource consists in two different
sets, an Italian dictionary of more than 277.000 words tagged with their prior
polarity value, and a set of polarity modifiers, containing more than 200
words, which can be used in combination with non neutral terms of the
dictionary in order to induce the sentiment of Italian compound terms. To the
best of our knowledge this is the first prior polarity manually annotated
resource which has been developed for the Italian natural language.
| 2,015 | Computation and Language |
Dimensionality on Summarization | Summarization is one of the key features of human intelligence. It plays an
important role in understanding and representation. With rapid and continual
expansion of texts, pictures and videos in cyberspace, automatic summarization
becomes more and more desirable. Text summarization has been studied for over
half century, but it is still hard to automatically generate a satisfied
summary. Traditional methods process texts empirically and neglect the
fundamental characteristics and principles of language use and understanding.
This paper summarizes previous text summarization approaches in a
multi-dimensional classification space, introduces a multi-dimensional
methodology for research and development, unveils the basic characteristics and
principles of language use and understanding, investigates some fundamental
mechanisms of summarization, studies the dimensions and forms of
representations, and proposes a multi-dimensional evaluation mechanisms.
Investigation extends to the incorporation of pictures into summary and to the
summarization of videos, graphs and pictures, and then reaches a general
summarization framework.
| 2,015 | Computation and Language |
Simple, Fast Semantic Parsing with a Tensor Kernel | We describe a simple approach to semantic parsing based on a tensor product
kernel. We extract two feature vectors: one for the query and one for each
candidate logical form. We then train a classifier using the tensor product of
the two vectors. Using very simple features for both, our system achieves an
average F1 score of 40.1% on the WebQuestions dataset. This is comparable to
more complex systems but is simpler to implement and runs faster.
| 2,015 | Computation and Language |
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and
Their Combination | This paper covers the two approaches for sentiment analysis: i) lexicon based
method; ii) machine learning method. We describe several techniques to
implement these approaches and discuss how they can be adopted for sentiment
classification of Twitter messages. We present a comparative study of different
lexicon combinations and show that enhancing sentiment lexicons with emoticons,
abbreviations and social-media slang expressions increases the accuracy of
lexicon-based classification for Twitter. We discuss the importance of feature
generation and feature selection processes for machine learning sentiment
classification. To quantify the performance of the main sentiment analysis
methods over Twitter we run these algorithms on a benchmark Twitter dataset
from the SemEval-2013 competition, task 2-B. The results show that machine
learning method based on SVM and Naive Bayes classifiers outperforms the
lexicon method. We present a new ensemble method that uses a lexicon based
sentiment score as input feature for the machine learning approach. The
combined method proved to produce more precise classifications. We also show
that employing a cost-sensitive classifier for highly unbalanced datasets
yields an improvement of sentiment classification performance up to 7%.
| 2,016 | Computation and Language |
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and
Lexemes | We present \textit{AutoExtend}, a system to learn embeddings for synsets and
lexemes. It is flexible in that it can take any word embeddings as input and
does not need an additional training corpus. The synset/lexeme embeddings
obtained live in the same vector space as the word embeddings. A sparse tensor
formalization guarantees efficiency and parallelizability. We use WordNet as a
lexical resource, but AutoExtend can be easily applied to other resources like
Freebase. AutoExtend achieves state-of-the-art performance on word similarity
and word sense disambiguation tasks.
| 2,022 | Computation and Language |
Dependency Recurrent Neural Language Models for Sentence Completion | Recent work on language modelling has shifted focus from count-based models
to neural models. In these works, the words in each sentence are always
considered in a left-to-right order. In this paper we show how we can improve
the performance of the recurrent neural network (RNN) language model by
incorporating the syntactic dependencies of a sentence, which have the effect
of bringing relevant contexts closer to the word being predicted. We evaluate
our approach on the Microsoft Research Sentence Completion Challenge and show
that the dependency RNN proposed improves over the RNN by about 10 points in
accuracy. Furthermore, we achieve results comparable with the state-of-the-art
models on this task.
| 2,015 | Computation and Language |
Correspondence Factor Analysis of Big Data Sets: A Case Study of 30
Million Words; and Contrasting Analytics using Apache Solr and Correspondence
Analysis in R | We consider a large number of text data sets. These are cooking recipes. Term
distribution and other distributional properties of the data are investigated.
Our aim is to look at various analytical approaches which allow for mining of
information on both high and low detail scales. Metric space embedding is
fundamental to our interest in the semantic properties of this data. We
consider the projection of all data into analyses of aggregated versions of the
data. We contrast that with projection of aggregated versions of the data into
analyses of all the data. Analogously for the term set, we look at analysis of
selected terms. We also look at inherent term associations such as between
singular and plural. In addition to our use of Correspondence Analysis in R,
for latent semantic space mapping, we also use Apache Solr. Setting up the Solr
server and carrying out querying is described. A further novelty is that
querying is supported in Solr based on the principal factor plane mapping of
all the data. This uses a bounding box query, based on factor projections.
| 2,015 | Computation and Language |
Reflections on Sentiment/Opinion Analysis | In this paper, we described possible directions for deeper understanding,
helping bridge the gap between psychology / cognitive science and computational
approaches in sentiment/opinion analysis literature. We focus on the opinion
holder's underlying needs and their resultant goals, which, in a utilitarian
model of sentiment, provides the basis for explaining the reason a sentiment
valence is held. While these thoughts are still immature, scattered,
unstructured, and even imaginary, we believe that these perspectives might
suggest fruitful avenues for various kinds of future work.
| 2,015 | Computation and Language |
A Survey and Classification of Controlled Natural Languages | What is here called controlled natural language (CNL) has traditionally been
given many different names. Especially during the last four decades, a wide
variety of such languages have been designed. They are applied to improve
communication among humans, to improve translation, or to provide natural and
intuitive representations for formal notations. Despite the apparent
differences, it seems sensible to put all these languages under the same
umbrella. To bring order to the variety of languages, a general classification
scheme is presented here. A comprehensive survey of existing English-based CNLs
is given, listing and describing 100 languages from 1930 until today.
Classification of these languages reveals that they form a single scattered
cloud filling the conceptual space between natural languages such as English on
the one end and formal languages such as propositional logic on the other. The
goal of this article is to provide a common terminology and a common model for
CNL, to contribute to the understanding of their general nature, to provide a
starting point for researchers interested in the area, and to help developers
to make design decisions.
| 2,014 | Computation and Language |
Dependency-based Convolutional Neural Networks for Sentence Embedding | In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.
| 2,015 | Computation and Language |
Hindi to English Transfer Based Machine Translation System | In large societies like India there is a huge demand to convert one human
language into another. Lots of work has been done in this area. Many transfer
based MTS have developed for English to other languages, as MANTRA CDAC Pune,
MATRA CDAC Pune, SHAKTI IISc Bangalore and IIIT Hyderabad. Still there is a
little work done for Hindi to other languages. Currently we are working on it.
