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Language Models for Image Captioning: The Quirks and What Works | Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent sentence. The second uses the penultimate activation layer of the
CNN as input to a recurrent neural network (RNN) that then generates the
caption sequence. In this paper, we compare the merits of these different
language modeling approaches for the first time by using the same
state-of-the-art CNN as input. We examine issues in the different approaches,
including linguistic irregularities, caption repetition, and data set overlap.
By combining key aspects of the ME and RNN methods, we achieve a new record
performance over previously published results on the benchmark COCO dataset.
However, the gains we see in BLEU do not translate to human judgments.
| 2,015 | Computation and Language |
Improved Relation Extraction with Feature-Rich Compositional Embedding
Models | Compositional embedding models build a representation (or embedding) for a
linguistic structure based on its component word embeddings. We propose a
Feature-rich Compositional Embedding Model (FCM) for relation extraction that
is expressive, generalizes to new domains, and is easy-to-implement. The key
idea is to combine both (unlexicalized) hand-crafted features with learned word
embeddings. The model is able to directly tackle the difficulties met by
traditional compositional embeddings models, such as handling arbitrary types
of sentence annotations and utilizing global information for composition. We
test the proposed model on two relation extraction tasks, and demonstrate that
our model outperforms both previous compositional models and traditional
feature rich models on the ACE 2005 relation extraction task, and the SemEval
2010 relation classification task. The combination of our model and a
log-linear classifier with hand-crafted features gives state-of-the-art
results.
| 2,015 | Computation and Language |
Fast Rhetorical Structure Theory Discourse Parsing | In recent years, There has been a variety of research on discourse parsing,
particularly RST discourse parsing. Most of the recent work on RST parsing has
focused on implementing new types of features or learning algorithms in order
to improve accuracy, with relatively little focus on efficiency, robustness, or
practical use. Also, most implementations are not widely available. Here, we
describe an RST segmentation and parsing system that adapts models and feature
sets from various previous work, as described below. Its accuracy is near
state-of-the-art, and it was developed to be fast, robust, and practical. For
example, it can process short documents such as news articles or essays in less
than a second.
| 2,015 | Computation and Language |
Comparing methods for Twitter Sentiment Analysis | This work extends the set of works which deal with the popular problem of
sentiment analysis in Twitter. It investigates the most popular document
("tweet") representation methods which feed sentiment evaluation mechanisms. In
particular, we study the bag-of-words, n-grams and n-gram graphs approaches and
for each of them we evaluate the performance of a lexicon-based and 7
learning-based classification algorithms (namely SVM, Na\"ive Bayesian
Networks, Logistic Regression, Multilayer Perceptrons, Best-First Trees,
Functional Trees and C4.5) as well as their combinations, using a set of 4451
manually annotated tweets. The results demonstrate the superiority of
learning-based methods and in particular of n-gram graphs approaches for
predicting the sentiment of tweets. They also show that the combinatory
approach has impressive effects on n-grams, raising the confidence up to 83.15%
on the 5-Grams, using majority vote and a balanced dataset (equal number of
positive, negative and neutral tweets for training). In the n-gram graph cases
the improvement was small to none, reaching 94.52% on the 4-gram graphs, using
Orthodromic distance and a threshold of 0.001.
| 2,015 | Computation and Language |
Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and
Instance Messages | Text segmentation task is an essential processing task for many of Natural
Language Processing (NLP) such as text summarization, text translation,
dialogue language understanding, among others. Turns segmentation considered
the key player in dialogue understanding task for building automatic
Human-Computer systems. In this paper, we introduce a novel approach to turn
segmentation into utterances for Egyptian spontaneous dialogues and Instance
Messages (IM) using Machine Learning (ML) approach as a part of automatic
understanding Egyptian spontaneous dialogues and IM task. Due to the lack of
Egyptian dialect dialogue corpus the system evaluated by our corpus includes
3001 turns, which are collected, segmented, and annotated manually from
Egyptian call-centers. The system achieves F1 scores of 90.74% and accuracy of
95.98%.
| 2,015 | Computation and Language |
A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and
Instant Message | Building dialogues systems interaction has recently gained considerable
attention, but most of the resources and systems built so far are tailored to
English and other Indo-European languages. The need for designing systems for
other languages is increasing such as Arabic language. For this reasons, there
are more interest for Arabic dialogue acts classification task because it a key
player in Arabic language understanding to building this systems. This paper
surveys different techniques for dialogue acts classification for Arabic. We
describe the main existing techniques for utterances segmentations and
classification, annotation schemas, and test corpora for Arabic dialogues
understanding that have introduced in the literature
| 2,015 | Computation and Language |
Indonesian Social Media Sentiment Analysis With Sarcasm Detection | Sarcasm is considered one of the most difficult problem in sentiment
analysis. In our ob-servation on Indonesian social media, for cer-tain topics,
people tend to criticize something using sarcasm. Here, we proposed two
additional features to detect sarcasm after a common sentiment analysis is
conducted. The features are the negativity information and the number of
interjection words. We also employed translated SentiWordNet in the sentiment
classification. All the classifications were conducted with machine learning
algorithms. The experimental results showed that the additional features are
quite effective in the sarcasm detection.
| 2,015 | Computation and Language |
Sentiment Analysis For Modern Standard Arabic And Colloquial | The rise of social media such as blogs and social networks has fueled
interest in sentiment analysis. With the proliferation of reviews, ratings,
recommendations and other forms of online expression, online opinion has turned
into a kind of virtual currency for businesses looking to market their
products, identify new opportunities and manage their reputations, therefore
many are now looking to the field of sentiment analysis. In this paper, we
present a feature-based sentence level approach for Arabic sentiment analysis.
Our approach is using Arabic idioms/saying phrases lexicon as a key importance
for improving the detection of the sentiment polarity in Arabic sentences as
well as a number of novels and rich set of linguistically motivated features
contextual Intensifiers, contextual Shifter and negation handling), syntactic
features for conflicting phrases which enhance the sentiment classification
accuracy. Furthermore, we introduce an automatic expandable wide coverage
polarity lexicon of Arabic sentiment words. The lexicon is built with
gold-standard sentiment words as a seed which is manually collected and
annotated and it expands and detects the sentiment orientation automatically of
new sentiment words using synset aggregation technique and free online Arabic
lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and
Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product
reviews, etc.). The experimental results using our resources and techniques
with SVM classifier indicate high performance levels, with accuracies of over
95%.
| 2,015 | Computation and Language |
Feature selection using Fisher's ratio technique for automatic speech
recognition | Automatic Speech Recognition involves mainly two steps; feature extraction
and classification . Mel Frequency Cepstral Coefficient is used as one of the
prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC
coefficients is used as the feature vector in the classification step. But the
question is whether the same or improved classification accuracy can be
achieved by using a subset of 12 MFCC as feature vector. In this paper,
Fisher's ratio technique is used for selecting a subset of 12 MFCC coefficients
that contribute more in discriminating a pattern. The selected coefficients are
used in classification with Hidden Markov Model algorithm. The classification
accuracies that we get by using 12 coefficients and by using the selected
coefficients are compared.
| 2,015 | Computation and Language |
Rank diversity of languages: Generic behavior in computational
linguistics | Statistical studies of languages have focused on the rank-frequency
distribution of words. Instead, we introduce here a measure of how word ranks
change in time and call this distribution \emph{rank diversity}. We calculate
this diversity for books published in six European languages since 1800, and
find that it follows a universal lognormal distribution. Based on the mean and
standard deviation associated with the lognormal distribution, we define three
different word regimes of languages: "heads" consist of words which almost do
not change their rank in time, "bodies" are words of general use, while "tails"
are comprised by context-specific words and vary their rank considerably in
time. The heads and bodies reflect the size of language cores identified by
linguists for basic communication. We propose a Gaussian random walk model
which reproduces the rank variation of words in time and thus the diversity.
Rank diversity of words can be understood as the result of random variations in
rank, where the size of the variation depends on the rank itself. We find that
the core size is similar for all languages studied.
| 2,015 | Computation and Language |
Distant Supervision for Entity Linking | Entity linking is an indispensable operation of populating knowledge
repositories for information extraction. It studies on aligning a textual
entity mention to its corresponding disambiguated entry in a knowledge
repository. In this paper, we propose a new paradigm named distantly supervised
entity linking (DSEL), in the sense that the disambiguated entities that belong
to a huge knowledge repository (Freebase) are automatically aligned to the
corresponding descriptive webpages (Wiki pages). In this way, a large scale of
weakly labeled data can be generated without manual annotation and fed to a
classifier for linking more newly discovered entities. Compared with
traditional paradigms based on solo knowledge base, DSEL benefits more via
jointly leveraging the respective advantages of Freebase and Wikipedia.
