Titles
stringlengths 6
220
| Abstracts
stringlengths 37
3.26k
| Years
int64 1.99k
2.02k
| Categories
stringclasses 1
value |
|---|---|---|---|
Non-crossing dependencies: least effort, not grammar | The use of null hypotheses (in a statistical sense) is common in hard
sciences but not in theoretical linguistics. Here the null hypothesis that the
low frequency of syntactic dependency crossings is expected by an arbitrary
ordering of words is rejected. It is shown that this would require star
dependency structures, which are both unrealistic and too restrictive. The
hypothesis of the limited resources of the human brain is revisited. Stronger
null hypotheses taking into account actual dependency lengths for the
likelihood of crossings are presented. Those hypotheses suggests that crossings
are likely to reduce when dependencies are shortened. A hypothesis based on
pressure to reduce dependency lengths is more parsimonious than a principle of
minimization of crossings or a grammatical ban that is totally dissociated from
the general and non-linguistic principle of economy.
| 2,020 | Computation and Language |
word2vec Parameter Learning Explained | The word2vec model and application by Mikolov et al. have attracted a great
amount of attention in recent two years. The vector representations of words
learned by word2vec models have been shown to carry semantic meanings and are
useful in various NLP tasks. As an increasing number of researchers would like
to experiment with word2vec or similar techniques, I notice that there lacks a
material that comprehensively explains the parameter learning process of word
embedding models in details, thus preventing researchers that are non-experts
in neural networks from understanding the working mechanism of such models.
This note provides detailed derivations and explanations of the parameter
update equations of the word2vec models, including the original continuous
bag-of-word (CBOW) and skip-gram (SG) models, as well as advanced optimization
techniques, including hierarchical softmax and negative sampling. Intuitive
interpretations of the gradient equations are also provided alongside
mathematical derivations.
In the appendix, a review on the basics of neuron networks and
backpropagation is provided. I also created an interactive demo, wevi, to
facilitate the intuitive understanding of the model.
| 2,016 | Computation and Language |
Distributed Representations for Compositional Semantics | The mathematical representation of semantics is a key issue for Natural
Language Processing (NLP). A lot of research has been devoted to finding ways
of representing the semantics of individual words in vector spaces.
Distributional approaches --- meaning distributed representations that exploit
co-occurrence statistics of large corpora --- have proved popular and
successful across a number of tasks. However, natural language usually comes in
structures beyond the word level, with meaning arising not only from the
individual words but also the structure they are contained in at the phrasal or
sentential level. Modelling the compositional process by which the meaning of
an utterance arises from the meaning of its parts is an equally fundamental
task of NLP.
This dissertation explores methods for learning distributed semantic
representations and models for composing these into representations for larger
linguistic units. Our underlying hypothesis is that neural models are a
suitable vehicle for learning semantically rich representations and that such
representations in turn are suitable vehicles for solving important tasks in
natural language processing. The contribution of this thesis is a thorough
evaluation of our hypothesis, as part of which we introduce several new
approaches to representation learning and compositional semantics, as well as
multiple state-of-the-art models which apply distributed semantic
representations to various tasks in NLP.
| 2,014 | Computation and Language |
Statistically Significant Detection of Linguistic Change | We propose a new computational approach for tracking and detecting
statistically significant linguistic shifts in the meaning and usage of words.
Such linguistic shifts are especially prevalent on the Internet, where the
rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis
approach constructs property time series of word usage, and then uses
statistically sound change point detection algorithms to identify significant
linguistic shifts.
We consider and analyze three approaches of increasing complexity to generate
such linguistic property time series, the culmination of which uses
distributional characteristics inferred from word co-occurrences. Using
recently proposed deep neural language models, we first train vector
representations of words for each time period. Second, we warp the vector
spaces into one unified coordinate system. Finally, we construct a
distance-based distributional time series for each word to track it's
linguistic displacement over time.
We demonstrate that our approach is scalable by tracking linguistic change
across years of micro-blogging using Twitter, a decade of product reviews using
a corpus of movie reviews from Amazon, and a century of written books using the
Google Book-ngrams. Our analysis reveals interesting patterns of language usage
change commensurate with each medium.
| 2,014 | Computation and Language |
A Text to Speech (TTS) System with English to Punjabi Conversion | The paper aims to show how an application can be developed that converts the
English language into the Punjabi Language, and the same application can
convert the Text to Speech(TTS) i.e. pronounce the text. This application can
be really beneficial for those with special needs.
| 2,014 | Computation and Language |
Learning Multi-Relational Semantics Using Neural-Embedding Models | In this paper we present a unified framework for modeling multi-relational
representations, scoring, and learning, and conduct an empirical study of
several recent multi-relational embedding models under the framework. We
investigate the different choices of relation operators based on linear and
bilinear transformations, and also the effects of entity representations by
incorporating unsupervised vectors pre-trained on extra textual resources. Our
results show several interesting findings, enabling the design of a simple
embedding model that achieves the new state-of-the-art performance on a popular
knowledge base completion task evaluated on Freebase.
| 2,014 | Computation and Language |
Resolution of Difficult Pronouns Using the ROSS Method | A new natural language understanding method for disambiguation of difficult
pronouns is described. Difficult pronouns are those pronouns for which a level
of world or domain knowledge is needed in order to perform anaphoral or other
types of resolution. Resolution of difficult pronouns may in some cases require
a prior step involving the application of inference to a situation that is
represented by the natural language text. A general method is described: it
performs entity resolution and pronoun resolution. An extension to the general
pronoun resolution method performs inference as an embedded commonsense
reasoning method. The general method and the embedded method utilize features
of the ROSS representational scheme; in particular the methods use ROSS
ontology classes and the ROSS situation model. The overall method is a working
solution that solves the following Winograd schemas: a) trophy and suitcase, b)
person lifts person, c) person pays detective, and d) councilmen and
demonstrators.
| 2,014 | Computation and Language |
Definition of Visual Speech Element and Research on a Method of
Extracting Feature Vector for Korean Lip-Reading | In this paper, we defined the viseme (visual speech element) and described
about the method of extracting visual feature vector. We defined the 10 visemes
based on vowel by analyzing of Korean utterance and proposed the method of
extracting the 20-dimensional visual feature vector, combination of static
features and dynamic features. Lastly, we took an experiment in recognizing
words based on 3-viseme HMM and evaluated the efficiency.