In this paper we focus on designing a system, that translate the document from
Hindi to English by using transfer based approach. This system takes an input
text check its structure through parsing. Reordering rules are used to generate
the text in target language. It is better than Corpus Based MTS because Corpus
Based MTS require large amount of word aligned data for translation that is not
available for many languages while Transfer Based MTS requires only knowledge
of both the languages(source language and target language) to make transfer
rules. We get correct translation for simple assertive sentences and almost
correct for complex and compound sentences.
| 2,015 | Computation and Language |
Generating Navigable Semantic Maps from Social Sciences Corpora | It is now commonplace to observe that we are facing a deluge of online
information. Researchers have of course long acknowledged the potential value
of this information since digital traces make it possible to directly observe,
describe and analyze social facts, and above all the co-evolution of ideas and
communities over time. However, most online information is expressed through
text, which means it is not directly usable by machines, since computers
require structured, organized and typed information in order to be able to
manipulate it. Our goal is thus twofold: 1. Provide new natural language
processing techniques aiming at automatically extracting relevant information
from texts, especially in the context of social sciences, and connect these
pieces of information so as to obtain relevant socio-semantic networks; 2.
Provide new ways of exploring these socio-semantic networks, thanks to tools
allowing one to dynamically navigate these networks, de-construct and
re-construct them interactively, from different points of view following the
needs expressed by domain experts.
| 2,015 | Computation and Language |
What Your Username Says About You | Usernames are ubiquitous on the Internet, and they are often suggestive of
user demographics. This work looks at the degree to which gender and language
can be inferred from a username alone by making use of unsupervised morphology
induction to decompose usernames into sub-units. Experimental results on the
two tasks demonstrate the effectiveness of the proposed morphological features
compared to a character n-gram baseline.
| 2,015 | Computation and Language |
Multi-Document Summarization via Discriminative Summary Reranking | Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models can always produce high-quality summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach.
| 2,015 | Computation and Language |
The Role of Pragmatics in Legal Norm Representation | Despite the 'apparent clarity' of a given legal provision, its application
may result in an outcome that does not exactly conform to the semantic level of
a statute. The vagueness within a legal text is induced intentionally to
accommodate all possible scenarios under which such norms should be applied,
thus making the role of pragmatics an important aspect also in the
representation of a legal norm and reasoning on top of it. The notion of
pragmatics considered in this paper does not focus on the aspects associated
with judicial decision making. The paper aims to shed light on the aspects of
pragmatics in legal linguistics, mainly focusing on the domain of patent law,
only from a knowledge representation perspective. The philosophical discussions
presented in this paper are grounded based on the legal theories from Grice and
Marmor.
| 2,015 | Computation and Language |
Learning to Mine Chinese Coordinate Terms Using the Web | Coordinate relation refers to the relation between instances of a concept and
the relation between the directly hyponyms of a concept. In this paper, we
focus on the task of extracting terms which are coordinate with a user given
seed term in Chinese, and grouping the terms which belong to different concepts
if the seed term has several meanings. We propose a semi-supervised method that
integrates manually defined linguistic patterns and automatically learned
semi-structural patterns to extract coordinate terms in Chinese from web search
results. In addition, terms are grouped into different concepts based on their
co-occurring terms and contexts. We further calculate the saliency scores of
extracted terms and rank them accordingly. Experimental results demonstrate
that our proposed method generates results with high quality and wide coverage.
| 2,015 | Computation and Language |
Talking to the crowd: What do people react to in online discussions? | This paper addresses the question of how language use affects community
reaction to comments in online discussion forums, and the relative importance
of the message vs. the messenger. A new comment ranking task is proposed based
on community annotated karma in Reddit discussions, which controls for topic
and timing of comments. Experimental work with discussion threads from six
subreddits shows that the importance of different types of language features
varies with the community of interest.
| 2,015 | Computation and Language |
FAQ-based Question Answering via Word Alignment | In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction method to extract a small set of effective lexical features. Third,
we propose a learning-to-rank algorithm to train parameters more suitable for
the ranking tasks. Experimental results, conducted on three languages (English,
Spanish and Japanese), demonstrate that the question similarity model is more
effective than baseline systems, the sparse features bring 5% improvements on
top-1 accuracy, and the learning-to-rank algorithm works significantly better
than the traditional method. We further evaluate our method on the answer
sentence selection task. Our method outperforms all the previous systems on the
standard TREC data set.
| 2,015 | Computation and Language |
A new hybrid stemming algorithm for Persian | Stemming has been an influential part in Information retrieval and search
engines. There have been tremendous endeavours in making stemmer that are both
efficient and accurate. Stemmers can have three method in stemming, Dictionary
based stemmer, statistical-based stemmers, and rule-based stemmers. This paper
aims at building a hybrid stemmer that uses both Dictionary based method and
rule-based method for stemming. This ultimately helps the efficacy and
accurateness of the stemmer.
| 2,015 | Computation and Language |
Classifier-Based Text Simplification for Improved Machine Translation | Machine Translation is one of the research fields of Computational
Linguistics. The objective of many MT Researchers is to develop an MT System
that produce good quality and high accuracy output translations and which also
covers maximum language pairs. As internet and Globalization is increasing day
by day, we need a way that improves the quality of translation. For this
reason, we have developed a Classifier based Text Simplification Model for
English-Hindi Machine Translation Systems. We have used support vector machines
and Na\"ive Bayes Classifier to develop this model. We have also evaluated the
performance of these classifiers.
| 2,015 | Computation and Language |
Supervised Hierarchical Classification for Student Answer Scoring | This paper describes a hierarchical system that predicts one label at a time
for automated student response analysis. For the task, we build a
classification binary tree that delays more easily confused labels to later
stages using hierarchical processes. In particular, the paper describes how the
hierarchical classifier has been built and how the classification task has been
broken down into binary subtasks. It finally discusses the motivations and
fundamentals of such an approach.
| 2,015 | Computation and Language |
Incremental LSTM-based Dialog State Tracker | A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance close to the state-of-the-art and experimentally analyze their
contribution: including the ASR confidence scores, abstracting scarcely
represented values, including transcriptions in the training data, and model
averaging.
| 2,015 | Computation and Language |
Neural CRF Parsing | This paper describes a parsing model that combines the exact dynamic
programming of CRF parsing with the rich nonlinear featurization of neural net
approaches. Our model is structurally a CRF that factors over anchored rule
productions, but instead of linear potential functions based on sparse
features, we use nonlinear potentials computed via a feedforward neural
network. Because potentials are still local to anchored rules, structured
inference (CKY) is unchanged from the sparse case. Computing gradients during
learning involves backpropagating an error signal formed from standard CRF
sufficient statistics (expected rule counts). Using only dense features, our
neural CRF already exceeds a strong baseline CRF model (Hall et al., 2014). In
combination with sparse features, our system achieves 91.1 F1 on section 23 of
the Penn Treebank, and more generally outperforms the best prior single parser
results on a range of languages.
| 2,015 | Computation and Language |
Recurrent Polynomial Network for Dialogue State Tracking | Dialogue state tracking (DST) is a process to estimate the distribution of
the dialogue states as a dialogue progresses. Recent studies on constrained
Markov Bayesian polynomial (CMBP) framework take the first step towards
bridging the gap between rule-based and statistical approaches for DST. In this
paper, the gap is further bridged by a novel framework -- recurrent polynomial
network (RPN). RPN's unique structure enables the framework to have all the
advantages of CMBP including efficiency, portability and interpretability.