Specifically, the proposed paradigm facilitates bridging the disambiguated
labels (Freebase) of entities and their textual descriptions (Wikipedia) for
Web-scale entities. Experiments conducted on a dataset of 140,000 items and
60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze
the feature performance and improve the F1-measure to 0.545.
| 2,015 | Computation and Language |
Arabic Inquiry-Answer Dialogue Acts Annotation Schema | We present an annotation schema as part of an effort to create a manually
annotated corpus for Arabic dialogue language understanding including spoken
dialogue and written "chat" dialogue for inquiry-answer domain. The proposed
schema handles mainly the request and response acts that occurs frequently in
inquiry-answer debate conversations expressing request services, suggests, and
offers. We applied the proposed schema on 83 Arabic inquiry-answer dialogues.
| 2,015 | Computation and Language |
A type-theoretical approach to Universal Grammar | The idea of Universal Grammar (UG) as the hypothetical linguistic structure
shared by all human languages harkens back at least to the 13th century. The
best known modern elaborations of the idea are due to Chomsky. Following a
devastating critique from theoretical, typological and field linguistics, these
elaborations, the idea of UG itself and the more general idea of language
universals stand untenable and are largely abandoned. The proposal tackles the
hypothetical contents of UG using dependent and polymorphic type theory in a
framework very different from the Chomskyan ones. We introduce a type logic for
a precise, universal and parsimonious representation of natural language
morphosyntax and compositional semantics. The logic handles grammatical
ambiguity (with polymorphic types), selectional restrictions and diverse kinds
of anaphora (with dependent types), and features a partly universal set of
morphosyntactic types (by the Curry-Howard isomorphism).
| 2,015 | Computation and Language |
Sifting Robotic from Organic Text: A Natural Language Approach for
Detecting Automation on Twitter | Twitter, a popular social media outlet, has evolved into a vast source of
linguistic data, rich with opinion, sentiment, and discussion. Due to the
increasing popularity of Twitter, its perceived potential for exerting social
influence has led to the rise of a diverse community of automatons, commonly
referred to as bots. These inorganic and semi-organic Twitter entities can
range from the benevolent (e.g., weather-update bots, help-wanted-alert bots)
to the malevolent (e.g., spamming messages, advertisements, or radical
opinions). Existing detection algorithms typically leverage meta-data (time
between tweets, number of followers, etc.) to identify robotic accounts. Here,
we present a powerful classification scheme that exclusively uses the natural
language text from organic users to provide a criterion for identifying
accounts posting automated messages. Since the classifier operates on text
alone, it is flexible and may be applied to any textual data beyond the
Twitter-sphere.
| 2,016 | Computation and Language |
CCG Parsing and Multiword Expressions | This thesis presents a study about the integration of information about
Multiword Expressions (MWEs) into parsing with Combinatory Categorial Grammar
(CCG). We build on previous work which has shown the benefit of adding
information about MWEs to syntactic parsing by implementing a similar pipeline
with CCG parsing. More specifically, we collapse MWEs to one token in training
and test data in CCGbank, a corpus which contains sentences annotated with CCG
derivations. Our collapsing algorithm however can only deal with MWEs when they
form a constituent in the data which is one of the limitations of our approach.
We study the effect of collapsing training and test data. A parsing effect
can be obtained if collapsed data help the parser in its decisions and a
training effect can be obtained if training on the collapsed data improves
results. We also collapse the gold standard and show that our model
significantly outperforms the baseline model on our gold standard, which
indicates that there is a training effect. We show that the baseline model
performs significantly better on our gold standard when the data are collapsed
before parsing than when the data are collapsed after parsing which indicates
that there is a parsing effect. We show that these results can lead to improved
performance on the non-collapsed standard benchmark although we fail to show
that it does so significantly. We conclude that despite the limited settings,
there are noticeable improvements from using MWEs in parsing. We discuss ways
in which the incorporation of MWEs into parsing can be improved and hypothesize
that this will lead to more substantial results.
We finally show that turning the MWE recognition part of the pipeline into an
experimental part is a useful thing to do as we obtain different results with
different recognizers.
| 2,015 | Computation and Language |
Learning Better Word Embedding by Asymmetric Low-Rank Projection of
Knowledge Graph | Word embedding, which refers to low-dimensional dense vector representations
of natural words, has demonstrated its power in many natural language
processing tasks. However, it may suffer from the inaccurate and incomplete
information contained in the free text corpus as training data. To tackle this
challenge, there have been quite a few works that leverage knowledge graphs as
an additional information source to improve the quality of word embedding.
Although these works have achieved certain success, they have neglected some
important facts about knowledge graphs: (i) many relationships in knowledge
graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many},
rather than simply \emph{one-to-one}; (ii) most head entities and tail entities
in knowledge graphs come from very different semantic spaces. To address these
issues, in this paper, we propose a new algorithm named ProjectNet. ProjecNet
models the relationships between head and tail entities after transforming them
with different low-rank projection matrices. The low-rank projection can allow
non \emph{one-to-one} relationships between entities, while different
projection matrices for head and tail entities allow them to originate in
different semantic spaces. The experimental results demonstrate that ProjectNet
yields more accurate word embedding than previous works, thus leads to clear
improvements in various natural language processing tasks.
| 2,015 | Computation and Language |
Boosting Named Entity Recognition with Neural Character Embeddings | Most state-of-the-art named entity recognition (NER) systems rely on
handcrafted features and on the output of other NLP tasks such as
part-of-speech (POS) tagging and text chunking. In this work we propose a
language-independent NER system that uses automatically learned features only.
Our approach is based on the CharWNN deep neural network, which uses word-level
and character-level representations (embeddings) to perform sequential
classification. We perform an extensive number of experiments using two
annotated corpora in two different languages: HAREM I corpus, which contains
texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in
Spanish. Our experimental results shade light on the contribution of neural
character embeddings for NER. Moreover, we demonstrate that the same neural
network which has been successfully applied to POS tagging can also achieve
state-of-the-art results for language-independet NER, using the same
hyperparameters, and without any handcrafted features. For the HAREM I corpus,
CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score
for the total scenario (ten NE classes), and by 7.2 points in the F1 for the
selective scenario (five NE classes).
| 2,015 | Computation and Language |
Knowlege Graph Embedding by Flexible Translation | Knowledge graph embedding refers to projecting entities and relations in
knowledge graph into continuous vector spaces. State-of-the-art methods, such
as TransE, TransH, and TransR build embeddings by treating relation as
translation from head entity to tail entity. However, previous models can not
deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or
lack of scalability and efficiency. Thus, we propose a novel method, flexible
translation, named TransF, to address the above issues. TransF regards relation
as translation between head entity vector and tail entity vector with flexible
magnitude. To evaluate the proposed model, we conduct link prediction and
triple classification on benchmark datasets. Experimental results show that our
method remarkably improve the performance compared with several
state-of-the-art baselines.
| 2,015 | Computation and Language |
A Re-ranking Model for Dependency Parser with Recursive Convolutional
Neural Network | In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets.
| 2,015 | Computation and Language |
Translation Memory Retrieval Methods | Translation Memory (TM) systems are one of the most widely used translation
technologies. An important part of TM systems is the matching algorithm that
determines what translations get retrieved from the bank of available
translations to assist the human translator. Although detailed accounts of the
matching algorithms used in commercial systems can't be found in the
literature, it is widely believed that edit distance algorithms are used. This
paper investigates and evaluates the use of several matching algorithms,
including the edit distance algorithm that is believed to be at the heart of
most modern commercial TM systems. This paper presents results showing how well
various matching algorithms correlate with human judgments of helpfulness
(collected via crowdsourcing with Amazon's Mechanical Turk). A new algorithm
based on weighted n-gram precision that can be adjusted for translator length
preferences consistently returns translations judged to be most helpful by
translators for multiple domains and language pairs.
| 2,014 | Computation and Language |
The IBM 2015 English Conversational Telephone Speech Recognition System | We describe the latest improvements to the IBM English conversational
telephone speech recognition system. Some of the techniques that were found
beneficial are: maxout networks with annealed dropout rates; networks with a
very large number of outputs trained on 2000 hours of data; joint modeling of
partially unfolded recurrent neural networks and convolutional nets by
combining the bottleneck and output layers and retraining the resulting model;
and lastly, sophisticated language model rescoring with exponential and neural
network LMs. These techniques result in an 8.0% word error rate on the
Switchboard part of the Hub5-2000 evaluation test set which is 23% relative
better than our previous best published result.
| 2,015 | Computation and Language |
Learning Dynamic Feature Selection for Fast Sequential Prediction | We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.
| 2,015 | Computation and Language |
Keyphrase Based Evaluation of Automatic Text Summarization | The development of methods to deal with the informative contents of the text
units in the matching process is a major challenge in automatic summary
evaluation systems that use fixed n-gram matching. The limitation causes
inaccurate matching between units in a peer and reference summaries. The
present study introduces a new Keyphrase based Summary Evaluator KpEval for
evaluating automatic summaries. The KpEval relies on the keyphrases since they
convey the most important concepts of a text. In the evaluation process, the
keyphrases are used in their lemma form as the matching text unit. The system
was applied to evaluate different summaries of Arabic multi-document data set
presented at TAC2011. The results showed that the new evaluation technique
correlates well with the known evaluation systems: Rouge1, Rouge2, RougeSU4,
and AutoSummENG MeMoG. KpEval has the strongest correlation with AutoSummENG
MeMoG, Pearson and spearman correlation coefficient measures are 0.8840, 0.9667
respectively.
| 2,015 | Computation and Language |
Exposing ambiguities in a relation-extraction gold standard with
crowdsourcing | Semantic relation extraction is one of the frontiers of biomedical natural
language processing research. Gold standards are key tools for advancing this
research. It is challenging to generate these standards because of the high
cost of expert time and the difficulty in establishing agreement between
annotators. We implemented and evaluated a microtask crowdsourcing approach
that can produce a gold standard for extracting drug-disease relations. The
aggregated crowd judgment agreed with expert annotations from a pre-existing
corpus on 43 of 60 sentences tested. The levels of crowd agreement varied in a
similar manner to the levels of agreement among the original expert annotators.
This work rein-forces the power of crowdsourcing in the process of assembling
gold standards for relation extraction. Further, it high-lights the importance
of exposing the levels of agreement between human annotators, expert or crowd,
in gold standard corpora as these are reproducible signals indicating
ambiguities in the data or in the annotation guidelines.
| 2,015 | Computation and Language |
Text to 3D Scene Generation with Rich Lexical Grounding | The ability to map descriptions of scenes to 3D geometric representations has
many applications in areas such as art, education, and robotics. However, prior
work on the text to 3D scene generation task has used manually specified object
categories and language that identifies them. We introduce a dataset of 3D
scenes annotated with natural language descriptions and learn from this data
how to ground textual descriptions to physical objects. Our method successfully
grounds a variety of lexical terms to concrete referents, and we show
quantitatively that our method improves 3D scene generation over previous work
using purely rule-based methods. We evaluate the fidelity and plausibility of
3D scenes generated with our grounding approach through human judgments. To
ease evaluation on this task, we also introduce an automated metric that
strongly correlates with human judgments.
| 2,015 | Computation and Language |
A Frobenius Model of Information Structure in Categorical Compositional
Distributional Semantics | The categorical compositional distributional model of Coecke, Sadrzadeh and
Clark provides a linguistically motivated procedure for computing the meaning
of a sentence as a function of the distributional meaning of the words therein.