| 2,014 | Computation and Language |
Investigating the Role of Prior Disambiguation in Deep-learning
Compositional Models of Meaning | This paper aims to explore the effect of prior disambiguation on neural
network- based compositional models, with the hope that better semantic
representations for text compounds can be produced. We disambiguate the input
word vectors before they are fed into a compositional deep net. A series of
evaluations shows the positive effect of prior disambiguation for such deep
models.
| 2,014 | Computation and Language |
Retrofitting Word Vectors to Semantic Lexicons | Vector space word representations are learned from distributional information
of words in large corpora. Although such statistics are semantically
informative, they disregard the valuable information that is contained in
semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This
paper proposes a method for refining vector space representations using
relational information from semantic lexicons by encouraging linked words to
have similar vector representations, and it makes no assumptions about how the
input vectors were constructed. Evaluated on a battery of standard lexical
semantic evaluation tasks in several languages, we obtain substantial
improvements starting with a variety of word vector models. Our refinement
method outperforms prior techniques for incorporating semantic lexicons into
the word vector training algorithms.
| 2,015 | Computation and Language |
Errata: Distant Supervision for Relation Extraction with Matrix
Completion | The essence of distantly supervised relation extraction is that it is an
incomplete multi-label classification problem with sparse and noisy features.
To tackle the sparsity and noise challenges, we propose solving the
classification problem using matrix completion on factorized matrix of
minimized rank. We formulate relation classification as completing the unknown
labels of testing items (entity pairs) in a sparse matrix that concatenates
training and testing textual features with training labels. Our algorithmic
framework is based on the assumption that the rank of item-by-feature and
item-by-label joint matrix is low. We apply two optimization models to recover
the underlying low-rank matrix leveraging the sparsity of feature-label matrix.
The matrix completion problem is then solved by the fixed point continuation
(FPC) algorithm, which can find the global optimum. Experiments on two widely
used datasets with different dimensions of textual features demonstrate that
our low-rank matrix completion approach significantly outperforms the baseline
and the state-of-the-art methods.
| 2,014 | Computation and Language |
Opinion mining of text documents written in Macedonian language | The ability to extract public opinion from web portals such as review sites,
social networks and blogs will enable companies and individuals to form a view,
an attitude and make decisions without having to do lengthy and costly
researches and surveys. In this paper machine learning techniques are used for
determining the polarity of forum posts on kajgana which are written in
Macedonian language. The posts are classified as being positive, negative or
neutral. We test different feature metrics and classifiers and provide detailed
evaluation of their participation in improving the overall performance on a
manually generated dataset. By achieving 92% accuracy, we show that the
performance of systems for automated opinion mining is comparable to a human
evaluator, thus making it a viable option for text data analysis. Finally, we
present a few statistics derived from the forum posts using the developed
system.
| 2,014 | Computation and Language |
Using graph transformation algorithms to generate natural language
equivalents of icons expressing medical concepts | A graphical language addresses the need to communicate medical information in
a synthetic way. Medical concepts are expressed by icons conveying fast visual
information about patients' current state or about the known effects of drugs.
In order to increase the visual language's acceptance and usability, a natural
language generation interface is currently developed. In this context, this
paper describes the use of an informatics method ---graph transformation--- to
prepare data consisting of concepts in an OWL-DL ontology for use in a natural
language generation component. The OWL concept may be considered as a
star-shaped graph with a central node. The method transforms it into a graph
representing the deep semantic structure of a natural language phrase. This
work may be of future use in other contexts where ontology concepts have to be
mapped to half-formalized natural language expressions.
| 2,014 | Computation and Language |
Relations World: A Possibilistic Graphical Model | We explore the idea of using a "possibilistic graphical model" as the basis
for a world model that drives a dialog system. As a first step we have
developed a system that uses text-based dialog to derive a model of the user's
family relations. The system leverages its world model to infer relational
triples, to learn to recover from upstream coreference resolution errors and
ambiguities, and to learn context-dependent paraphrase models. We also explore
some theoretical aspects of the underlying graphical model.
| 2,014 | Computation and Language |
Network Motifs Analysis of Croatian Literature | In this paper we analyse network motifs in the co-occurrence directed
networks constructed from five different texts (four books and one portal) in
the Croatian language. After preparing the data and network construction, we
perform the network motif analysis. We analyse the motif frequencies and
Z-scores in the five networks. We present the triad significance profile for
five datasets. Furthermore, we compare our results with the existing results
for the linguistic networks. Firstly, we show that the triad significance
profile for the Croatian language is very similar with the other languages and
all the networks belong to the same family of networks. However, there are
certain differences between the Croatian language and other analysed languages.
We conclude that this is due to the free word-order of the Croatian language.
| 2,014 | Computation and Language |
Type-Driven Incremental Semantic Parsing with Polymorphism | Semantic parsing has made significant progress, but most current semantic
parsers are extremely slow (CKY-based) and rather primitive in representation.
We introduce three new techniques to tackle these problems. First, we design
the first linear-time incremental shift-reduce-style semantic parsing algorithm
which is more efficient than conventional cubic-time bottom-up semantic
parsers. Second, our parser, being type-driven instead of syntax-driven, uses
type-checking to decide the direction of reduction, which eliminates the need
for a syntactic grammar such as CCG. Third, to fully exploit the power of
type-driven semantic parsing beyond simple types (such as entities and truth
values), we borrow from programming language theory the concepts of subtype
polymorphism and parametric polymorphism to enrich the type system in order to
better guide the parsing. Our system learns very accurate parses in GeoQuery,
Jobs and Atis domains.
| 2,014 | Computation and Language |
Linking GloVe with word2vec | The Global Vectors for word representation (GloVe), introduced by Jeffrey
Pennington et al. is reported to be an efficient and effective method for
learning vector representations of words. State-of-the-art performance is also
provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec
tool. In this note, we explain the similarities between the training objectives
of the two models, and show that the objective of SGNS is similar to the
objective of a specialized form of GloVe, though their cost functions are
defined differently.