Additionally, RPN achieves more properties of statistical approaches than CMBP.
RPN was evaluated on the data corpora of the second and the third Dialog State
Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly
outperform both traditional rule-based approaches and statistical approaches
with similar feature set. Compared with the state-of-the-art statistical DST
approaches with a lot richer features, RPN is also competitive.
| 2,015 | Computation and Language |
Feature Normalisation for Robust Speech Recognition | Speech recognition system performance degrades in noisy environments. If the
acoustic models are built using features of clean utterances, the features of a
noisy test utterance would be acoustically mismatched with the trained model.
This gives poor likelihoods and poor recognition accuracy. Model adaptation and
feature normalisation are two broad areas that address this problem. While the
former often gives better performance, the latter involves estimation of lesser
number of parameters, making the system feasible for practical implementations.
This research focuses on the efficacies of various subspace, statistical and
stereo based feature normalisation techniques. A subspace projection based
method has been investigated as a standalone and adjunct technique involving
reconstruction of noisy speech features from a precomputed set of clean speech
building-blocks. The building blocks are learned using non-negative matrix
factorisation (NMF) on log-Mel filter bank coefficients, which form a basis for
the clean speech subspace. The work provides a detailed study on how the method
can be incorporated into the extraction process of Mel-frequency cepstral
coefficients. Experimental results show that the new features are robust to
noise, and achieve better results when combined with the existing techniques.
The work also proposes a modification to the training process of SPLICE
algorithm for noise robust speech recognition. It is based on feature
correlations, and enables this stereo-based algorithm to improve the
performance in all noise conditions, especially in unseen cases. Further, the
modified framework is extended to work for non-stereo datasets where clean and
noisy training utterances, but not stereo counterparts, are required. An
MLLR-based computationally efficient run-time noise adaptation method in SPLICE
framework has been proposed.
| 2,015 | Computation and Language |
Bias and population structure in the actuation of sound change | Why do human languages change at some times, and not others? We address this
longstanding question from a computational perspective, focusing on the case of
sound change. Sound change arises from the pronunciation variability ubiquitous
in every speech community, but most such variability does not lead to change.
Hence, an adequate model must allow for stability as well as change. Existing
theories of sound change tend to emphasize factors at the level of individual
learners promoting one outcome or the other, such as channel bias (which favors
change) or inductive bias (which favors stability). Here, we consider how the
interaction of these biases can lead to both stability and change in a
population setting. We find that population structure itself can act as a
source of stability, but that both stability and change are possible only when
both types of bias are active, suggesting that it is possible to understand why
sound change occurs at some times and not others as the population-level result
of the interplay between forces promoting each outcome in individual speakers.
In addition, if it is assumed that learners learn from two or more teachers,
the transition from stability to change is marked by a phase transition,
consistent with the abrupt transitions seen in many empirical cases of sound
change. The predictions of multiple-teacher models thus match empirical cases
of sound change better than the predictions of single-teacher models,
underscoring the importance of modeling language change in a population
setting.
| 2,015 | Computation and Language |
A Dependency-Based Neural Network for Relation Classification | Previous research on relation classification has verified the effectiveness
of using dependency shortest paths or subtrees. In this paper, we further
explore how to make full use of the combination of these dependency
information. We first propose a new structure, termed augmented dependency path
(ADP), which is composed of the shortest dependency path between two entities
and the subtrees attached to the shortest path. To exploit the semantic
representation behind the ADP structure, we develop dependency-based neural
networks (DepNN): a recursive neural network designed to model the subtrees,
and a convolutional neural network to capture the most important features on
the shortest path. Experiments on the SemEval-2010 dataset show that our
proposed method achieves state-of-art results.
| 2,015 | Computation and Language |
Building End-To-End Dialogue Systems Using Generative Hierarchical
Neural Network Models | We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.
| 2,016 | Computation and Language |
Persistent Topology of Syntax | We study the persistent homology of the data set of syntactic parameters of
the world languages. We show that, while homology generators behave erratically
over the whole data set, non-trivial persistent homology appears when one
restricts to specific language families. Different families exhibit different
persistent homology. We focus on the cases of the Indo-European and the
Niger-Congo families, for which we compare persistent homology over different
cluster filtering values. We investigate the possible significance, in
historical linguistic terms, of the presence of persistent generators of the
first homology. In particular, we show that the persistent first homology
generator we find in the Indo-European family is not due (as one might guess)
to the Anglo-Norman bridge in the Indo-European phylogenetic network, but is
related to the position of Ancient Greek and the Hellenic branch within the
network.
| 2,015 | Computation and Language |
How to Generate a Good Word Embedding? | We analyze three critical components of word embedding training: the model,
the corpus, and the training parameters. We systematize existing
neural-network-based word embedding algorithms and compare them using the same
corpus. We evaluate each word embedding in three ways: analyzing its semantic
properties, using it as a feature for supervised tasks and using it to
initialize neural networks. We also provide several simple guidelines for
training word embeddings. First, we discover that corpus domain is more
important than corpus size. We recommend choosing a corpus in a suitable domain
for the desired task, after that, using a larger corpus yields better results.
Second, we find that faster models provide sufficient performance in most
cases, and more complex models can be used if the training corpus is
sufficiently large. Third, the early stopping metric for iterating should rely
on the development set of the desired task rather than the validation loss of
training embedding.
| 2,015 | Computation and Language |
Notes About a More Aware Dependency Parser | In this paper I explain the reasons that led me to research and conceive a
novel technology for dependency parsing, mixing together the strengths of
data-driven transition-based and constraint-based approaches. In particular I
highlight the problem to infer the reliability of the results of a data-driven
transition-based parser, which is extremely important for high-level processes
that expect to use correct parsing results. I then briefly introduce a number
of notes about a new parser model I'm working on, capable to proceed with the
analysis in a "more aware" way, with a more "robust" concept of robustness.
| 2,015 | Computation and Language |
Practical Selection of SVM Supervised Parameters with Different Feature
Representations for Vowel Recognition | It is known that the classification performance of Support Vector Machine
(SVM) can be conveniently affected by the different parameters of the kernel
tricks and the regularization parameter, C. Thus, in this article, we propose a
study in order to find the suitable kernel with which SVM may achieve good
generalization performance as well as the parameters to use. We need to analyze
the behavior of the SVM classifier when these parameters take very small or
very large values. The study is conducted for a multi-class vowel recognition
using the TIMIT corpus. Furthermore, for the experiments, we used different
feature representations such as MFCC and PLP. Finally, a comparative study was
done to point out the impact of the choice of the parameters, kernel trick and
feature representations on the performance of the SVM classifier
| 2,015 | Computation and Language |
An Empirical Comparison of SVM and Some Supervised Learning Algorithms
for Vowel recognition | In this article, we conduct a study on the performance of some supervised
learning algorithms for vowel recognition. This study aims to compare the
accuracy of each algorithm. Thus, we present an empirical comparison between
five supervised learning classifiers and two combined classifiers: SVM, KNN,
Naive Bayes, Quadratic Bayes Normal (QDC) and Nearst Mean. Those algorithms
were tested for vowel recognition using TIMIT Corpus and Mel-frequency cepstral
coefficients (MFCCs).