The theoretical framework allows for reasoning about compositional aspects of
language and offers structural ways of studying the underlying relationships.
While the model so far has been applied on the level of syntactic structures, a
sentence can bring extra information conveyed in utterances via intonational
means. In the current paper we extend the framework in order to accommodate
this additional information, using Frobenius algebraic structures canonically
induced over the basis of finite-dimensional vector spaces. We detail the
theory, provide truth-theoretic and distributional semantics for meanings of
intonationally-marked utterances, and present justifications and extensive
examples.
| 2,015 | Computation and Language |
Deep Speaker Vectors for Semi Text-independent Speaker Verification | Recent research shows that deep neural networks (DNNs) can be used to extract
deep speaker vectors (d-vectors) that preserve speaker characteristics and can
be used in speaker verification. This new method has been tested on
text-dependent speaker verification tasks, and improvement was reported when
combined with the conventional i-vector method.
This paper extends the d-vector approach to semi text-independent speaker
verification tasks, i.e., the text of the speech is in a limited set of short
phrases. We explore various settings of the DNN structure used for d-vector
extraction, and present a phone-dependent training which employs the posterior
features obtained from an ASR system. The experimental results show that it is
possible to apply d-vectors on semi text-independent speaker recognition, and
the phone-dependent training improves system performance.
| 2,015 | Computation and Language |
Representing Meaning with a Combination of Logical and Distributional
Models | NLP tasks differ in the semantic information they require, and at this time
no single se- mantic representation fulfills all requirements. Logic-based
representations characterize sentence structure, but do not capture the graded
aspect of meaning. Distributional models give graded similarity ratings for
words and phrases, but do not capture sentence structure in the same detail as
logic-based approaches. So it has been argued that the two are complementary.
We adopt a hybrid approach that combines logic-based and distributional
semantics through probabilistic logic inference in Markov Logic Networks
(MLNs). In this paper, we focus on the three components of a practical system
integrating logical and distributional models: 1) Parsing and task
representation is the logic-based part where input problems are represented in
probabilistic logic. This is quite different from representing them in standard
first-order logic. 2) For knowledge base construction we form weighted
inference rules. We integrate and compare distributional information with other
sources, notably WordNet and an existing paraphrase collection. In particular,
we use our system to evaluate distributional lexical entailment approaches. We
use a variant of Robinson resolution to determine the necessary inference
rules. More sources can easily be added by mapping them to logical rules; our
system learns a resource-specific weight that corrects for scaling differences
between resources. 3) In discussing probabilistic inference, we show how to
solve the inference problems efficiently. To evaluate our approach, we use the
task of textual entailment (RTE), which can utilize the strengths of both
logic-based and distributional representations. In particular we focus on the
SICK dataset, where we achieve state-of-the-art results.
| 2,016 | Computation and Language |
Unsupervised Cross-Domain Word Representation Learning | Meaning of a word varies from one domain to another. Despite this important
domain dependence in word semantics, existing word representation learning
methods are bound to a single domain. Given a pair of
\emph{source}-\emph{target} domains, we propose an unsupervised method for
learning domain-specific word representations that accurately capture the
domain-specific aspects of word semantics. First, we select a subset of
frequent words that occur in both domains as \emph{pivots}. Next, we optimize
an objective function that enforces two constraints: (a) for both source and
target domain documents, pivots that appear in a document must accurately
predict the co-occurring non-pivots, and (b) word representations learnt for
pivots must be similar in the two domains. Moreover, we propose a method to
perform domain adaptation using the learnt word representations. Our proposed
method significantly outperforms competitive baselines including the
state-of-the-art domain-insensitive word representations, and reports best
sentiment classification accuracies for all domain-pairs in a benchmark
dataset.
| 2,015 | Computation and Language |
Unveiling the Political Agenda of the European Parliament Plenary: A
Topical Analysis | This study analyzes political interactions in the European Parliament (EP) by
considering how the political agenda of the plenary sessions has evolved over
time and the manner in which Members of the European Parliament (MEPs) have
reacted to external and internal stimuli when making Parliamentary speeches. It
does so by considering the context in which speeches are made, and the content
of those speeches. To detect latent themes in legislative speeches over time,
speech content is analyzed using a new dynamic topic modeling method, based on
two layers of matrix factorization. This method is applied to a new corpus of
all English language legislative speeches in the EP plenary from the period
1999-2014. Our findings suggest that the political agenda of the EP has evolved
significantly over time, is impacted upon by the committee structure of the
Parliament, and reacts to exogenous events such as EU Treaty referenda and the
emergence of the Euro-crisis have a significant impact on what is being
discussed in Parliament.
| 2,015 | Computation and Language |
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and
POS Tagging for Micro-blog Texts | In this paper, we give an overview for the shared task at the 4th CCF
Conference on Natural Language Processing \& Chinese Computing (NLPCC 2015):
Chinese word segmentation and part-of-speech (POS) tagging for micro-blog
texts. Different with the popular used newswire datasets, the dataset of this
shared task consists of the relatively informal micro-texts. The shared task
has two sub-tasks: (1) individual Chinese word segmentation and (2) joint
Chinese word segmentation and POS Tagging. Each subtask has three tracks to
distinguish the systems with different resources. We first introduce the
dataset and task, then we characterize the different approaches of the
participating systems, report the test results, and provide a overview analysis
of these results. An online system is available for open registration and
evaluation at http://nlp.fudan.edu.cn/nlpcc2015.
| 2,015 | Computation and Language |
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered
Word Embedding | Intelligence Quotient (IQ) Test is a set of standardized questions designed
to evaluate human intelligence. Verbal comprehension questions appear very
frequently in IQ tests, which measure human's verbal ability including the
understanding of the words with multiple senses, the synonyms and antonyms, and
the analogies among words. In this work, we explore whether such tests can be
solved automatically by artificial intelligence technologies, especially the
deep learning technologies that are recently developed and successfully applied
in a number of fields. However, we found that the task was quite challenging,
and simply applying existing technologies (e.g., word embedding) could not
achieve a good performance, mainly due to the multiple senses of words and the
complex relations among words. To tackle these challenges, we propose a novel
framework consisting of three components. First, we build a classifier to
recognize the specific type of a verbal question (e.g., analogy,
classification, synonym, or antonym). Second, we obtain distributed
representations of words and relations by leveraging a novel word embedding
method that considers the multi-sense nature of words and the relational
knowledge among words (or their senses) contained in dictionaries. Third, for
each type of questions, we propose a specific solver based on the obtained
distributed word representations and relation representations. Experimental
results have shown that the proposed framework can not only outperform existing
methods for solving verbal comprehension questions but also exceed the average
performance of the Amazon Mechanical Turk workers involved in the study. The
results indicate that with appropriate uses of the deep learning technologies
we might be a further step closer to the human intelligence.
| 2,016 | Computation and Language |
Supervised Fine Tuning for Word Embedding with Integrated Knowledge | Learning vector representation for words is an important research field which
may benefit many natural language processing tasks. Two limitations exist in
nearly all available models, which are the bias caused by the context
definition and the lack of knowledge utilization. They are difficult to tackle
because these algorithms are essentially unsupervised learning approaches.
Inspired by deep learning, the authors propose a supervised framework for
learning vector representation of words to provide additional supervised fine
tuning after unsupervised learning. The framework is knowledge rich approacher
and compatible with any numerical vectors word representation. The authors
perform both intrinsic evaluation like attributional and relational similarity
prediction and extrinsic evaluations like the sentence completion and sentiment
analysis. Experiments results on 6 embeddings and 4 tasks with 10 datasets show
that the proposed fine tuning framework may significantly improve the quality
of the vector representation of words.
| 2,015 | Computation and Language |
Transition-Based Dependency Parsing with Stack Long Short-Term Memory | We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed to or popped from the top of the stack in constant time, but, in
addition, an LSTM maintains a continuous space embedding of the stack contents.
This lets us formulate an efficient parsing model that captures three facets of
a parser's state: (i) unbounded look-ahead into the buffer of incoming words,
(ii) the complete history of actions taken by the parser, and (iii) the
complete contents of the stack of partially built tree fragments, including
their internal structures. Standard backpropagation techniques are used for
training and yield state-of-the-art parsing performance.
| 2,015 | Computation and Language |
Modeling meaning: computational interpreting and understanding of
natural language fragments | In this introductory article we present the basics of an approach to
implementing computational interpreting of natural language aiming to model the
meanings of words and phrases. Unlike other approaches, we attempt to define
the meanings of text fragments in a composable and computer interpretable way.