| 2,014 | Computation and Language |
A Joint Probabilistic Classification Model of Relevant and Irrelevant
Sentences in Mathematical Word Problems | Estimating the difficulty level of math word problems is an important task
for many educational applications. Identification of relevant and irrelevant
sentences in math word problems is an important step for calculating the
difficulty levels of such problems. This paper addresses a novel application of
text categorization to identify two types of sentences in mathematical word
problems, namely relevant and irrelevant sentences. A novel joint probabilistic
classification model is proposed to estimate the joint probability of
classification decisions for all sentences of a math word problem by utilizing
the correlation among all sentences along with the correlation between the
question sentence and other sentences, and sentence text. The proposed model is
compared with i) a SVM classifier which makes independent classification
decisions for individual sentences by only using the sentence text and ii) a
novel SVM classifier that considers the correlation between the question
sentence and other sentences along with the sentence text. An extensive set of
experiments demonstrates the effectiveness of the joint probabilistic
classification model for identifying relevant and irrelevant sentences as well
as the novel SVM classifier that utilizes the correlation between the question
sentence and other sentences. Furthermore, empirical results and analysis show
that i) it is highly beneficial not to remove stopwords and ii) utilizing part
of speech tagging does not make a significant improvement although it has been
shown to be effective for the related task of math word problem type
classification.
| 2,014 | Computation and Language |
Pre-processing of Domain Ontology Graph Generation System in Punjabi | This paper describes pre-processing phase of ontology graph generation system
from Punjabi text documents of different domains. This research paper focuses
on pre-processing of Punjabi text documents. Pre-processing is structured
representation of the input text. Pre-processing of ontology graph generation
includes allowing input restrictions to the text, removal of special symbols
and punctuation marks, removal of duplicate terms, removal of stop words,
extract terms by matching input terms with dictionary and gazetteer lists
terms.
| 2,014 | Computation and Language |
One Vector is Not Enough: Entity-Augmented Distributional Semantics for
Discourse Relations | Discourse relations bind smaller linguistic units into coherent texts.
However, automatically identifying discourse relations is difficult, because it
requires understanding the semantics of the linked arguments. A more subtle
challenge is that it is not enough to represent the meaning of each argument of
a discourse relation, because the relation may depend on links between
lower-level components, such as entity mentions. Our solution computes
distributional meaning representations by composition up the syntactic parse
tree. A key difference from previous work on compositional distributional
semantics is that we also compute representations for entity mentions, using a
novel downward compositional pass. Discourse relations are predicted from the
distributional representations of the arguments, and also of their coreferent
entity mentions. The resulting system obtains substantial improvements over the
previous state-of-the-art in predicting implicit discourse relations in the
Penn Discourse Treebank.
| 2,014 | Computation and Language |
LABR: A Large Scale Arabic Sentiment Analysis Benchmark | We introduce LABR, the largest sentiment analysis dataset to-date for the
Arabic language. It consists of over 63,000 book reviews, each rated on a scale
of 1 to 5 stars. We investigate the properties of the dataset, and present its
statistics. We explore using the dataset for two tasks: (1) sentiment polarity
classification; and (2) ratings classification. Moreover, we provide standard
splits of the dataset into training, validation and testing, for both polarity
and ratings classification, in both balanced and unbalanced settings. We extend
our previous work by performing a comprehensive analysis on the dataset. In
particular, we perform an extended survey of the different classifiers
typically used for the sentiment polarity classification problem. We also
construct a sentiment lexicon from the dataset that contains both single and
compound sentiment words and we explore its effectiveness. We make the dataset
and experimental details publicly available.
| 2,015 | Computation and Language |
Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic
Models and Encyclopedic Knowledge | We present a method for coarse-grained cross-lingual alignment of comparable
texts: segments consisting of contiguous paragraphs that discuss the same theme
(e.g. history, economy) are aligned based on induced multilingual topics. The
method combines three ideas: a two-level LDA model that filters out words that
do not convey themes, an HMM that models the ordering of themes in the
collection of documents, and language-independent concept annotations to serve
as a cross-language bridge and to strengthen the connection between paragraphs
in the same segment through concept relations. The method is evaluated on
English and French data previously used for monolingual alignment. The results
show state-of-the-art performance in both monolingual and cross-lingual
settings.
| 2,014 | Computation and Language |
Using Sentence Plausibility to Learn the Semantics of Transitive Verbs | The functional approach to compositional distributional semantics considers
transitive verbs to be linear maps that transform the distributional vectors
representing nouns into a vector representing a sentence. We conduct an initial
investigation that uses a matrix consisting of the parameters of a logistic
regression classifier trained on a plausibility task as a transitive verb
function. We compare our method to a commonly used corpus-based method for
constructing a verb matrix and find that the plausibility training may be more
effective for disambiguation tasks.
| 2,014 | Computation and Language |
Understanding confounding effects in linguistic coordination: an
information-theoretic approach | We suggest an information-theoretic approach for measuring stylistic
coordination in dialogues. The proposed measure has a simple predictive
interpretation and can account for various confounding factors through proper
conditioning. We revisit some of the previous studies that reported strong
signatures of stylistic accommodation, and find that a significant part of the
observed coordination can be attributed to a simple confounding effect - length
coordination. Specifically, longer utterances tend to be followed by longer
responses, which gives rise to spurious correlations in the other stylistic
features. We propose a test to distinguish correlations in length due to
contextual factors (topic of conversation, user verbosity, etc.) and
turn-by-turn coordination. We also suggest a test to identify whether stylistic
coordination persists even after accounting for length coordination and
contextual factors.
| 2,015 | Computation and Language |
Tiered Clustering to Improve Lexical Entailment | Many tasks in Natural Language Processing involve recognizing lexical
entailment. Two different approaches to this problem have been proposed
recently that are quite different from each other. The first is an asymmetric
similarity measure designed to give high scores when the contexts of the
narrower term in the entailment are a subset of those of the broader term. The
second is a supervised approach where a classifier is learned to predict
entailment given a concatenated latent vector representation of the word. Both
of these approaches are vector space models that use a single context vector as
a representation of the word. In this work, I study the effects of clustering
words into senses and using these multiple context vectors to infer entailment
using extensions of these two algorithms. I find that this approach offers some
improvement to these entailment algorithms.
| 2,014 | Computation and Language |
Watsonsim: Overview of a Question Answering Engine | The objective of the project is to design and run a system similar to Watson,
designed to answer Jeopardy questions. In the course of a semester, we
developed an open source question answering system using the Indri, Lucene,
Bing and Google search engines, Apache UIMA, Open- and CoreNLP, and Weka among
additional modules. By the end of the semester, we achieved 18% accuracy on
Jeopardy questions, and work has not stopped since then.