| 2,015 | Computation and Language |
Robust speech recognition using consensus function based on multi-layer
networks | The clustering ensembles mingle numerous partitions of a specified data into
a single clustering solution. Clustering ensemble has emerged as a potent
approach for ameliorating both the forcefulness and the stability of
unsupervised classification results. One of the major problems in clustering
ensembles is to find the best consensus function. Finding final partition from
different clustering results requires skillfulness and robustness of the
classification algorithm. In addition, the major problem with the consensus
function is its sensitivity to the used data sets quality. This limitation is
due to the existence of noisy, silence or redundant data. This paper proposes a
novel consensus function of cluster ensembles based on Multilayer networks
technique and a maintenance database method. This maintenance database approach
is used in order to handle any given noisy speech and, thus, to guarantee the
quality of databases. This can generates good results and efficient data
partitions. To show its effectiveness, we support our strategy with empirical
evaluation using distorted speech from Aurora speech databases.
| 2,014 | Computation and Language |
Incorporating Belief Function in SVM for Phoneme Recognition | The Support Vector Machine (SVM) method has been widely used in numerous
classification tasks. The main idea of this algorithm is based on the principle
of the margin maximization to find an hyperplane which separates the data into
two different classes.In this paper, SVM is applied to phoneme recognition
task. However, in many real-world problems, each phoneme in the data set for
recognition problems may differ in the degree of significance due to noise,
inaccuracies, or abnormal characteristics; All those problems can lead to the
inaccuracies in the prediction phase. Unfortunately, the standard formulation
of SVM does not take into account all those problems and, in particular, the
variation in the speech input. This paper presents a new formulation of SVM
(B-SVM) that attributes to each phoneme a confidence degree computed based on
its geometric position in the space. Then, this degree is used in order to
strengthen the class membership of the tested phoneme. Hence, we introduce a
reformulation of the standard SVM that incorporates the degree of belief.
Experimental performance on TIMIT database shows the effectiveness of the
proposed method B-SVM on a phoneme recognition problem.
| 2,014 | Computation and Language |
The challenges of SVM optimization using Adaboost on a phoneme
recognition problem | The use of digital technology is growing at a very fast pace which led to the
emergence of systems based on the cognitive infocommunications. The expansion
of this sector impose the use of combining methods in order to ensure the
robustness in cognitive systems.
| 2,013 | Computation and Language |
Discriminative Segmental Cascades for Feature-Rich Phone Recognition | Discriminative segmental models, such as segmental conditional random fields
(SCRFs) and segmental structured support vector machines (SSVMs), have had
success in speech recognition via both lattice rescoring and first-pass
decoding. However, such models suffer from slow decoding, hampering the use of
computationally expensive features, such as segment neural networks or other
high-order features. A typical solution is to use approximate decoding, either
by beam pruning in a single pass or by beam pruning to generate a lattice
followed by a second pass. In this work, we study discriminative segmental
models trained with a hinge loss (i.e., segmental structured SVMs). We show
that beam search is not suitable for learning rescoring models in this
approach, though it gives good approximate decoding performance when the model
is already well-trained. Instead, we consider an approach inspired by
structured prediction cascades, which use max-marginal pruning to generate
lattices. We obtain a high-accuracy phonetic recognition system with several
expensive feature types: a segment neural network, a second-order language
model, and second-order phone boundary features.
| 2,016 | Computation and Language |
The Polylingual Labeled Topic Model | In this paper, we present the Polylingual Labeled Topic Model, a model which
combines the characteristics of the existing Polylingual Topic Model and
Labeled LDA. The model accounts for multiple languages with separate topic
distributions for each language while restricting the permitted topics of a
document to a set of predefined labels. We explore the properties of the model
in a two-language setting on a dataset from the social science domain. Our
experiments show that our model outperforms LDA and Labeled LDA in terms of
their held-out perplexity and that it produces semantically coherent topics
which are well interpretable by human subjects.
| 2,017 | Computation and Language |
YARBUS : Yet Another Rule Based belief Update System | We introduce a new rule based system for belief tracking in dialog systems.
Despite the simplicity of the rules being considered, the proposed belief
tracker ranks favourably compared to the previous submissions on the second and
third Dialog State Tracking challenges. The results of this simple tracker
allows to reconsider the performances of previous submissions using more
elaborate techniques.
| 2,015 | Computation and Language |
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech
Recognition | We have recently shown that deep Long Short-Term Memory (LSTM) recurrent
neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as
acoustic models for speech recognition. More recently, we have shown that the
performance of sequence trained context dependent (CD) hidden Markov model
(HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained
phone models initialized with connectionist temporal classification (CTC). In
this paper, we present techniques that further improve performance of LSTM RNN
acoustic models for large vocabulary speech recognition. We show that frame
stacking and reduced frame rate lead to more accurate models and faster
decoding. CD phone modeling leads to further improvements. We also present
initial results for LSTM RNN models outputting words directly.
| 2,015 | Computation and Language |
Reasoning about Linguistic Regularities in Word Embeddings using Matrix
Manifolds | Recent work has explored methods for learning continuous vector space word
representations reflecting the underlying semantics of words. Simple vector
space arithmetic using cosine distances has been shown to capture certain types
of analogies, such as reasoning about plurals from singulars, past tense from
present tense, etc. In this paper, we introduce a new approach to capture
analogies in continuous word representations, based on modeling not just
individual word vectors, but rather the subspaces spanned by groups of words.
We exploit the property that the set of subspaces in n-dimensional Euclidean
space form a curved manifold space called the Grassmannian, a quotient subgroup
of the Lie group of rotations in n- dimensions. Based on this mathematical
model, we develop a modified cosine distance model based on geodesic kernels
that captures relation-specific distances across word categories. Our
experiments on analogy tasks show that our approach performs significantly
better than the previous approaches for the given task.
| 2,015 | Computation and Language |
Classifying informative and imaginative prose using complex networks | Statistical methods have been widely employed in recent years to grasp many
language properties. The application of such techniques have allowed an
improvement of several linguistic applications, which encompasses machine
translation, automatic summarization and document classification. In the
latter, many approaches have emphasized the semantical content of texts, as it
is the case of bag-of-word language models. This approach has certainly yielded
reasonable performance. However, some potential features such as the structural
organization of texts have been used only on a few studies. In this context, we
probe how features derived from textual structure analysis can be effectively
employed in a classification task. More specifically, we performed a supervised
classification aiming at discriminating informative from imaginative documents.