We discuss models and ideas for detecting different types of semantic
incomprehension and choosing the interpretation that makes most sense in a
given context. Knowledge representation is designed for handling
context-sensitive and uncertain / imprecise knowledge, and for easy
accommodation of new information. It stores quantitative information capturing
the essence of the concepts, because it is crucial for working with natural
language understanding and reasoning. Still, the representation is general
enough to allow for new knowledge to be learned, and even generated by the
system. The article concludes by discussing some reasoning-related topics:
possible approaches to generation of new abstract concepts, and describing
situations and concepts in words (e.g. for specifying interpretation
difficulties).
| 2,019 | Computation and Language |
Using Syntactic Features for Phishing Detection | This paper reports on the comparison of the subject and object of verbs in
their usage between phishing emails and legitimate emails. The purpose of this
research is to explore whether the syntactic structures and subjects and
objects of verbs can be distinguishable features for phishing detection. To
achieve the objective, we have conducted two series of experiments: the
syntactic similarity for sentences, and the subject and object of verb
comparison. The results of the experiments indicated that both features can be
used for some verbs, but more work has to be done for others.
| 2,015 | Computation and Language |
Recurrent Neural Networks with External Memory for Language
Understanding | Recurrent Neural Networks (RNNs) have become increasingly popular for the
task of language understanding. In this task, a semantic tagger is deployed to
associate a semantic label to each word in an input sequence. The success of
RNN may be attributed to its ability to memorize long-term dependence that
relates the current-time semantic label prediction to the observations many
time instances away. However, the memory capacity of simple RNNs is limited
because of the gradient vanishing and exploding problem. We propose to use an
external memory to improve memorization capability of RNNs. We conducted
experiments on the ATIS dataset, and observed that the proposed model was able
to achieve the state-of-the-art results. We compare our proposed model with
alternative models and report analysis results that may provide insights for
future research.
| 2,015 | Computation and Language |
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme
Conversion | Sequence-to-sequence translation methods based on generation with a
side-conditioned language model have recently shown promising results in
several tasks. In machine translation, models conditioned on source side words
have been used to produce target-language text, and in image captioning, models
conditioned images have been used to generate caption text. Past work with this
approach has focused on large vocabulary tasks, and measured quality in terms
of BLEU. In this paper, we explore the applicability of such models to the
qualitatively different grapheme-to-phoneme task. Here, the input and output
side vocabularies are small, plain n-gram models do well, and credit is only
given when the output is exactly correct. We find that the simple
side-conditioned generation approach is able to rival the state-of-the-art, and
we are able to significantly advance the stat-of-the-art with bi-directional
long short-term memory (LSTM) neural networks that use the same alignment
information that is used in conventional approaches.
| 2,015 | Computation and Language |
Diversity in Spectral Learning for Natural Language Parsing | We describe an approach to create a diverse set of predictions with spectral
learning of latent-variable PCFGs (L-PCFGs). Our approach works by creating
multiple spectral models where noise is added to the underlying features in the
training set before the estimation of each model. We describe three ways to
decode with multiple models. In addition, we describe a simple variant of the
spectral algorithm for L-PCFGs that is fast and leads to compact models. Our
experiments for natural language parsing, for English and German, show that we
get a significant improvement over baselines comparable to state of the art.
For English, we achieve the $F_1$ score of 90.18, and for German we achieve the
$F_1$ score of 83.38.
| 2,015 | Computation and Language |
Learning to Answer Questions From Image Using Convolutional Neural
Network | In this paper, we propose to employ the convolutional neural network (CNN)
for the image question answering (QA). Our proposed CNN provides an end-to-end
framework with convolutional architectures for learning not only the image and
question representations, but also their inter-modal interactions to produce
the answer. More specifically, our model consists of three CNNs: one image CNN
to encode the image content, one sentence CNN to compose the words of the
question, and one multimodal convolution layer to learn their joint
representation for the classification in the space of candidate answer words.
We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA
datasets, which are two benchmark datasets for the image QA, with the
performances significantly outperforming the state-of-the-art.
| 2,015 | Computation and Language |
Modeling Relation Paths for Representation Learning of Knowledge Bases | Representation learning of knowledge bases (KBs) aims to embed both entities
and relations into a low-dimensional space. Most existing methods only consider
direct relations in representation learning. We argue that multiple-step
relation paths also contain rich inference patterns between entities, and
propose a path-based representation learning model. This model considers
relation paths as translations between entities for representation learning,
and addresses two key challenges: (1) Since not all relation paths are
reliable, we design a path-constraint resource allocation algorithm to measure
the reliability of relation paths. (2) We represent relation paths via semantic
composition of relation embeddings. Experimental results on real-world datasets
show that, as compared with baselines, our model achieves significant and
consistent improvements on knowledge base completion and relation extraction
from text.
| 2,015 | Computation and Language |
Monolingually Derived Phrase Scores for Phrase Based SMT Using Neural
Networks Vector Representations | In this paper, we propose two new features for estimating phrase-based
machine translation parameters from mainly monolingual data. Our method is
based on two recently introduced neural network vector representation models
for words and sentences. It is the first time that these models have been used
in an end to end phrase-based machine translation system. Scores obtained from
our method can recover more than 80% of BLEU loss caused by removing phrase
table probabilities. We also show that our features combined with the phrase
table probabilities improve the BLEU score by absolute 0.74 points.
| 2,016 | Computation and Language |
Medical Synonym Extraction with Concept Space Models | In this paper, we present a novel approach for medical synonym extraction. We
aim to integrate the term embedding with the medical domain knowledge for
healthcare applications. One advantage of our method is that it is very
scalable. Experiments on a dataset with more than 1M term pairs show that the
proposed approach outperforms the baseline approaches by a large margin.
| 2,015 | Computation and Language |
Statistical Machine Translation Features with Multitask Tensor Networks | We present a three-pronged approach to improving Statistical Machine
Translation (SMT), building on recent success in the application of neural
networks to SMT. First, we propose new features based on neural networks to
model various non-local translation phenomena. Second, we augment the
architecture of the neural network with tensor layers that capture important
higher-order interaction among the network units. Third, we apply multitask
learning to estimate the neural network parameters jointly. Each of our
proposed methods results in significant improvements that are complementary.
The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and
Chinese-English translation over a state-of-the-art system that already
includes neural network features.
| 2,015 | Computation and Language |
Learning Speech Rate in Speech Recognition | A significant performance reduction is often observed in speech recognition
when the rate of speech (ROS) is too low or too high. Most of present
approaches to addressing the ROS variation focus on the change of speech
signals in dynamic properties caused by ROS, and accordingly modify the dynamic
model, e.g., the transition probabilities of the hidden Markov model (HMM).
However, an abnormal ROS changes not only the dynamic but also the static
property of speech signals, and thus can not be compensated for purely by
modifying the dynamic model. This paper proposes an ROS learning approach based
on deep neural networks (DNN), which involves an ROS feature as the input of
the DNN model and so the spectrum distortion caused by ROS can be learned and
compensated for. The experimental results show that this approach can deliver
better performance for too slow and too fast utterances, demonstrating our
conjecture that ROS impacts both the dynamic and the static property of speech.
In addition, the proposed approach can be combined with the conventional HMM
transition adaptation method, offering additional performance gains.
| 2,015 | Computation and Language |
The Influence of Context on Dialogue Act Recognition | This article presents an analysis of the influence of context information on
dialog act recognition. We performed experiments on the widely explored
Switchboard corpus, as well as on data annotated according to the recent ISO
24617-2 standard. The latter was obtained from the Tilburg DialogBank and
through the mapping of the annotations of a subset of the Let's Go corpus. We
used a classification approach based on SVMs, which had proved successful in
previous work and allowed us to limit the amount of context information
provided. This way, we were able to observe the influence patterns as the
amount of context information increased. Our base features consisted of
n-grams, punctuation, and wh-words. Context information was obtained from one
to five preceding segments and provided either as n-grams or dialog act
classifications, with the latter typically leading to better results and more
stable influence patterns. In addition to the conclusions about the importance
and influence of context information, our experiments on the Switchboard corpus
also led to results that advanced the state-of-the-art on the dialog act
recognition task on that corpus. Furthermore, the results obtained on data
annotated according to the ISO 24617-2 standard define a baseline for future
work and contribute for the standardization of experiments in the area.
| 2,017 | Computation and Language |
A Hierarchical Neural Autoencoder for Paragraphs and Documents | Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragraphs. We introduce an LSTM model that hierarchically
builds an embedding for a paragraph from embeddings for sentences and words,
then decodes this embedding to reconstruct the original paragraph. We evaluate
the reconstructed paragraph using standard metrics like ROUGE and Entity Grid,
showing that neural models are able to encode texts in a way that preserve
syntactic, semantic, and discourse coherence. While only a first step toward
generating coherent text units from neural models, our work has the potential
to significantly impact natural language generation and
summarization\footnote{Code for the three models described in this paper can be
found at www.stanford.edu/~jiweil/ .
| 2,015 | Computation and Language |
Visualizing and Understanding Neural Models in NLP | While neural networks have been successfully applied to many NLP tasks the
resulting vector-based models are very difficult to interpret. For example it's
not clear how they achieve {\em compositionality}, building sentence meaning
from the meanings of words and phrases. In this paper we describe four
strategies for visualizing compositionality in neural models for NLP, inspired
by similar work in computer vision. We first plot unit values to visualize
compositionality of negation, intensification, and concessive clauses, allow us
to see well-known markedness asymmetries in negation. We then introduce three
simple and straightforward methods for visualizing a unit's {\em salience}, the
amount it contributes to the final composed meaning: (1) gradient
back-propagation, (2) the variance of a token from the average word node, (3)
LSTM-style gates that measure information flow. We test our methods on
sentiment using simple recurrent nets and LSTMs. Our general-purpose methods
may have wide applications for understanding compositionality and other
semantic properties of deep networks , and also shed light on why LSTMs
outperform simple recurrent nets,
| 2,016 | Computation and Language |
Do Multi-Sense Embeddings Improve Natural Language Understanding? | Learning a distinct representation for each sense of an ambiguous word could
lead to more powerful and fine-grained models of vector-space representations.
Yet while `multi-sense' methods have been proposed and tested on artificial
word-similarity tasks, we don't know if they improve real natural language
understanding tasks. In this paper we introduce a multi-sense embedding model
based on Chinese Restaurant Processes that achieves state of the art
performance on matching human word similarity judgments, and propose a
pipelined architecture for incorporating multi-sense embeddings into language
understanding.