| 2,014 | Computation and Language |
Effective Use of Word Order for Text Categorization with Convolutional
Neural Networks | Convolutional neural network (CNN) is a neural network that can make use of
the internal structure of data such as the 2D structure of image data. This
paper studies CNN on text categorization to exploit the 1D structure (namely,
word order) of text data for accurate prediction. Instead of using
low-dimensional word vectors as input as is often done, we directly apply CNN
to high-dimensional text data, which leads to directly learning embedding of
small text regions for use in classification. In addition to a straightforward
adaptation of CNN from image to text, a simple but new variation which employs
bag-of-word conversion in the convolution layer is proposed. An extension to
combine multiple convolution layers is also explored for higher accuracy. The
experiments demonstrate the effectiveness of our approach in comparison with
state-of-the-art methods.
| 2,015 | Computation and Language |
Mary Astell's words in A Serious Proposal to the Ladies (part I), a
lexicographic inquiry with NooJ | In the following article we elected to study with NooJ the lexis of a 17 th
century text, Mary Astell's seminal essay, A Serious Proposal to the Ladies,
part I, published in 1694. We first focused on the semantics to see how Astell
builds her vindication of the female sex, which words she uses to sensitise
women to their alienated condition and promote their education. Then we studied
the morphology of the lexemes (which is different from contemporary English)
used by the author, thanks to the NooJ tools we have devised for this purpose.
NooJ has great functionalities for lexicographic work. Its commands and graphs
prove to be most efficient in the spotting of archaic words or variants in
spelling. Introduction In our previous articles, we have studied the
singularities of 17 th century English within the framework of a diachronic
analysis thanks to syntactical and morphological graphs and thanks to the
dictionaries we have compiled from a corpus that may be expanded overtime. Our
early work was based on a limited corpus of English travel literature to Greece
in the 17 th century. This article deals with a late seventeenth century text
written by a woman philosopher and essayist, Mary Astell (1666--1731),
considered as one of the first English feminists. Astell wrote her essay at a
time in English history when women were "the weaker vessel" and their main
business in life was to charm and please men by their looks and submissiveness.
In this essay we will see how NooJ can help us analyse Astell's rhetoric (what
point of view does she adopt, does she speak in her own name, in the name of
all women, what is her representation of men and women and their relationships
in the text, what are the goals of education?). Then we will turn our attention
to the morphology of words in the text and use NooJ commands and graphs to
carry out a lexicographic inquiry into Astell's lexemes.
| 2,014 | Computation and Language |
A perspective on the advancement of natural language processing tasks
via topological analysis of complex networks | Comment on "Approaching human language with complex networks" by Cong and Liu
(Physics of Life Reviews, Volume 11, Issue 4, December 2014, Pages 598-618).
| 2,014 | Computation and Language |
Deep Learning for Answer Sentence Selection | Answer sentence selection is the task of identifying sentences that contain
the answer to a given question. This is an important problem in its own right
as well as in the larger context of open domain question answering. We propose
a novel approach to solving this task via means of distributed representations,
and learn to match questions with answers by considering their semantic
encoding. This contrasts prior work on this task, which typically relies on
classifiers with large numbers of hand-crafted syntactic and semantic features
and various external resources. Our approach does not require any feature
engineering nor does it involve specialist linguistic data, making this model
easily applicable to a wide range of domains and languages. Experimental
results on a standard benchmark dataset from TREC demonstrate that---despite
its simplicity---our model matches state of the art performance on the answer
sentence selection task.
| 2,014 | Computation and Language |
Context-Dependent Fine-Grained Entity Type Tagging | Entity type tagging is the task of assigning category labels to each mention
of an entity in a document. While standard systems focus on a small set of
types, recent work (Ling and Weld, 2012) suggests that using a large
fine-grained label set can lead to dramatic improvements in downstream tasks.
In the absence of labeled training data, existing fine-grained tagging systems
obtain examples automatically, using resolved entities and their types
extracted from a knowledge base. However, since the appropriate type often
depends on context (e.g. Washington could be tagged either as city or
government), this procedure can result in spurious labels, leading to poorer
generalization. We propose the task of context-dependent fine type tagging,
where the set of acceptable labels for a mention is restricted to only those
deducible from the local context (e.g. sentence or document). We introduce new
resources for this task: 12,017 mentions annotated with their context-dependent
fine types, and we provide baseline experimental results on this data.
| 2,016 | Computation and Language |
Exemplar Dynamics and Sound Merger in Language | We develop a model of phonological contrast in natural language.
Specifically, the model describes the maintenance of contrast between different
words in a language, and the elimination of such contrast when sounds in the
words merge. An example of such a contrast is that provided by the two vowel
sounds 'i' and 'e', which distinguish pairs of words such as 'pin' and 'pen' in
most dialects of English. We model language users' knowledge of the
pronunciation of a word as consisting of collections of labeled exemplars
stored in memory. Each exemplar is a detailed memory of a particular utterance
of the word in question. In our model an exemplar is represented by one or two
phonetic variables along with a weight indicating how strong the memory of the
utterance is. Starting from an exemplar-level model we derive
integro-differential equations for the evolution of exemplar density fields in
phonetic space. Using these latter equations we investigate under what
conditions two sounds merge, thus eliminating the contrast. Our main conclusion
is that for the preservation of phonological contrast, it is necessary that
anomalous utterances of a given word are discarded, and not merely stored in
memory as an exemplar of another word.
| 2,015 | Computation and Language |
Integer-Programming Ensemble of Temporal-Relations Classifiers | The extraction and understanding of temporal events and their relations are
major challenges in natural language processing. Processing text on a
sentence-by-sentence or expression-by-expression basis often fails, in part due
to the challenge of capturing the global consistency of the text. We present an
ensemble method, which reconciles the outputs of multiple classifiers of
temporal expressions across the text using integer programming. Computational
experiments show that the ensemble improves upon the best individual results
from two recent challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and
SemEval-2016 Task 12 (Clinical TempEval).