Using a networked model that describes the local topological/dynamical
properties of function words, we achieved an accuracy rate of up to 95%, which
is much higher than similar networked approaches. A systematic analysis of
feature relevance revealed that symmetry and accessibility measurements are
among the most prominent network measurements. Our results suggest that these
measurements could be used in related language applications, as they play a
complementary role in characterizing texts.
| 2,016 | Computation and Language |
Document Embedding with Paragraph Vectors | Paragraph Vectors has been recently proposed as an unsupervised method for
learning distributed representations for pieces of texts. In their work, the
authors showed that the method can learn an embedding of movie review texts
which can be leveraged for sentiment analysis. That proof of concept, while
encouraging, was rather narrow. Here we consider tasks other than sentiment
analysis, provide a more thorough comparison of Paragraph Vectors to other
document modelling algorithms such as Latent Dirichlet Allocation, and evaluate
performance of the method as we vary the dimensionality of the learned
representation. We benchmarked the models on two document similarity data sets,
one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method
performs significantly better than other methods, and propose a simple
improvement to enhance embedding quality. Somewhat surprisingly, we also show
that much like word embeddings, vector operations on Paragraph Vectors can
perform useful semantic results.
| 2,015 | Computation and Language |
EESEN: End-to-End Speech Recognition using Deep RNN Models and
WFST-based Decoding | The performance of automatic speech recognition (ASR) has improved
tremendously due to the application of deep neural networks (DNNs). Despite
this progress, building a new ASR system remains a challenging task, requiring
various resources, multiple training stages and significant expertise. This
paper presents our Eesen framework which drastically simplifies the existing
pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen
involves learning a single recurrent neural network (RNN) predicting
context-independent targets (phonemes or characters). To remove the need for
pre-generated frame labels, we adopt the connectionist temporal classification
(CTC) objective function to infer the alignments between speech and label
sequences. A distinctive feature of Eesen is a generalized decoding approach
based on weighted finite-state transducers (WFSTs), which enables the efficient
incorporation of lexicons and language models into CTC decoding. Experiments
show that compared with the standard hybrid DNN systems, Eesen achieves
comparable word error rates (WERs), while at the same time speeding up decoding
significantly.
| 2,015 | Computation and Language |
Tag-Weighted Topic Model For Large-scale Semi-Structured Documents | To date, there have been massive Semi-Structured Documents (SSDs) during the
evolution of the Internet. These SSDs contain both unstructured features (e.g.,
plain text) and metadata (e.g., tags). Most previous works focused on modeling
the unstructured text, and recently, some other methods have been proposed to
model the unstructured text with specific tags. To build a general model for
SSDs remains an important problem in terms of both model fitness and
efficiency. We propose a novel method to model the SSDs by a so-called
Tag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both the
tags and words information, not only to learn the document-topic and topic-word
distributions, but also to infer the tag-topic distributions for text mining
tasks. We present an efficient variational inference method with an EM
algorithm for estimating the model parameters. Meanwhile, we propose three
large-scale solutions for our model under the MapReduce distributed computing
platform for modeling large-scale SSDs. The experimental results show the
effectiveness, efficiency and the robustness by comparing our model with the
state-of-the-art methods in document modeling, tags prediction and text
classification. We also show the performance of the three distributed solutions
in terms of time and accuracy on document modeling.
| 2,015 | Computation and Language |
One model, two languages: training bilingual parsers with harmonized
treebanks | We introduce an approach to train lexicalized parsers using bilingual corpora
obtained by merging harmonized treebanks of different languages, producing
parsers that can analyze sentences in either of the learned languages, or even
sentences that mix both. We test the approach on the Universal Dependency
Treebanks, training with MaltParser and MaltOptimizer. The results show that
these bilingual parsers are more than competitive, as most combinations not
only preserve accuracy, but some even achieve significant improvements over the
corresponding monolingual parsers. Preliminary experiments also show the
approach to be promising on texts with code-switching and when more languages
are added.
| 2,016 | Computation and Language |
Unsupervised Sentence Simplification Using Deep Semantics | We present a novel approach to sentence simplification which departs from
previous work in two main ways. First, it requires neither hand written rules
nor a training corpus of aligned standard and simplified sentences. Second,
sentence splitting operates on deep semantic structure. We show (i) that the
unsupervised framework we propose is competitive with four state-of-the-art
supervised systems and (ii) that our semantic based approach allows for a
principled and effective handling of sentence splitting.
| 2,016 | Computation and Language |
Multilayer Network of Language: a Unified Framework for Structural
Analysis of Linguistic Subsystems | Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language.
| 2,015 | Computation and Language |
Separated by an Un-common Language: Towards Judgment Language Informed
Vector Space Modeling | A common evaluation practice in the vector space models (VSMs) literature is
to measure the models' ability to predict human judgments about lexical
semantic relations between word pairs. Most existing evaluation sets, however,
consist of scores collected for English word pairs only, ignoring the potential
impact of the judgment language in which word pairs are presented on the human
scores. In this paper we translate two prominent evaluation sets, wordsim353
(association) and SimLex999 (similarity), from English to Italian, German and
Russian and collect scores for each dataset from crowdworkers fluent in its
language. Our analysis reveals that human judgments are strongly impacted by
the judgment language. Moreover, we show that the predictions of monolingual
VSMs do not necessarily best correlate with human judgments made with the
language used for model training, suggesting that models and humans are
affected differently by the language they use when making semantic judgments.
Finally, we show that in a large number of setups, multilingual VSM combination
results in improved correlations with human judgments, suggesting that
multilingualism may partially compensate for the judgment language effect on
human judgments.
| 2,015 | Computation and Language |
Class Vectors: Embedding representation of Document Classes | Distributed representations of words and paragraphs as semantic embeddings in
high dimensional data are used across a number of Natural Language
Understanding tasks such as retrieval, translation, and classification. In this
work, we propose "Class Vectors" - a framework for learning a vector per class
in the same embedding space as the word and paragraph embeddings. Similarity
between these class vectors and word vectors are used as features to classify a
document to a class. In experiment on several sentiment analysis tasks such as
Yelp reviews and Amazon electronic product reviews, class vectors have shown
better or comparable results in classification while learning very meaningful
class embeddings.
| 2,015 | Computation and Language |
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text
Networks | Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune.
| 2,015 | Computation and Language |
Compositional Semantic Parsing on Semi-Structured Tables | Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available.
| 2,015 | Computation and Language |
Spin Glass Models of Syntax and Language Evolution | Using the SSWL database of syntactic parameters of world languages, and the
MIT Media Lab data on language interactions, we construct a spin glass model of
language evolution. We treat binary syntactic parameters as spin states, with
languages as vertices of a graph, and assigned interaction energies along the
edges. We study a rough model of syntax evolution, under the assumption that a
strong interaction energy tends to cause parameters to align, as in the case of
ferromagnetic materials. We also study how the spin glass model needs to be
modified to account for entailment relations between syntactic parameters. This
modification leads naturally to a generalization of Potts models with external
magnetic field, which consists of a coupling at the vertices of an Ising model
and a Potts model with q=3, that have the same edge interactions. We describe
the results of simulations of the dynamics of these models, in different
temperature and energy regimes. We discuss the linguistic interpretation of the
parameters of the physical model.
| 2,015 | Computation and Language |
Improved Transition-Based Parsing by Modeling Characters instead of
Words with LSTMs | We present extensions to a continuous-state dependency parsing method that
makes it applicable to morphologically rich languages. Starting with a
high-performance transition-based parser that uses long short-term memory
(LSTM) recurrent neural networks to learn representations of the parser state,
we replace lookup-based word representations with representations constructed
from the orthographic representations of the words, also using LSTMs. This
allows statistical sharing across word forms that are similar on the surface.