We then test the performance of our model on part-of-speech tagging, named
entity recognition, sentiment analysis, semantic relation identification and
semantic relatedness, controlling for embedding dimensionality. We find that
multi-sense embeddings do improve performance on some tasks (part-of-speech
tagging, semantic relation identification, semantic relatedness) but not on
others (named entity recognition, various forms of sentiment analysis). We
discuss how these differences may be caused by the different role of word sense
information in each of the tasks. The results highlight the importance of
testing embedding models in real applications.
| 2,015 | Computation and Language |
Traversing Knowledge Graphs in Vector Space | Path queries on a knowledge graph can be used to answer compositional
questions such as "What languages are spoken by people living in Lisbon?".
However, knowledge graphs often have missing facts (edges) which disrupts path
queries. Recent models for knowledge base completion impute missing facts by
embedding knowledge graphs in vector spaces. We show that these models can be
recursively applied to answer path queries, but that they suffer from cascading
errors. This motivates a new "compositional" training objective, which
dramatically improves all models' ability to answer path queries, in some cases
more than doubling accuracy. On a standard knowledge base completion task, we
also demonstrate that compositional training acts as a novel form of structural
regularization, reliably improving performance across all base models (reducing
errors by up to 43%) and achieving new state-of-the-art results.
| 2,015 | Computation and Language |
A Hybrid Model for Enhancing Lexical Statistical Machine Translation
(SMT) | The interest in statistical machine translation systems increases currently
due to political and social events in the world. A proposed Statistical Machine
Translation (SMT) based model that can be used to translate a sentence from the
source Language (English) to the target language (Arabic) automatically through
efficiently incorporating different statistical and Natural Language Processing
(NLP) models such as language model, alignment model, phrase based model,
reordering model, and translation model. These models are combined to enhance
the performance of statistical machine translation (SMT). Many implementation
tools have been used in this work such as Moses, Gizaa++, IRSTLM, KenLM, and
BLEU. Based on the implementation, evaluation of this model, and comparing the
generated translation with other implemented machine translation systems like
Google Translate, it was proved that this proposed model has enhanced the
results of the statistical machine translation, and forms a reliable and
efficient model in this field of research.
| 2,015 | Computation and Language |
Personalizing Universal Recurrent Neural Network Language Model with
User Characteristic Features by Social Network Crowdsouring | With the popularity of mobile devices, personalized speech recognizer becomes
more realizable today and highly attractive. Each mobile device is primarily
used by a single user, so it's possible to have a personalized recognizer well
matching to the characteristics of individual user. Although acoustic model
personalization has been investigated for decades, much less work have been
reported on personalizing language model, probably because of the difficulties
in collecting enough personalized corpora. Previous work used the corpora
collected from social networks to solve the problem, but constructing a
personalized model for each user is troublesome. In this paper, we propose a
universal recurrent neural network language model with user characteristic
features, so all users share the same model, except each with different user
characteristic features. These user characteristic features can be obtained by
crowdsouring over social networks, which include huge quantity of texts posted
by users with known friend relationships, who may share some subject topics and
wording patterns. The preliminary experiments on Facebook corpus showed that
this proposed approach not only drastically reduced the model perplexity, but
offered very good improvement in recognition accuracy in n-best rescoring
tests. This approach also mitigated the data sparseness problem for
personalized language models.
| 2,016 | Computation and Language |
Summarization of Films and Documentaries Based on Subtitles and Scripts | We assess the performance of generic text summarization algorithms applied to
films and documentaries, using the well-known behavior of summarization of news
articles as reference. We use three datasets: (i) news articles, (ii) film
scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics
are used for comparing generated summaries against news abstracts, plot
summaries, and synopses. We show that the best performing algorithms are LSA,
for news articles and documentaries, and LexRank and Support Sets, for films.
Despite the different nature of films and documentaries, their relative
behavior is in accordance with that obtained for news articles.
| 2,016 | Computation and Language |
Abstractive Multi-Document Summarization via Phrase Selection and
Merging | We propose an abstraction-based multi-document summarization framework that
can construct new sentences by exploring more fine-grained syntactic units than
sentences, namely, noun/verb phrases. Different from existing abstraction-based
approaches, our method first constructs a pool of concepts and facts
represented by phrases from the input documents. Then new sentences are
generated by selecting and merging informative phrases to maximize the salience
of phrases and meanwhile satisfy the sentence construction constraints. We
employ integer linear optimization for conducting phrase selection and merging
simultaneously in order to achieve the global optimal solution for a summary.
Experimental results on the benchmark data set TAC 2011 show that our framework
outperforms the state-of-the-art models under automated pyramid evaluation
metric, and achieves reasonably well results on manual linguistic quality
evaluation.
| 2,015 | Computation and Language |
Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial
Sentiment Analysis | Although, the fair amount of works in sentiment analysis (SA) and opinion
mining (OM) systems in the last decade and with respect to the performance of
these systems, but it still not desired performance, especially for
morphologically-Rich Language (MRL) such as Arabic, due to the complexities and
challenges exist in the nature of the languages itself. One of these challenges
is the detection of idioms or proverbs phrases within the writer text or
comment. An idiom or proverb is a form of speech or an expression that is
peculiar to itself. Grammatically, it cannot be understood from the individual
meanings of its elements and can yield different sentiment when treats as
separate words. Consequently, In order to facilitate the task of detection and
classification of lexical phrases for automated SA systems, this paper presents
AIPSeLEX a novel idioms/ proverbs sentiment lexicon for modern standard Arabic
(MSA) and colloquial. AIPSeLEX is manually collected and annotated at sentence
level with semantic orientation (positive or negative). The efforts of manually
building and annotating the lexicon are reported. Moreover, we build a
classifier that extracts idioms and proverbs, phrases from text using n-gram
and similarity measure methods. Finally, several experiments were carried out
on various data, including Arabic tweets and Arabic microblogs (hotel
reservation, product reviews, and TV program comments) from publicly available
Arabic online reviews websites (social media, blogs, forums, e-commerce web
sites) to evaluate the coverage and accuracy of AIPSeLEX.
| 2,015 | Computation and Language |
Content Translation: Computer-assisted translation tool for Wikipedia
articles | The quality and quantity of articles in each Wikipedia language varies
greatly. Translating from another Wikipedia is a natural way to add more
content, but the translation process is not properly supported in the software
used by Wikipedia. Past computer-assisted translation tools built for Wikipedia
are not commonly used. We created a tool that adapts to the specific needs of
an open community and to the kind of content in Wikipedia. Qualitative and
quantitative data indicates that the new tool helps users translate articles
easier and faster.
| 2,015 | Computation and Language |
Sparse Overcomplete Word Vector Representations | Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sparse (and optionally binary) vectors. The resulting
representations are more similar to the interpretable features typically used
in NLP, though they are discovered automatically from raw corpora. Because the
vectors are highly sparse, they are computationally easy to work with. Most
importantly, we find that they outperform the original vectors on benchmark
tasks.
| 2,015 | Computation and Language |
Confounds and Consequences in Geotagged Twitter Data | Twitter is often used in quantitative studies that identify
geographically-preferred topics, writing styles, and entities. These studies
rely on either GPS coordinates attached to individual messages, or on the
user-supplied location field in each profile. In this paper, we compare these
data acquisition techniques and quantify the biases that they introduce; we
also measure their effects on linguistic analysis and text-based geolocation.
GPS-tagging and self-reported locations yield measurably different corpora, and
these linguistic differences are partially attributable to differences in
dataset composition by age and gender. Using a latent variable model to induce
age and gender, we show how these demographic variables interact with geography
to affect language use. We also show that the accuracy of text-based
geolocation varies with population demographics, giving the best results for
men above the age of 40.
| 2,015 | Computation and Language |
SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and
Implicature | We introduce a corpus of 7,032 sentences rated by human annotators for
formality, informativeness, and implicature on a 1-7 scale. The corpus was
annotated using Amazon Mechanical Turk. Reliability in the obtained judgments
was examined by comparing mean ratings across two MTurk experiments, and
correlation with pilot annotations (on sentence formality) conducted in a more
controlled setting. Despite the subjectivity and inherent difficulty of the
annotation task, correlations between mean ratings were quite encouraging,
especially on formality and informativeness. We further explored correlation
between the three linguistic variables, genre-wise variation of ratings and
correlations within genres, compatibility with automatic stylistic scoring, and
sentential make-up of a document in terms of style. To date, our corpus is the
largest sentence-level annotated corpus released for formality,
informativeness, and implicature.
| 2,016 | Computation and Language |
A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for
Unsupervised Discovery of Linguistic Units and Generation of High Quality
Features | This paper summarizes the work done by the authors for the Zero Resource
Speech Challenge organized in the technical program of Interspeech 2015. The
goal of the challenge is to discover linguistic units directly from unlabeled
speech data. The Multi-layered Acoustic Tokenizer (MAT) proposed in this work
automatically discovers multiple sets of acoustic tokens from the given corpus.
Each acoustic token set is specified by a set of hyperparameters that describe
the model configuration. These sets of acoustic tokens carry different
characteristics of the given corpus and the language behind thus can be
mutually reinforced. The multiple sets of token labels are then used as the
targets of a Multi-target DNN (MDNN) trained on low-level acoustic features.
Bottleneck features extracted from the MDNN are used as feedback for the MAT
and the MDNN itself. We call this iterative system the Multi-layered Acoustic
Tokenizing Deep Neural Network (MAT-DNN) which generates high quality features
for track 1 of the challenge and acoustic tokens for track 2 of the challenge.
| 2,015 | Computation and Language |
Modeling Order in Neural Word Embeddings at Scale | Natural Language Processing (NLP) systems commonly leverage bag-of-words
co-occurrence techniques to capture semantic and syntactic word relationships.