| 2,020 | Computation and Language |
On Using Very Large Target Vocabulary for Neural Machine Translation | Neural machine translation, a recently proposed approach to machine
translation based purely on neural networks, has shown promising results
compared to the existing approaches such as phrase-based statistical machine
translation. Despite its recent success, neural machine translation has its
limitation in handling a larger vocabulary, as training complexity as well as
decoding complexity increase proportionally to the number of target words. In
this paper, we propose a method that allows us to use a very large target
vocabulary without increasing training complexity, based on importance
sampling. We show that decoding can be efficiently done even with the model
having a very large target vocabulary by selecting only a small subset of the
whole target vocabulary. The models trained by the proposed approach are
empirically found to outperform the baseline models with a small vocabulary as
well as the LSTM-based neural machine translation models. Furthermore, when we
use the ensemble of a few models with very large target vocabularies, we
achieve the state-of-the-art translation performance (measured by BLEU) on the
English->German translation and almost as high performance as state-of-the-art
English->French translation system.
| 2,015 | Computation and Language |
Practice in Synonym Extraction at Large Scale | Synonym extraction is an important task in natural language processing and
often used as a submodule in query expansion, question answering and other
applications. Automatic synonym extractor is highly preferred for large scale
applications. Previous studies in synonym extraction are most limited to small
scale datasets. In this paper, we build a large dataset with 3.4 million
synonym/non-synonym pairs to capture the challenges in real world scenarios. We
proposed (1) a new cost function to accommodate the unbalanced learning
problem, and (2) a feature learning based deep neural network to model the
complicated relationships in synonym pairs. We compare several different
approaches based on SVMs and neural networks, and find out a novel feature
learning based neural network outperforms the methods with hand-assigned
features. Specifically, the best performance of our model surpasses the SVM
baseline with a significant 97\% relative improvement.
| 2,015 | Computation and Language |
Learning Word Representations from Relational Graphs | Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if there are numerous semantic relations between two words, we can
expect some of the attributes of one of the words to be inherited by the other.
Motivated by this close connection between attributes and relations, given a
relational graph in which words are inter- connected via numerous semantic
relations, we propose a method to learn a latent representation for the
individual words. The proposed method considers not only the co-occurrences of
words as done by existing approaches for word representation learning, but also
the semantic relations in which two words co-occur. To evaluate the accuracy of
the word representations learnt using the proposed method, we use the learnt
word representations to solve semantic word analogy problems. Our experimental
results show that it is possible to learn better word representations by using
semantic semantics between words.
| 2,014 | Computation and Language |
Rediscovering the Alphabet - On the Innate Universal Grammar | Universal Grammar (UG) theory has been one of the most important research
topics in linguistics since introduced five decades ago. UG specifies the
restricted set of languages learnable by human brain, and thus, many
researchers believe in its biological roots. Numerous empirical studies of
neurobiological and cognitive functions of the human brain, and of many natural
languages, have been conducted to unveil some aspects of UG. This, however,
resulted in different and sometimes contradicting theories that do not indicate
a universally unique grammar. In this research, we tackle the UG problem from
an entirely different perspective. We search for the Unique Universal Grammar
(UUG) that facilitates communication and knowledge transfer, the sole purpose
of a language. We formulate this UG and show that it is unique, intrinsic, and
cosmic, rather than humanistic. Initial analysis on a widespread natural
language already showed some positive results.
| 2,014 | Computation and Language |
Unsupervised Induction of Semantic Roles within a Reconstruction-Error
Minimization Framework | We introduce a new approach to unsupervised estimation of feature-rich
semantic role labeling models. Our model consists of two components: (1) an
encoding component: a semantic role labeling model which predicts roles given a
rich set of syntactic and lexical features; (2) a reconstruction component: a
tensor factorization model which relies on roles to predict argument fillers.
When the components are estimated jointly to minimize errors in argument
reconstruction, the induced roles largely correspond to roles defined in
annotated resources. Our method performs on par with most accurate role
induction methods on English and German, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
languages.
| 2,014 | Computation and Language |
Zipf's Law and the Frequency of Characters or Words of Oracles | The article discusses the frequency of characters of Oracle,concluding that
the frequency and the rank of a word or character is fit to Zipf-Mandelboit Law
or Zipf's law with three parameters,and figuring out the parameters based on
the frequency,and pointing out that what some researchers of Oracle call the
assembling on the two ends is just a description by their impression about the
Oracle data.
| 2,014 | Computation and Language |
Statistical Patterns in Written Language | Quantitative linguistics has been allowed, in the last few decades, within
the admittedly blurry boundaries of the field of complex systems. A growing
host of applied mathematicians and statistical physicists devote their efforts
to disclose regularities, correlations, patterns, and structural properties of
language streams, using techniques borrowed from statistics and information
theory. Overall, results can still be categorized as modest, but the prospects
are promising: medium- and long-range features in the organization of human
language -which are beyond the scope of traditional linguistics- have already
emerged from this kind of analysis and continue to be reported, contributing a
new perspective to our understanding of this most complex communication system.
This short book is intended to review some of these recent contributions.
| 2,017 | Computation and Language |
A Robust Transformation-Based Learning Approach Using Ripple Down Rules
for Part-of-Speech Tagging | In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; thus allowing systematic control of the interaction between the
rules. Experimental results on 13 languages show that our approach is fast in
terms of training time and tagging speed. Furthermore, our approach obtains
very competitive accuracy in comparison to state-of-the-art POS and
morphological taggers.
| 2,016 | Computation and Language |
Ripple Down Rules for Question Answering | Recent years have witnessed a new trend of building ontology-based question
answering systems. These systems use semantic web information to produce more
precise answers to users' queries. However, these systems are mostly designed
for English. In this paper, we introduce an ontology-based question answering
system named KbQAS which, to the best of our knowledge, is the first one made
for Vietnamese. KbQAS employs our question analysis approach that
systematically constructs a knowledge base of grammar rules to convert each
input question into an intermediate representation element. KbQAS then takes
the intermediate representation element with respect to a target ontology and
applies concept-matching techniques to return an answer. On a wide range of
Vietnamese questions, experimental results show that the performance of KbQAS
is promising with accuracies of 84.1% and 82.4% for analyzing input questions
and retrieving output answers, respectively. Furthermore, our question analysis
approach can easily be applied to new domains and new languages, thus saving
time and human effort.
| 2,017 | Computation and Language |
Incorporating Both Distributional and Relational Semantics in Word
Representations | We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.