Experiments for morphologically rich languages show that the parsing model
benefits from incorporating the character-based encodings of words.
| 2,015 | Computation and Language |
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs | We study the extent to which online social networks can be connected to open
knowledge bases. The problem is referred to as learning social knowledge
graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn
latent topics that generate word and network embeddings. GenVector leverages
large-scale unlabeled data with embeddings and represents data of two
modalities---i.e., social network users and knowledge concepts---in a shared
latent topic space. Experiments on three datasets show that the proposed method
clearly outperforms state-of-the-art methods. We then deploy the method on
AMiner, a large-scale online academic search system with a network of
38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our
method significantly decreases the error rate in an online A/B test with live
users.
| 2,016 | Computation and Language |
Relation Classification via Recurrent Neural Network | Deep learning has gained much success in sentence-level relation
classification. For example, convolutional neural networks (CNN) have delivered
competitive performance without much effort on feature engineering as the
conventional pattern-based methods. Thus a lot of works have been produced
based on CNN structures. However, a key issue that has not been well addressed
by the CNN-based method is the lack of capability to learn temporal features,
especially long-distance dependency between nominal pairs. In this paper, we
propose a simple framework based on recurrent neural networks (RNN) and compare
it with CNN-based model. To show the limitation of popular used SemEval-2010
Task 8 dataset, we introduce another dataset refined from MIMLRE(Angeli et al.,
2014). Experiments on two different datasets strongly indicates that the
RNN-based model can deliver better performance on relation classification, and
it is particularly capable of learning long-distance relation patterns. This
makes it suitable for real-world applications where complicated expressions are
often involved.
| 2,015 | Computation and Language |
Topic Stability over Noisy Sources | Topic modelling techniques such as LDA have recently been applied to speech
transcripts and OCR output. These corpora may contain noisy or erroneous texts
which may undermine topic stability. Therefore, it is important to know how
well a topic modelling algorithm will perform when applied to noisy data. In
this paper we show that different types of textual noise will have diverse
effects on the stability of different topic models. From these observations, we
propose guidelines for text corpus generation, with a focus on automatic speech
transcription. We also suggest topic model selection methods for noisy corpora.
| 2,015 | Computation and Language |
Listen, Attend and Spell | We present Listen, Attend and Spell (LAS), a neural network that learns to
transcribe speech utterances to characters. Unlike traditional DNN-HMM models,
this model learns all the components of a speech recognizer jointly. Our system
has two components: a listener and a speller. The listener is a pyramidal
recurrent network encoder that accepts filter bank spectra as inputs. The
speller is an attention-based recurrent network decoder that emits characters
as outputs. The network produces character sequences without making any
independence assumptions between the characters. This is the key improvement of
LAS over previous end-to-end CTC models. On a subset of the Google voice search
task, LAS achieves a word error rate (WER) of 14.1% without a dictionary or a
language model, and 10.3% with language model rescoring over the top 32 beams.
By comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0%.
| 2,015 | Computation and Language |
Replication and Generalization of PRECISE | This report describes an initial replication study of the PRECISE system and
develops a clearer, more formal description of the approach. Based on our
evaluation, we conclude that the PRECISE results do not fully replicate.
However the formalization developed here suggests a road map to further enhance
and extend the approach pioneered by PRECISE.
After a long, productive discussion with Ana-Maria Popescu (one of the
authors of PRECISE) we got more clarity on the PRECISE approach and how the
lexicon was authored for the GEO evaluation. Based on this we built a more
direct implementation over a repaired formalism. Although our new evaluation is
not yet complete, it is clear that the system is performing much better now. We
will continue developing our ideas and implementation and generate a future
report/publication that more accurately evaluates PRECISE like approaches.
| 2,015 | Computation and Language |
On Gobbledygook and Mood of the Philippine Senate: An Exploratory Study
on the Readability and Sentiment of Selected Philippine Senators' Microposts | This paper presents the findings of a readability assessment and sentiment
analysis of selected six Philippine senators' microposts over the popular
Twitter microblog. Using the Simple Measure of Gobbledygook (SMOG), tweets of
Senators Cayetano, Defensor-Santiago, Pangilinan, Marcos, Guingona, and
Escudero were assessed. A sentiment analysis was also done to determine the
polarity of the senators' respective microposts. Results showed that on the
average, the six senators are tweeting at an eight to ten SMOG level. This
means that, at least a sixth grader will be able to understand the senators'
tweets. Moreover, their tweets are mostly neutral and their sentiments vary in
unison at some period of time. This could mean that a senator's tweet sentiment
is affected by specific Philippine-based events.
| 2,015 | Computation and Language |
Word sense disambiguation: a survey | In this paper, we made a survey on Word Sense Disambiguation (WSD). Near
about in all major languages around the world, research in WSD has been
conducted upto different extents. In this paper, we have gone through a survey
regarding the different approaches adopted in different research works, the
State of the Art in the performance in this domain, recent works in different
Indian languages and finally a survey in Bengali language. We have made a
survey on different competitions in this field and the bench mark results,
obtained from those competitions.
| 2,015 | Computation and Language |
Automatic classification of bengali sentences based on sense definitions
present in bengali wordnet | Based on the sense definition of words available in the Bengali WordNet, an
attempt is made to classify the Bengali sentences automatically into different
groups in accordance with their underlying senses. The input sentences are
collected from 50 different categories of the Bengali text corpus developed in
the TDIL project of the Govt. of India, while information about the different
senses of particular ambiguous lexical item is collected from Bengali WordNet.
In an experimental basis we have used Naive Bayes probabilistic model as a
useful classifier of sentences. We have applied the algorithm over 1747
sentences that contain a particular Bengali lexical item which, because of its
ambiguous nature, is able to trigger different senses that render sentences in
different meanings. In our experiment we have achieved around 84% accurate
result on the sense classification over the total input sentences. We have
analyzed those residual sentences that did not comply with our experiment and
did affect the results to note that in many cases, wrong syntactic structures
and less semantic information are the main hurdles in semantic classification
of sentences. The applicational relevance of this study is attested in
automatic text classification, machine learning, information extraction, and
word sense disambiguation.
| 2,015 | Computation and Language |
Using Linguistic Analysis to Translate Arabic Natural Language Queries
to SPARQL | The logic-based machine-understandable framework of the Semantic Web often
challenges naive users when they try to query ontology-based knowledge bases.