The resulting word-level distributed representations often ignore morphological
information, though character-level embeddings have proven valuable to NLP
tasks. We propose a new neural language model incorporating both word order and
character order in its embedding. The model produces several vector spaces with
meaningful substructure, as evidenced by its performance of 85.8% on a recent
word-analogy task, exceeding best published syntactic word-analogy scores by a
58% error margin. Furthermore, the model includes several parallel training
methods, most notably allowing a skip-gram network with 160 billion parameters
to be trained overnight on 3 multi-core CPUs, 14x larger than the previous
largest neural network.
| 2,015 | Computation and Language |
Connotation Frames: A Data-Driven Investigation | Through a particular choice of a predicate (e.g., "x violated y"), a writer
can subtly connote a range of implied sentiments and presupposed facts about
the entities x and y: (1) writer's perspective: projecting x as an
"antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes
x, (3) effect: something bad happened to y, (4) value: y is something valuable,
and (5) mental state: y is distressed by the event. We introduce connotation
frames as a representation formalism to organize these rich dimensions of
connotation using typed relations. First, we investigate the feasibility of
obtaining connotative labels through crowdsourcing experiments. We then present
models for predicting the connotation frames of verb predicates based on their
distributional word representations and the interplay between different types
of connotative relations. Empirical results confirm that connotation frames can
be induced from various data sources that reflect how people use language and
give rise to the connotative meanings. We conclude with analytical results that
show the potential use of connotation frames for analyzing subtle biases in
online news media.
| 2,016 | Computation and Language |
WordRank: Learning Word Embeddings via Robust Ranking | Embedding words in a vector space has gained a lot of attention in recent
years. While state-of-the-art methods provide efficient computation of word
similarities via a low-dimensional matrix embedding, their motivation is often
left unclear. In this paper, we argue that word embedding can be naturally
viewed as a ranking problem due to the ranking nature of the evaluation
metrics. Then, based on this insight, we propose a novel framework WordRank
that efficiently estimates word representations via robust ranking, in which
the attention mechanism and robustness to noise are readily achieved via the
DCG-like ranking losses. The performance of WordRank is measured in word
similarity and word analogy benchmarks, and the results are compared to the
state-of-the-art word embedding techniques. Our algorithm is very competitive
to the state-of-the- arts on large corpora, while outperforms them by a
significant margin when the training set is limited (i.e., sparse and noisy).
With 17 million tokens, WordRank performs almost as well as existing methods
using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node
distributed implementation of WordRank is publicly available for general usage.
| 2,016 | Computation and Language |
Leveraging Textual Features for Best Answer Prediction in
Community-based Question Answering | This paper addresses the problem of determining the best answer in
Community-based Question Answering (CQA) websites by focussing on the content.
In particular, we present a system, ACQUA [http://acqua.kmi.open.ac.uk], that
can be installed onto the majority of browsers as a plugin. The service offers
a seamless and accurate prediction of the answer to be accepted. Previous
research on this topic relies on the exploitation of community feedback on the
answers, which involves rating of either users (e.g., reputation) or answers
(e.g. scores manually assigned to answers). We propose a new technique that
leverages the content/textual features of answers in a novel way. Our approach
delivers better results than related linguistics-based solutions and manages to
match rating-based approaches. More specifically, the gain in performance is
achieved by rendering the values of these features into a discretised form. We
also show how our technique manages to deliver equally good results in
real-time settings, as opposed to having to rely on information not always
readily available, such as user ratings and answer scores. We ran an evaluation
on 21 StackExchange websites covering around 4 million questions and more than
8 million answers. We obtain 84% average precision and 70% recall, which shows
that our technique is robust, effective, and widely applicable.
| 2,015 | Computation and Language |
An Ensemble method for Content Selection for Data-to-text Systems | We present a novel approach for automatic report generation from time-series
data, in the context of student feedback generation. Our proposed methodology
treats content selection as a multi-label classification (MLC) problem, which
takes as input time-series data (students' learning data) and outputs a summary
of these data (feedback). Unlike previous work, this method considers all data
simultaneously using ensembles of classifiers, and therefore, it achieves
higher accuracy and F- score compared to meaningful baselines.
| 2,015 | Computation and Language |
Robust Subgraph Generation Improves Abstract Meaning Representation
Parsing | The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F$_1$ on both the LDC2013E117 and
LDC2014T12 datasets.
| 2,015 | Computation and Language |
A cognitive neural architecture able to learn and communicate through
natural language | Communicative interactions involve a kind of procedural knowledge that is
used by the human brain for processing verbal and nonverbal inputs and for
language production. Although considerable work has been done on modeling human
language abilities, it has been difficult to bring them together to a
comprehensive tabula rasa system compatible with current knowledge of how
verbal information is processed in the brain. This work presents a cognitive
system, entirely based on a large-scale neural architecture, which was
developed to shed light on the procedural knowledge involved in language
elaboration. The main component of this system is the central executive, which
is a supervising system that coordinates the other components of the working
memory. In our model, the central executive is a neural network that takes as
input the neural activation states of the short-term memory and yields as
output mental actions, which control the flow of information among the working
memory components through neural gating mechanisms. The proposed system is
capable of learning to communicate through natural language starting from
tabula rasa, without any a priori knowledge of the structure of phrases,
meaning of words, role of the different classes of words, only by interacting
with a human through a text-based interface, using an open-ended incremental
learning process. It is able to learn nouns, verbs, adjectives, pronouns and
other word classes, and to use them in expressive language. The model was
validated on a corpus of 1587 input sentences, based on literature on early
language assessment, at the level of about 4-years old child, and produced 521
output sentences, expressing a broad range of language processing
functionalities.
| 2,015 | Computation and Language |
Combining Temporal Information and Topic Modeling for Cross-Document
Event Ordering | Building unified timelines from a collection of written news articles
requires cross-document event coreference resolution and temporal relation
extraction. In this paper we present an approach event coreference resolution
according to: a) similar temporal information, and b) similar semantic
arguments. Temporal information is detected using an automatic temporal
information system (TIPSem), while semantic information is represented by means
of LDA Topic Modeling. The evaluation of our approach shows that it obtains the
highest Micro-average F-score results in the SemEval2015 Task 4: TimeLine:
Cross-Document Event Ordering (25.36\% for TrackB, 23.15\% for SubtrackB), with
an improvement of up to 6\% in comparison to the other systems. However, our
experiment also showed some draw-backs in the Topic Modeling approach that
degrades performance of the system.
| 2,015 | Computation and Language |
Teaching Machines to Read and Comprehend | Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.
| 2,015 | Computation and Language |
From Paraphrase Database to Compositional Paraphrase Model and Back | The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic)
confidence estimates. However, it is still unclear how it can best be used, due
to the heuristic nature of the confidences and its necessarily incomplete
coverage. We propose models to leverage the phrase pairs from the PPDB to build
parametric paraphrase models that score paraphrase pairs more accurately than
the PPDB's internal scores while simultaneously improving its coverage. They
allow for learning phrase embeddings as well as improved word embeddings.
Moreover, we introduce two new, manually annotated datasets to evaluate
short-phrase paraphrasing models. Using our paraphrase model trained using
PPDB, we achieve state-of-the-art results on standard word and bigram
similarity tasks and beat strong baselines on our new short phrase paraphrase
tasks.
| 2,015 | Computation and Language |
Learning language through pictures | We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently predict its visual representation and the next word in the
sentence. Mimicking an important aspect of human language learning, it acquires
meaning representations for individual words from descriptions of visual
scenes. Moreover, it learns to effectively use sequential structure in semantic
interpretation of multi-word phrases.
| 2,015 | Computation and Language |
Entity-Specific Sentiment Classification of Yahoo News Comments | Sentiment classification is widely used for product reviews and in online
social media such as forums, Twitter, and blogs. However, the problem of
classifying the sentiment of user comments on news sites has not been addressed
yet. News sites cover a wide range of domains including politics, sports,
technology, and entertainment, in contrast to other online social sites such as
forums and review sites, which are specific to a particular domain. A user
associated with a news site is likely to post comments on diverse topics (e.g.,
politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or
Google). Classifying the sentiment of users tied to various entities may help
obtain a holistic view of their personality, which could be useful in
applications such as online advertising, content personalization, and political
campaign planning. In this paper, we formulate the problem of entity-specific
sentiment classification of comments posted on news articles in Yahoo News and
propose novel features that are specific to news comments. Experimental results
show that our models outperform state-of-the-art baselines.
| 2,015 | Computation and Language |
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to
Action Sequences | We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a representation of the observable world state. We
introduce a multi-level aligner that empowers our model to focus on sentence
"regions" salient to the current world state by using multiple abstractions of
the input sentence. In contrast to existing methods, our model uses no
specialized linguistic resources (e.g., parsers) or task-specific annotations
(e.g., seed lexicons). It is therefore generalizable, yet still achieves the
best results reported to-date on a benchmark single-sentence dataset and
competitive results for the limited-training multi-sentence setting. We analyze
our model through a series of ablations that elucidate the contributions of the
primary components of our model.
| 2,015 | Computation and Language |
A Publicly Available Cross-Platform Lemmatizer for Bulgarian | Our dictionary-based lemmatizer for the Bulgarian language presented here is
distributed as free software, publicly available to download and use under the
GPL v3 license. The presented software is written entirely in Java and is
distributed as a GATE plugin. To our best knowledge, at the time of writing
this article, there are not any other free lemmatization tools specifically
targeting the Bulgarian language. The presented lemmatizer is a work in
progress and currently yields an accuracy of about 95% in comparison to the
manually annotated corpus BulTreeBank-Morph, which contains 273933 tokens.
| 2,015 | Computation and Language |
Evaluation of the Accuracy of the BGLemmatizer | This paper reveals the results of an analysis of the accuracy of developed
software for automatic lemmatization for the Bulgarian language. This
lemmatization software is written entirely in Java and is distributed as a GATE
plugin. Certain statistical methods are used to define the accuracy of this
software. The results of the analysis show 95% lemmatization accuracy.