| 2,015 | Computation and Language |
Unsupervised Domain Adaptation with Feature Embeddings | Representation learning is the dominant technique for unsupervised domain
adaptation, but existing approaches often require the specification of "pivot
features" that generalize across domains, which are selected by task-specific
heuristics. We show that a novel but simple feature embedding approach provides
better performance, by exploiting the feature template structure common in NLP
problems.
| 2,015 | Computation and Language |
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR
Systems | Transcription of broadcast news is an interesting and challenging application
for large-vocabulary continuous speech recognition (LVCSR). We present in
detail the structure of a manually segmented and annotated corpus including
over 160 hours of German broadcast news, and propose it as an evaluation
framework of LVCSR systems. We show our own experimental results on the corpus,
achieved with a state-of-the-art LVCSR decoder, measuring the effect of
different feature sets and decoding parameters, and thereby demonstrate that
real-time decoding of our test set is feasible on a desktop PC at 9.2% word
error rate.
| 2,014 | Computation and Language |
Rule-based Emotion Detection on Social Media: Putting Tweets on
Plutchik's Wheel | We study sentiment analysis beyond the typical granularity of polarity and
instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an
extension to the Rule-Based Emission Model algorithm to deduce such emotions
from human-written messages. We evaluate our approach on two different datasets
and compare its performance with the current state-of-the-art techniques for
emotion detection, including a recursive auto-encoder. The results of the
experimental study suggest that RBEM-Emo is a promising approach advancing the
current state-of-the-art in emotion detection.
| 2,014 | Computation and Language |
Scaling laws in human speech, decreasing emergence of new words and a
generalized model | Human language, as a typical complex system, its organization and evolution
is an attractive topic for both physical and cultural researchers. In this
paper, we present the first exhaustive analysis of the text organization of
human speech. Two important results are that: (i) the construction and
organization of spoken language can be characterized as Zipf's law and Heaps'
law, as observed in written texts; (ii) word frequency vs. rank distribution
and the growth of distinct words with the increase of text length shows
significant differences between book and speech. In speech word frequency
distribution are more concentrated on higher frequency words, and the emergence
of new words decreases much rapidly when the content length grows. Based on
these observations, a new generalized model is proposed to explain these
complex dynamical behaviors and the differences between speech and book.
| 2,015 | Computation and Language |
Rehabilitation of Count-based Models for Word Vector Representations | Recent works on word representations mostly rely on predictive models.
Distributed word representations (aka word embeddings) are trained to optimally
predict the contexts in which the corresponding words tend to appear. Such
models have succeeded in capturing word similarties as well as semantic and
syntactic regularities. Instead, we aim at reviving interest in a model based
on counts. We present a systematic study of the use of the Hellinger distance
to extract semantic representations from the word co-occurence statistics of
large text corpora. We show that this distance gives good performance on word
similarity and analogy tasks, with a proper type and size of context, and a
dimensionality reduction based on a stochastic low-rank approximation. Besides
being both simple and intuitive, this method also provides an encoding function
which can be used to infer unseen words or phrases. This becomes a clear
advantage compared to predictive models which must train these new words.
| 2,015 | Computation and Language |
Application of Topic Models to Judgments from Public Procurement Domain | In this work, automatic analysis of themes contained in a large corpora of
judgments from public procurement domain is performed. The employed technique
is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed,
to use LDA in conjunction with recently developed method of unsupervised
keyword extraction. Such an approach improves the interpretability of the
automatically obtained topics and allows for better computational performance.
The described analysis illustrates a potential of the method in detecting
recurring themes and discovering temporal trends in lodged contract appeals.
These results may be in future applied to improve information retrieval from
repositories of legal texts or as auxiliary material for legal analyses carried
out by human experts.
| 2,014 | Computation and Language |
Ensemble of Generative and Discriminative Techniques for Sentiment
Analysis of Movie Reviews | Sentiment analysis is a common task in natural language processing that aims
to detect polarity of a text document (typically a consumer review). In the
simplest settings, we discriminate only between positive and negative
sentiment, turning the task into a standard binary classification problem. We
compare several ma- chine learning approaches to this problem, and combine them
to achieve the best possible results. We show how to use for this task the
standard generative lan- guage models, which are slightly complementary to the
state of the art techniques. We achieve strong results on a well-known dataset
of IMDB movie reviews. Our results are easily reproducible, as we publish also
the code needed to repeat the experiments. This should simplify further advance
of the state of the art, as other researchers can combine their techniques with
ours with little effort.
| 2,015 | Computation and Language |
Word Network Topic Model: A Simple but General Solution for Short and
Imbalanced Texts | The short text has been the prevalent format for information of Internet in
recent decades, especially with the development of online social media, whose
millions of users generate a vast number of short messages everyday. Although
sophisticated signals delivered by the short text make it a promising source
for topic modeling, its extreme sparsity and imbalance brings unprecedented
challenges to conventional topic models like LDA and its variants. Aiming at
presenting a simple but general solution for topic modeling in short texts, we
present a word co-occurrence network based model named WNTM to tackle the
sparsity and imbalance simultaneously. Different from previous approaches, WNTM
models the distribution over topics for each word instead of learning topics
for each document, which successfully enhance the semantic density of data
space without importing too much time or space complexity. Meanwhile, the rich
contextual information preserved in the word-word space also guarantees its
sensitivity in identifying rare topics with convincing quality. Furthermore,
employing the same Gibbs sampling with LDA makes WNTM easily to be extended to
various application scenarios. Extensive validations on both short and normal
texts testify the outperformance of WNTM as compared to baseline methods. And
finally we also demonstrate its potential in precisely discovering newly
emerging topics or unexpected events in Weibo at pretty early stages.
| 2,014 | Computation and Language |
Computational Model to Generate Case-Inflected Forms of Masculine Nouns
for Word Search in Sanskrit E-Text | The problem of word search in Sanskrit is inseparable from complexities that
include those caused by euphonic conjunctions and case-inflections. The
case-inflectional forms of a noun normally number 24 owing to the fact that in
Sanskrit there are eight cases and three numbers-singular, dual and plural. The
traditional method of generating these inflectional forms is rather elaborate
owing to the fact that there are differences in the forms generated between
even very similar words and there are subtle nuances involved. Further, it
would be a cumbersome exercise to generate and search for 24 forms of a word
during a word search in a large text, using the currently available
case-inflectional form generators. This study presents a new approach to
generating case-inflectional forms that is simpler to compute. Further, an
optimized model that is sufficient for generating only those word forms that
are required in a word search and is more than 80% efficient compared to the
complete case-inflectional forms generator, is presented in this study for the
first time.