Existing research efforts have approached this problem by introducing Natural
Language (NL) interfaces to ontologies. These NL interfaces have the ability to
construct SPARQL queries based on NL user queries. However, most efforts were
restricted to queries expressed in English, and they often benefited from the
advancement of English NLP tools. However, little research has been done to
support querying the Arabic content on the Semantic Web by using NL queries.
This paper presents a domain-independent approach to translate Arabic NL
queries to SPARQL by leveraging linguistic analysis. Based on a special
consideration on Noun Phrases (NPs), our approach uses a language parser to
extract NPs and the relations from Arabic parse trees and match them to the
underlying ontology. It then utilizes knowledge in the ontology to group NPs
into triple-based representations. A SPARQL query is finally generated by
extracting targets and modifiers, and interpreting them into SPARQL. The
interpretation of advanced semantic features including negation, conjunctive
and disjunctive modifiers is also supported. The approach was evaluated by
using two datasets consisting of OWL test data and queries, and the obtained
results have confirmed its feasibility to translate Arabic NL queries to
SPARQL.
| 2,015 | Computation and Language |
Hyponymy extraction of domain ontology concept based on ccrfs and
hierarchy clustering | Concept hierarchy is the backbone of ontology, and the concept hierarchy
acquisition has been a hot topic in the field of ontology learning. this paper
proposes a hyponymy extraction method of domain ontology concept based on
cascaded conditional random field(CCRFs) and hierarchy clustering. It takes
free text as extracting object, adopts CCRFs identifying the domain concepts.
First the low layer of CCRFs is used to identify simple domain concept, then
the results are sent to the high layer, in which the nesting concepts are
recognized. Next we adopt hierarchy clustering to identify the hyponymy
relation between domain ontology concepts. The experimental results demonstrate
the proposed method is efficient.
| 2,015 | Computation and Language |
Automata networks model for alignment and least effort on vocabulary
formation | Can artificial communities of agents develop language with scaling relations
close to the Zipf law? As a preliminary answer to this question, we propose an
Automata Networks model of the formation of a vocabulary on a population of
individuals, under two in principle opposite strategies: the alignment and the
least effort principle. Within the previous account to the emergence of
linguistic conventions (specially, the Naming Game), we focus on modeling
speaker and hearer efforts as actions over their vocabularies and we study the
impact of these actions on the formation of a shared language. The numerical
simulations are essentially based on an energy function, that measures the
amount of local agreement between the vocabularies. The results suggests that
on one dimensional lattices the best strategy to the formation of shared
languages is the one that minimizes the efforts of speakers on communicative
tasks.
| 2,016 | Computation and Language |
Automata networks for memory loss effects in the formation of linguistic
conventions | This work attempts to give new theoretical insights to the absence of
intermediate stages in the evolution of language. In particular, it is
developed an automata networks approach to a crucial question: how a population
of language users can reach agreement on a linguistic convention? To describe
the appearance of sharp transitions in the self-organization of language, it is
adopted an extremely simple model of (working) memory. At each time step,
language users simply loss part of their word-memories. Through computer
simulations of low-dimensional lattices, it appear sharp transitions at
critical values that depend on the size of the vicinities of the individuals.
| 2,016 | Computation and Language |
Applying Deep Learning to Answer Selection: A Study and An Open Task | We apply a general deep learning framework to address the non-factoid
question answering task. Our approach does not rely on any linguistic tools and
can be applied to different languages or domains. Various architectures are
presented and compared. We create and release a QA corpus and setup a new QA
task in the insurance domain. Experimental results demonstrate superior
performance compared to the baseline methods and various technologies give
further improvements. For this highly challenging task, the top-1 accuracy can
reach up to 65.3% on a test set, which indicates a great potential for
practical use.
| 2,015 | Computation and Language |
Study of Phonemes Confusions in Hierarchical Automatic Phoneme
Recognition System | In this paper, we have analyzed the impact of confusions on the robustness of
phoneme recognitions system. The confusions are detected at the pronunciation
and the confusions matrices of the phoneme recognizer. The confusions show that
some similarities between phonemes at the pronunciation affect significantly
the recognition rates. This paper proposes to understand those confusions in
order to improve the performance of the phoneme recognition system by isolating
the problematic phonemes. Confusion analysis leads to build a new hierarchical
recognizer using new phoneme distribution and the information from the
confusion matrices. This new hierarchical phoneme recognition system shows
significant improvements of the recognition rates on TIMIT database.
| 2,015 | Computation and Language |
Semantically Conditioned LSTM-based Natural Language Generation for
Spoken Dialogue Systems | Natural language generation (NLG) is a critical component of spoken dialogue
and it has a significant impact both on usability and perceived quality. Most
NLG systems in common use employ rules and heuristics and tend to generate
rigid and stylised responses without the natural variation of human language.
They are also not easily scaled to systems covering multiple domains and
languages. This paper presents a statistical language generator based on a
semantically controlled Long Short-term Memory (LSTM) structure. The LSTM
generator can learn from unaligned data by jointly optimising sentence planning
and surface realisation using a simple cross entropy training criterion, and
language variation can be easily achieved by sampling from output candidates.
With fewer heuristics, an objective evaluation in two differing test domains
showed the proposed method improved performance compared to previous methods.
Human judges scored the LSTM system higher on informativeness and naturalness
and overall preferred it to the other systems.
| 2,015 | Computation and Language |
Stochastic Language Generation in Dialogue using Recurrent Neural
Networks with Convolutional Sentence Reranking | The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.
| 2,015 | Computation and Language |
Mimicry Is Presidential: Linguistic Style Matching in Presidential
Debates and Improved Polling Numbers | The current research used the contexts of U.S. presidential debates and
negotiations to examine whether matching the linguistic style of an opponent in
a two-party exchange affects the reactions of third-party observers. Building
off communication accommodation theory (CAT), interaction alignment theory
(IAT), and processing fluency, we propose that language style matching (LSM)
will improve subsequent third-party evaluations because matching an opponent's
linguistic style reflects greater perspective taking and will make one's
arguments easier to process. In contrast, research on status inferences
predicts that LSM will negatively impact third-party evaluations because LSM
implies followership. We conduct two studies to test these competing
hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between
1976 and 2012 and found that candidates who matched their opponent's linguistic
style increased their standing in the polls. Study 2 demonstrated a causal
relationship between LSM and third-party observer evaluations using negotiation
transcripts.
| 2,015 | Computation and Language |
Bidirectional LSTM-CRF Models for Sequence Tagging | In this paper, we propose a variety of Long Short-Term Memory (LSTM) based
models for sequence tagging. These models include LSTM networks, bidirectional
LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer
(LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is
the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to
NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model
can efficiently use both past and future input features thanks to a
bidirectional LSTM component. It can also use sentence level tag information
thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or
close to) accuracy on POS, chunking and NER data sets. In addition, it is
robust and has less dependence on word embedding as compared to previous
observations.
| 2,015 | Computation and Language |
An Automatic Machine Translation Evaluation Metric Based on Dependency
Parsing Model | Most of the syntax-based metrics obtain the similarity by comparing the
sub-structures extracted from the trees of hypothesis and reference. These
sub-structures are defined by human and can't express all the information in
the trees because of the limited length of sub-structures. In addition, the
overlapped parts between these sub-structures are computed repeatedly. To avoid
these problems, we propose a novel automatic evaluation metric based on
dependency parsing model, with no need to define sub-structures by human.