| 2,015 | Computation and Language |
A Bayesian Model for Generative Transition-based Dependency Parsing | We propose a simple, scalable, fully generative model for transition-based
dependency parsing with high accuracy. The model, parameterized by Hierarchical
Pitman-Yor Processes, overcomes the limitations of previous generative models
by allowing fast and accurate inference. We propose an efficient decoding
algorithm based on particle filtering that can adapt the beam size to the
uncertainty in the model while jointly predicting POS tags and parse trees. The
UAS of the parser is on par with that of a greedy discriminative baseline. As a
language model, it obtains better perplexity than a n-gram model by performing
semi-supervised learning over a large unlabelled corpus. We show that the model
is able to generate locally and syntactically coherent sentences, opening the
door to further applications in language generation.
| 2,015 | Computation and Language |
Leveraging Word Embeddings for Spoken Document Summarization | Owing to the rapidly growing multimedia content available on the Internet,
extractive spoken document summarization, with the purpose of automatically
selecting a set of representative sentences from a spoken document to concisely
express the most important theme of the document, has been an active area of
research and experimentation. On the other hand, word embedding has emerged as
a newly favorite research subject because of its excellent performance in many
natural language processing (NLP)-related tasks. However, as far as we are
aware, there are relatively few studies investigating its use in extractive
text or speech summarization. A common thread of leveraging word embeddings in
the summarization process is to represent the document (or sentence) by
averaging the word embeddings of the words occurring in the document (or
sentence). Then, intuitively, the cosine similarity measure can be employed to
determine the relevance degree between a pair of representations. Beyond the
continued efforts made to improve the representation of words, this paper
focuses on building novel and efficient ranking models based on the general
word embedding methods for extractive speech summarization. Experimental
results demonstrate the effectiveness of our proposed methods, compared to
existing state-of-the-art methods.
| 2,015 | Computation and Language |
Distilling Word Embeddings: An Encoding Approach | Distilling knowledge from a well-trained cumbersome network to a small one
has recently become a new research topic, as lightweight neural networks with
high performance are particularly in need in various resource-restricted
systems. This paper addresses the problem of distilling word embeddings for NLP
tasks. We propose an encoding approach to distill task-specific knowledge from
a set of high-dimensional embeddings, which can reduce model complexity by a
large margin as well as retain high accuracy, showing a good compromise between
efficiency and performance. Experiments in two tasks reveal the phenomenon that
distilling knowledge from cumbersome embeddings is better than directly
training neural networks with small embeddings.
| 2,016 | Computation and Language |
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy
Game | Interpersonal relations are fickle, with close friendships often dissolving
into enmity. In this work, we explore linguistic cues that presage such
transitions by studying dyadic interactions in an online strategy game where
players form alliances and break those alliances through betrayal. We
characterize friendships that are unlikely to last and examine temporal
patterns that foretell betrayal.
We reveal that subtle signs of imminent betrayal are encoded in the
conversational patterns of the dyad, even if the victim is not aware of the
relationship's fate. In particular, we find that lasting friendships exhibit a
form of balance that manifests itself through language. In contrast, sudden
changes in the balance of certain conversational attributes---such as positive
sentiment, politeness, or focus on future planning---signal impending betrayal.
| 2,015 | Computation and Language |
Exploiting Text and Network Context for Geolocation of Social Media
Users | Research on automatically geolocating social media users has conventionally
been based on the text content of posts from a given user or the social network
of the user, with very little crossover between the two, and no bench-marking
of the two approaches over compara- ble datasets. We bring the two threads of
research together in first proposing a text-based method based on adaptive
grids, followed by a hybrid network- and text-based method. Evaluating over
three Twitter datasets, we show that the empirical difference between text- and
network-based methods is not great, and that hybridisation of the two is
superior to the component methods, especially in contexts where the user graph
is not well connected. We achieve state-of-the-art results on all three
datasets.
| 2,015 | Computation and Language |
Significance of the levels of spectral valleys with application to
front/back distinction of vowel sounds | An objective critical distance (OCD) has been defined as that spacing between
adjacent formants, when the level of the valley between them reaches the mean
spectral level. The measured OCD lies in the same range (viz., 3-3.5 bark) as
the critical distance determined by subjective experiments for similar
experimental conditions. The level of spectral valley serves a purpose similar
to that of the spacing between the formants with an added advantage that it can
be measured from the spectral envelope without an explicit knowledge of formant
frequencies. Based on the relative spacing of formant frequencies, the level of
the spectral valley, VI (between F1 and F2) is much higher than the level of
VII (spectral valley between F2 and F3) for back vowels and vice-versa for
front vowels. Classification of vowels into front/back distinction with the
difference (VI-VII) as an acoustic feature, tested using TIMIT, NTIMIT, Tamil
and Kannada language databases gives, on the average, an accuracy of about 95%,
which is comparable to the accuracy (90.6%) obtained using a neural network
classifier trained and tested using MFCC as the feature vector for TIMIT
database. The acoustic feature (VI-VII) has also been tested for its robustness
on the TIMIT database for additive white and babble noise and an accuracy of
about 95% has been obtained for SNRs down to 25 dB for both types of noise.
| 2,015 | Computation and Language |
Tree-structured composition in neural networks without tree-structured
architectures | Tree-structured neural networks encode a particular tree geometry for a
sentence in the network design. However, these models have at best only
slightly outperformed simpler sequence-based models. We hypothesize that neural
sequence models like LSTMs are in fact able to discover and implicitly use
recursive compositional structure, at least for tasks with clear cues to that
structure in the data. We demonstrate this possibility using an artificial data
task for which recursive compositional structure is crucial, and find an
LSTM-based sequence model can indeed learn to exploit the underlying tree
structure. However, its performance consistently lags behind that of tree
models, even on large training sets, suggesting that tree-structured models are
more effective at exploiting recursive structure.
| 2,015 | Computation and Language |
Author Identification using Multi-headed Recurrent Neural Networks | Recurrent neural networks (RNNs) are very good at modelling the flow of text,
but typically need to be trained on a far larger corpus than is available for
the PAN 2015 Author Identification task. This paper describes a novel approach
where the output layer of a character-level RNN language model is split into
several independent predictive sub-models, each representing an author, while
the recurrent layer is shared by all. This allows the recurrent layer to model
the language as a whole without over-fitting, while the outputs select aspects
of the underlying model that reflect their author's style. The method proves
competitive, ranking first in two of the four languages.
| 2,016 | Computation and Language |
Parsing Natural Language Sentences by Semi-supervised Methods | We present our work on semi-supervised parsing of natural language sentences,
focusing on multi-source crosslingual transfer of delexicalized dependency
parsers. We first evaluate the influence of treebank annotation styles on
parsing performance, focusing on adposition attachment style. Then, we present
KLcpos3, an empirical language similarity measure, designed and tuned for
source parser weighting in multi-source delexicalized parser transfer. And
finally, we introduce a novel resource combination method, based on
interpolation of trained parser models.
| 2,015 | Computation and Language |
Recognize Foreign Low-Frequency Words with Similar Pairs | Low-frequency words place a major challenge for automatic speech recognition
(ASR). The probabilities of these words, which are often important name
entities, are generally under-estimated by the language model (LM) due to their
limited occurrences in the training data. Recently, we proposed a word-pair
approach to deal with the problem, which borrows information of frequent words
to enhance the probabilities of low-frequency words. This paper presents an
extension to the word-pair method by involving multiple `predicting words' to
produce better estimation for low-frequency words. We also employ this approach
to deal with out-of-language words in the task of multi-lingual speech
recognition.
| 2,015 | Computation and Language |
Emotion Analysis of Songs Based on Lyrical and Audio Features | In this paper, a method is proposed to detect the emotion of a song based on
its lyrical and audio features. Lyrical features are generated by segmentation
of lyrics during the process of data extraction. ANEW and WordNet knowledge is
then incorporated to compute Valence and Arousal values. In addition to this,
linguistic association rules are applied to ensure that the issue of ambiguity
is properly addressed. Audio features are used to supplement the lyrical ones
and include attributes like energy, tempo, and danceability. These features are
extracted from The Echo Nest, a widely used music intelligence platform.
Construction of training and test sets is done on the basis of social tags
extracted from the last.fm website. The classification is done by applying
feature weighting and stepwise threshold reduction on the k-Nearest Neighbors
algorithm to provide fuzziness in the classification.
| 2,015 | Computation and Language |
Non-distributional Word Vector Representations | Data-driven representation learning for words is a technique of central
importance in NLP. While indisputably useful as a source of features in
downstream tasks, such vectors tend to consist of uninterpretable components
whose relationship to the categories of traditional lexical semantic theories
is tenuous at best. We present a method for constructing interpretable word
vectors from hand-crafted linguistic resources like WordNet, FrameNet etc.
These vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We
analyze their performance on state-of-the-art evaluation methods for
distributional models of word vectors and find they are competitive to standard
distributional approaches.
| 2,015 | Computation and Language |
Editorial for the First Workshop on Mining Scientific Papers:
Computational Linguistics and Bibliometrics | The workshop "Mining Scientific Papers: Computational Linguistics and
Bibliometrics" (CLBib 2015), co-located with the 15th International Society of
Scientometrics and Informetrics Conference (ISSI 2015), brought together
researchers in Bibliometrics and Computational Linguistics in order to study
the ways Bibliometrics can benefit from large-scale text analytics and sense
mining of scientific papers, thus exploring the interdisciplinarity of
Bibliometrics and Natural Language Processing (NLP). The goals of the workshop
were to answer questions like: How can we enhance author network analysis and
Bibliometrics using data obtained by text analytics? What insights can NLP
provide on the structure of scientific writing, on citation networks, and on
in-text citation analysis? This workshop is the first step to foster the
reflection on the interdisciplinarity and the benefits that the two disciplines
Bibliometrics and Natural Language Processing can drive from it.
| 2,015 | Computation and Language |
Comparing and evaluating extended Lambek calculi | Lambeks Syntactic Calculus, commonly referred to as the Lambek calculus, was
innovative in many ways, notably as a precursor of linear logic. But it also
showed that we could treat our grammatical framework as a logic (as opposed to
a logical theory). However, though it was successful in giving at least a basic
treatment of many linguistic phenomena, it was also clear that a slightly more
expressive logical calculus was needed for many other cases. Therefore, many
extensions and variants of the Lambek calculus have been proposed, since the
eighties and up until the present day. As a result, there is now a large class
of calculi, each with its own empirical successes and theoretical results, but
also each with its own logical primitives. This raises the question: how do we
compare and evaluate these different logical formalisms? To answer this
question, I present two unifying frameworks for these extended Lambek calculi.