| 2,014 | Computation and Language |
Deep Speech: Scaling up end-to-end speech recognition | We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. In contrast, our system does not need hand-designed
components to model background noise, reverberation, or speaker variation, but
instead directly learns a function that is robust to such effects. We do not
need a phoneme dictionary, nor even the concept of a "phoneme." Key to our
approach is a well-optimized RNN training system that uses multiple GPUs, as
well as a set of novel data synthesis techniques that allow us to efficiently
obtain a large amount of varied data for training. Our system, called Deep
Speech, outperforms previously published results on the widely studied
Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech
also handles challenging noisy environments better than widely used,
state-of-the-art commercial speech systems.
| 2,014 | Computation and Language |
Effective sampling for large-scale automated writing evaluation systems | Automated writing evaluation (AWE) has been shown to be an effective
mechanism for quickly providing feedback to students. It has already seen wide
adoption in enterprise-scale applications and is starting to be adopted in
large-scale contexts. Training an AWE model has historically required a single
batch of several hundred writing examples and human scores for each of them.
This requirement limits large-scale adoption of AWE since human-scoring essays
is costly. Here we evaluate algorithms for ensuring that AWE models are
consistently trained using the most informative essays. Our results show how to
minimize training set sizes while maximizing predictive performance, thereby
reducing cost without unduly sacrificing accuracy. We conclude with a
discussion of how to integrate this approach into large-scale AWE systems.
| 2,014 | Computation and Language |
Entity-Augmented Distributional Semantics for Discourse Relations | Discourse relations bind smaller linguistic elements into coherent texts.
However, automatically identifying discourse relations is difficult, because it
requires understanding the semantics of the linked sentences. A more subtle
challenge is that it is not enough to represent the meaning of each sentence of
a discourse relation, because the relation may depend on links between
lower-level elements, such as entity mentions. Our solution computes
distributional meaning representations by composition up the syntactic parse
tree. A key difference from previous work on compositional distributional
semantics is that we also compute representations for entity mentions, using a
novel downward compositional pass. Discourse relations are predicted not only
from the distributional representations of the sentences, but also of their
coreferent entity mentions. The resulting system obtains substantial
improvements over the previous state-of-the-art in predicting implicit
discourse relations in the Penn Discourse Treebank.
| 2,015 | Computation and Language |
Incorporating Both Distributional and Relational Semantics in Word
Representations | We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.
| 2,015 | Computation and Language |
A Simple and Efficient Method To Generate Word Sense Representations | Distributed representations of words have boosted the performance of many
Natural Language Processing tasks. However, usually only one representation per
word is obtained, not acknowledging the fact that some words have multiple
meanings. This has a negative effect on the individual word representations and
the language model as a whole. In this paper we present a simple model that
enables recent techniques for building word vectors to represent distinct
senses of polysemic words. In our assessment of this model we show that it is
able to effectively discriminate between words' senses and to do so in a
computationally efficient manner.
| 2,016 | Computation and Language |
Supertagging: Introduction, learning, and application | Supertagging is an approach originally developed by Bangalore and Joshi
(1999) to improve the parsing efficiency. In the beginning, the scholars used
small training datasets and somewhat na\"ive smoothing techniques to learn the
probability distributions of supertags. Since its inception, the applicability
of Supertags has been explored for TAG (tree-adjoining grammar) formalism as
well as other related yet, different formalisms such as CCG. This article will
try to summarize the various chapters, relevant to statistical parsing, from
the most recent edited book volume (Bangalore and Joshi, 2010). The chapters
were selected so as to blend the learning of supertags, its integration into
full-scale parsing, and in semantic parsing.
| 2,014 | Computation and Language |
N-gram-Based Low-Dimensional Representation for Document Classification | The bag-of-words (BOW) model is the common approach for classifying
documents, where words are used as feature for training a classifier. This
generally involves a huge number of features. Some techniques, such as Latent
Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been
designed to summarize documents in a lower dimension with the least semantic
information loss. Some semantic information is nevertheless always lost, since
only words are considered. Instead, we aim at using information coming from
n-grams to overcome this limitation, while remaining in a low-dimension space.
Many approaches, such as the Skip-gram model, provide good word vector
representations very quickly. We propose to average these representations to
obtain representations of n-grams. All n-grams are thus embedded in a same
semantic space. A K-means clustering can then group them into semantic
concepts. The number of features is therefore dramatically reduced and
documents can be represented as bag of semantic concepts. We show that this
model outperforms LSA and LDA on a sentiment classification task, and yields
similar results than a traditional BOW-model with far less features.
| 2,015 | Computation and Language |
Leveraging Monolingual Data for Crosslingual Compositional Word
Representations | In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both bilingual and monolingual data, and it is scalable to large
vocabularies and large quantities of data. The key component of our approach is
what we refer to as a monolingual inclusion criterion, that exploits the
observation that phrases are more closely semantically related to their
sub-phrases than to other randomly sampled phrases. We evaluate our method on a
well-established crosslingual document classification task and achieve results
that are either comparable, or greatly improve upon previous state-of-the-art
methods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for
the English to German and German to English sub-tasks respectively. The former
advances the state of the art by 0.9% points of accuracy, the latter is an
absolute improvement upon the previous state of the art by 7.7% points of
accuracy and an improvement of 33.0% in error reduction.
| 2,015 | Computation and Language |
Inducing Semantic Representation from Text by Jointly Predicting and
Factorizing Relations | In this work, we propose a new method to integrate two recent lines of work:
unsupervised induction of shallow semantics (e.g., semantic roles) and
factorization of relations in text and knowledge bases. Our model consists of
two components: (1) an encoding component: a semantic role labeling model which
predicts roles given a rich set of syntactic and lexical features; (2) a
reconstruction component: a tensor factorization model which relies on roles to
predict argument fillers. When the components are estimated jointly to minimize
errors in argument reconstruction, the induced roles largely correspond to
roles defined in annotated resources. Our method performs on par with most
accurate role induction methods on English, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
language.
| 2,015 | Computation and Language |
Embedding Word Similarity with Neural Machine Translation | Neural language models learn word representations, or embeddings, that
capture rich linguistic and conceptual information. Here we investigate the
embeddings learned by neural machine translation models, a recently-developed
class of neural language model. We show that embeddings from translation models
outperform those learned by monolingual models at tasks that require knowledge
of both conceptual similarity and lexical-syntactic role. We further show that
these effects hold when translating from both English to French and English to
German, and argue that the desirable properties of translation embeddings
should emerge largely independently of the source and target languages.