First, we train a dependency parsing model by the reference dependency tree.
Then we generate the hypothesis dependency tree and the corresponding
probability by the dependency parsing model. The quality of the hypothesis can
be judged by this probability. In order to obtain the lexicon similarity, we
also introduce the unigram F-score to the new metric. Experiment results show
that the new metric gets the state-of-the-art performance on system level, and
is comparable with METEOR on sentence level.
| 2,016 | Computation and Language |
Egyptian Dialect Stopword List Generation from Social Network Data | This paper proposes a methodology for generating a stopword list from online
social network (OSN) corpora in Egyptian Dialect(ED). The aim of the paper is
to investigate the effect of removingED stopwords on the Sentiment Analysis
(SA) task. The stopwords lists generated before were on Modern Standard Arabic
(MSA) which is not the common language used in OSN. We have generated a
stopword list of Egyptian dialect to be used with the OSN corpora. We compare
the efficiency of text classification when using the generated list along with
previously generated lists of MSA and combining the Egyptian dialect list with
the MSA list. The text classification was performed using Na\"ive Bayes and
Decision Tree classifiers and two feature selection approaches, unigram and
bigram. The experiments show that removing ED stopwords give better performance
than using lists of MSA stopwords only.
| 2,015 | Computation and Language |
Image Representations and New Domains in Neural Image Captioning | We examine the possibility that recent promising results in automatic caption
generation are due primarily to language models. By varying image
representation quality produced by a convolutional neural network, we find that
a state-of-the-art neural captioning algorithm is able to produce quality
captions even when provided with surprisingly poor image representations. We
replicate this result in a new, fine-grained, transfer learned captioning
domain, consisting of 66K recipe image/title pairs. We also provide some
experiments regarding the appropriateness of datasets for automatic captioning,
and find that having multiple captions per image is beneficial, but not an
absolute requirement.
| 2,015 | Computation and Language |
Finding Function in Form: Compositional Character Models for Open
Vocabulary Word Representation | We introduce a model for constructing vector representations of words by
composing characters using bidirectional LSTMs. Relative to traditional word
representation models that have independent vectors for each word type, our
model requires only a single vector per character type and a fixed set of
parameters for the compositional model. Despite the compactness of this model
and, more importantly, the arbitrary nature of the form-function relationship
in language, our "composed" word representations yield state-of-the-art results
in language modeling and part-of-speech tagging. Benefits over traditional
baselines are particularly pronounced in morphologically rich languages (e.g.,
Turkish).
| 2,016 | Computation and Language |
Learning Structural Kernels for Natural Language Processing | Structural kernels are a flexible learning paradigm that has been widely used
in Natural Language Processing. However, the problem of model selection in
kernel-based methods is usually overlooked. Previous approaches mostly rely on
setting default values for kernel hyperparameters or using grid search, which
is slow and coarse-grained. In contrast, Bayesian methods allow efficient model
selection by maximizing the evidence on the training data through
gradient-based methods. In this paper we show how to perform this in the
context of structural kernels by using Gaussian Processes. Experimental results
on tree kernels show that this procedure results in better prediction
performance compared to hyperparameter optimization via grid search. The
framework proposed in this paper can be adapted to other structures besides
trees, e.g., strings and graphs, thereby extending the utility of kernel-based
methods.
| 2,015 | Computation and Language |
Feature-based Decipherment for Large Vocabulary Machine Translation | Orthographic similarities across languages provide a strong signal for
probabilistic decipherment, especially for closely related language pairs. The
existing decipherment models, however, are not well-suited for exploiting these
orthographic similarities. We propose a log-linear model with latent variables
that incorporates orthographic similarity features. Maximum likelihood training
is computationally expensive for the proposed log-linear model. To address this
challenge, we perform approximate inference via MCMC sampling and contrastive
divergence. Our results show that the proposed log-linear model with
contrastive divergence scales to large vocabularies and outperforms the
existing generative decipherment models by exploiting the orthographic
features.
| 2,015 | Computation and Language |
Improve the Evaluation of Fluency Using Entropy for Machine Translation
Evaluation Metrics | The widely-used automatic evaluation metrics cannot adequately reflect the
fluency of the translations. The n-gram-based metrics, like BLEU, limit the
maximum length of matched fragments to n and cannot catch the matched fragments
longer than n, so they can only reflect the fluency indirectly. METEOR, which
is not limited by n-gram, uses the number of matched chunks but it does not
consider the length of each chunk. In this paper, we propose an entropy-based
method, which can sufficiently reflect the fluency of translations through the
distribution of matched words. This method can easily combine with the
widely-used automatic evaluation metrics to improve the evaluation of fluency.
Experiments show that the correlations of BLEU and METEOR are improved on
sentence level after combining with the entropy-based method on WMT 2010 and
WMT 2012.
| 2,016 | Computation and Language |
Adapting Phrase-based Machine Translation to Normalise Medical Terms in
Social Media Messages | Previous studies have shown that health reports in social media, such as
DailyStrength and Twitter, have potential for monitoring health conditions
(e.g. adverse drug reactions, infectious diseases) in particular communities.
However, in order for a machine to understand and make inferences on these
health conditions, the ability to recognise when laymen's terms refer to a
particular medical concept (i.e.\ text normalisation) is required. To achieve
this, we propose to adapt an existing phrase-based machine translation (MT)
technique and a vector representation of words to map between a social media
phrase and a medical concept. We evaluate our proposed approach using a
collection of phrases from tweets related to adverse drug reactions. Our
experimental results show that the combination of a phrase-based MT technique
and the similarity between word vector representations outperforms the
baselines that apply only either of them by up to 55%.
| 2,015 | Computation and Language |
Measuring Word Significance using Distributed Representations of Words | Distributed representations of words as real-valued vectors in a relatively
low-dimensional space aim at extracting syntactic and semantic features from
large text corpora. A recently introduced neural network, named word2vec
(Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic
information in the direction of the word vectors. In this brief report, it is
proposed to use the length of the vectors, together with the term frequency, as
measure of word significance in a corpus. Experimental evidence using a
domain-specific corpus of abstracts is presented to support this proposal. A
useful visualization technique for text corpora emerges, where words are mapped
onto a two-dimensional plane and automatically ranked by significance.
| 2,015 | Computation and Language |
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models
of Meaning | Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.
| 2,015 | Computation and Language |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.