Both are proof net calculi with graph contraction criteria. The first calculus
is a very general system: you specify the structure of your sequents and it
gives you the connectives and contractions which correspond to it. The calculus
can be extended with structural rules, which translate directly into graph
rewrite rules. The second calculus is first-order (multiplicative
intuitionistic) linear logic, which turns out to have several other,
independently proposed extensions of the Lambek calculus as fragments. I will
illustrate the use of each calculus in building bridges between analyses
proposed in different frameworks, in highlighting differences and in helping to
identify problems.
| 2,015 | Computation and Language |
Pragmatic Side Effects | In the quest to give a formal compositional semantics to natural languages,
semanticists have started turning their attention to phenomena that have been
also considered as parts of pragmatics (e.g., discourse anaphora and
presupposition projection). To account for these phenomena, the very kinds of
meanings assigned to words and phrases are often revisited. To be more
specific, in the prevalent paradigm of modeling natural language denotations
using the simply-typed lambda calculus (higher-order logic) this means
revisiting the types of denotations assigned to individual parts of speech.
However, the lambda calculus also serves as a fundamental theory of
computation, and in the study of computation, similar type shifts have been
employed to give a meaning to side effects. Side effects in programming
languages correspond to actions that go beyond the lexical scope of an
expression (a thrown exception might propagate throughout a program, a variable
modified at one point might later be read at an another) or even beyond the
scope of the program itself (a program might interact with the outside world by
e.g., printing documents, making sounds, operating robotic limbs...).
| 2,015 | Computation and Language |
Comparing the writing style of real and artificial papers | Recent years have witnessed the increase of competition in science. While
promoting the quality of research in many cases, an intense competition among
scientists can also trigger unethical scientific behaviors. To increase the
total number of published papers, some authors even resort to software tools
that are able to produce grammatical, but meaningless scientific manuscripts.
Because automatically generated papers can be misunderstood as real papers, it
becomes of paramount importance to develop means to identify these scientific
frauds. In this paper, I devise a methodology to distinguish real manuscripts
from those generated with SCIGen, an automatic paper generator. Upon modeling
texts as complex networks (CN), it was possible to discriminate real from fake
papers with at least 89\% of accuracy. A systematic analysis of features
relevance revealed that the accessibility and betweenness were useful in
particular cases, even though the relevance depended upon the dataset. The
successful application of the methods described here show, as a proof of
principle, that network features can be used to identify scientific gibberish
papers. In addition, the CN-based approach can be combined in a straightforward
fashion with traditional statistical language processing methods to improve the
performance in identifying artificially generated papers.
| 2,015 | Computation and Language |
"The Sum of Its Parts": Joint Learning of Word and Phrase
Representations with Autoencoders | Recently, there has been a lot of effort to represent words in continuous
vector spaces. Those representations have been shown to capture both semantic
and syntactic information about words. However, distributed representations of
phrases remain a challenge. We introduce a novel model that jointly learns word
vector representations and their summation. Word representations are learnt
using the word co-occurrence statistical information. To embed sequences of
words (i.e. phrases) with different sizes into a common semantic space, we
propose to average word vector representations. In contrast with previous
methods which reported a posteriori some compositionality aspects by simple
summation, we simultaneously train words to sum, while keeping the maximum
information from the original vectors. We evaluate the quality of the word
representations on several classical word evaluation tasks, and we introduce a
novel task to evaluate the quality of the phrase representations. While our
distributed representations compete with other methods of learning word
representations on word evaluations, we show that they give better performance
on the phrase evaluation. Such representations of phrases could be interesting
for many tasks in natural language processing.
| 2,015 | Computation and Language |
LCSTS: A Large Scale Chinese Short Text Summarization Dataset | Automatic text summarization is widely regarded as the highly difficult
problem, partially because of the lack of large text summarization data set.
Due to the great challenge of constructing the large scale summaries for full
text, in this paper, we introduce a large corpus of Chinese short text
summarization dataset constructed from the Chinese microblogging website Sina
Weibo, which is released to the public
{http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over
2 million real Chinese short texts with short summaries given by the author of
each text. We also manually tagged the relevance of 10,666 short summaries with
their corresponding short texts. Based on the corpus, we introduce recurrent
neural network for the summary generation and achieve promising results, which
not only shows the usefulness of the proposed corpus for short text
summarization research, but also provides a baseline for further research on
this topic.
| 2,016 | Computation and Language |
A Neural Conversational Model | Conversational modeling is an important task in natural language
understanding and machine intelligence. Although previous approaches exist,
they are often restricted to specific domains (e.g., booking an airline ticket)
and require hand-crafted rules. In this paper, we present a simple approach for
this task which uses the recently proposed sequence to sequence framework. Our
model converses by predicting the next sentence given the previous sentence or
sentences in a conversation. The strength of our model is that it can be
trained end-to-end and thus requires much fewer hand-crafted rules. We find
that this straightforward model can generate simple conversations given a large
conversational training dataset. Our preliminary results suggest that, despite
optimizing the wrong objective function, the model is able to converse well. It
is able extract knowledge from both a domain specific dataset, and from a
large, noisy, and general domain dataset of movie subtitles. On a
domain-specific IT helpdesk dataset, the model can find a solution to a
technical problem via conversations. On a noisy open-domain movie transcript
dataset, the model can perform simple forms of common sense reasoning. As
expected, we also find that the lack of consistency is a common failure mode of
our model.
| 2,015 | Computation and Language |
Structured Training for Neural Network Transition-Based Parsing | We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron with beam-search decoding. On the Penn Treebank, our parser reaches
94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge
is the best accuracy on Stanford Dependencies to date. We also provide in-depth
ablative analysis to determine which aspects of our model provide the largest
gains in accuracy.
| 2,015 | Computation and Language |
Extreme Extraction: Only One Hour per Relation | Information Extraction (IE) aims to automatically generate a large knowledge
base from natural language text, but progress remains slow. Supervised learning
requires copious human annotation, while unsupervised and weakly supervised
approaches do not deliver competitive accuracy. As a result, most fielded
applications of IE, as well as the leading TAC-KBP systems, rely on significant
amounts of manual engineering. Even "Extreme" methods, such as those reported
in Freedman et al. 2011, require about 10 hours of expert labor per relation.
This paper shows how to reduce that effort by an order of magnitude. We
present a novel system, InstaRead, that streamlines authoring with an ensemble
of methods: 1) encoding extraction rules in an expressive and compositional
representation, 2) guiding the user to promising rules based on corpus
statistics and mined resources, and 3) introducing a new interactive
development cycle that provides immediate feedback --- even on large datasets.
Experiments show that experts can create quality extractors in under an hour
and even NLP novices can author good extractors. These extractors equal or
outperform ones obtained by comparably supervised and state-of-the-art
distantly supervised approaches.
| 2,015 | Computation and Language |
A Deep Memory-based Architecture for Sequence-to-Sequence Learning | We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence
learning, which performs the task through a series of nonlinear transformations
from the representation of the input sequence (e.g., a Chinese sentence) to the
final output sequence (e.g., translation to English). Inspired by the recently
proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate
representations in stacked layers of memories, and use read-write operations on
the memories to realize the nonlinear transformations between the
representations. The types of transformations are designed in advance but the
parameters are learned from data. Through layer-by-layer transformations,
DEEPMEMORY can model complicated relations between sequences necessary for
applications such as machine translation between distant languages. The
architecture can be trained with normal back-propagation on sequenceto-sequence
data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is
broad enough to subsume the state-of-the-art neural translation model in
(Bahdanau et al., 2015) as its special case, while significantly improving upon
the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely
neural network-based, can achieve performance comparable to the traditional
phrase-based machine translation system Moses with a small vocabulary and a
modest parameter size.
| 2,016 | Computation and Language |
Answer Sequence Learning with Neural Networks for Answer Selection in
Community Question Answering | In this paper, the answer selection problem in community question answering
(CQA) is regarded as an answer sequence labeling task, and a novel approach is
proposed based on the recurrent architecture for this problem. Our approach
applies convolution neural networks (CNNs) to learning the joint representation
of question-answer pair firstly, and then uses the joint representation as
input of the long short-term memory (LSTM) to learn the answer sequence of a
question for labeling the matching quality of each answer. Experiments
conducted on the SemEval 2015 CQA dataset shows the effectiveness of our
approach.
| 2,015 | Computation and Language |
Distributional Sentence Entailment Using Density Matrices | Categorical compositional distributional model of Coecke et al. (2010)
suggests a way to combine grammatical composition of the formal, type logical
models with the corpus based, empirical word representations of distributional
semantics. This paper contributes to the project by expanding the model to also
capture entailment relations. This is achieved by extending the representations
of words from points in meaning space to density operators, which are
probability distributions on the subspaces of the space. A symmetric measure of
similarity and an asymmetric measure of entailment is defined, where lexical
entailment is measured using von Neumann entropy, the quantum variant of
Kullback-Leibler divergence. Lexical entailment, combined with the composition
map on word representations, provides a method to obtain entailment relations
on the level of sentences. Truth theoretic and corpus-based examples are
provided.
| 2,015 | Computation and Language |
A Neural Network Approach to Context-Sensitive Generation of
Conversational Responses | We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into account previous dialog utterances. Our dynamic-context generative
models show consistent gains over both context-sensitive and
non-context-sensitive Machine Translation and Information Retrieval baselines.
| 2,015 | Computation and Language |
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