Finally, we apply a new method for training neural translation models with very
large vocabularies, and show that this vocabulary expansion algorithm results
in minimal degradation of embedding quality. Our embedding spaces can be
queried in an online demo and downloaded from our web page. Overall, our
analyses indicate that translation-based embeddings should be used in
applications that require concepts to be organised according to similarity
and/or lexical function, while monolingual embeddings are better suited to
modelling (nonspecific) inter-word relatedness.
| 2,015 | Computation and Language |
Improving zero-shot learning by mitigating the hubness problem | The zero-shot paradigm exploits vector-based word representations extracted
from text corpora with unsupervised methods to learn general mapping functions
from other feature spaces onto word space, where the words associated to the
nearest neighbours of the mapped vectors are used as their linguistic labels.
We show that the neighbourhoods of the mapped elements are strongly polluted by
hubs, vectors that tend to be near a high proportion of items, pushing their
correct labels down the neighbour list. After illustrating the problem
empirically, we propose a simple method to correct it by taking the proximity
distribution of potential neighbours across many mapped vectors into account.
We show that this correction leads to consistent improvements in realistic
zero-shot experiments in the cross-lingual, image labeling and image retrieval
domains.
| 2,015 | Computation and Language |
Embedding Entities and Relations for Learning and Inference in Knowledge
Bases | We consider learning representations of entities and relations in KBs using
the neural-embedding approach. We show that most existing models, including NTN
(Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized
under a unified learning framework, where entities are low-dimensional vectors
learned from a neural network and relations are bilinear and/or linear mapping
functions. Under this framework, we compare a variety of embedding models on
the link prediction task. We show that a simple bilinear formulation achieves
new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2%
vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach
that utilizes the learned relation embeddings to mine logical rules such as
"BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that
embeddings learned from the bilinear objective are particularly good at
capturing relational semantics and that the composition of relations is
characterized by matrix multiplication. More interestingly, we demonstrate that
our embedding-based rule extraction approach successfully outperforms a
state-of-the-art confidence-based rule mining approach in mining Horn rules
that involve compositional reasoning.
| 2,015 | Computation and Language |
Outperforming Word2Vec on Analogy Tasks with Random Projections | We present a distributed vector representation based on a simplification of
the BEAGLE system, designed in the context of the Sigma cognitive architecture.
Our method does not require gradient-based training of neural networks, matrix
decompositions as with LSA, or convolutions as with BEAGLE. All that is
involved is a sum of random vectors and their pointwise products. Despite the
simplicity of this technique, it gives state-of-the-art results on analogy
problems, in most cases better than Word2Vec. To explain this success, we
interpret it as a dimension reduction via random projection.
| 2,015 | Computation and Language |
Word Representations via Gaussian Embedding | Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product or cosine similarity, and enabling more expressive
parameterization of decision boundaries. This paper advocates for density-based
distributed embeddings and presents a method for learning representations in
the space of Gaussian distributions. We compare performance on various word
embedding benchmarks, investigate the ability of these embeddings to model
entailment and other asymmetric relationships, and explore novel properties of
the representation.
| 2,015 | Computation and Language |
Extraction of Salient Sentences from Labelled Documents | We present a hierarchical convolutional document model with an architecture
designed to support introspection of the document structure. Using this model,
we show how to use visualisation techniques from the computer vision literature
to identify and extract topic-relevant sentences.
We also introduce a new scalable evaluation technique for automatic sentence
extraction systems that avoids the need for time consuming human annotation of
validation data.
| 2,015 | Computation and Language |
Tailoring Word Embeddings for Bilexical Predictions: An Experimental
Comparison | We investigate the problem of inducing word embeddings that are tailored for
a particular bilexical relation. Our learning algorithm takes an existing
lexical vector space and compresses it such that the resulting word embeddings
are good predictors for a target bilexical relation. In experiments we show
that task-specific embeddings can benefit both the quality and efficiency in
lexical prediction tasks.
| 2,015 | Computation and Language |
Language Recognition using Random Indexing | Random Indexing is a simple implementation of Random Projections with a wide
range of applications. It can solve a variety of problems with good accuracy
without introducing much complexity. Here we use it for identifying the
language of text samples. We present a novel method of generating language
representation vectors using letter blocks. Further, we show that the method is
easily implemented and requires little computational power and space.
Experiments on a number of model parameters illustrate certain properties about
high dimensional sparse vector representations of data. Proof of statistically
relevant language vectors are shown through the extremely high success of
various language recognition tasks. On a difficult data set of 21,000 short
sentences from 21 different languages, our model performs a language
recognition task and achieves 97.8% accuracy, comparable to state-of-the-art
methods.
| 2,015 | Computation and Language |
Diverse Embedding Neural Network Language Models | We propose Diverse Embedding Neural Network (DENN), a novel architecture for
language models (LMs). A DENNLM projects the input word history vector onto
multiple diverse low-dimensional sub-spaces instead of a single
higher-dimensional sub-space as in conventional feed-forward neural network
LMs. We encourage these sub-spaces to be diverse during network training
through an augmented loss function. Our language modeling experiments on the
Penn Treebank data set show the performance benefit of using a DENNLM.
| 2,015 | Computation and Language |
Pragmatic Neural Language Modelling in Machine Translation | This paper presents an in-depth investigation on integrating neural language
models in translation systems. Scaling neural language models is a difficult
task, but crucial for real-world applications. This paper evaluates the impact
on end-to-end MT quality of both new and existing scaling techniques. We show
when explicitly normalising neural models is necessary and what optimisation
tricks one should use in such scenarios. We also focus on scalable training
algorithms and investigate noise contrastive estimation and diagonal contexts
as sources for further speed improvements. We explore the trade-offs between
neural models and back-off n-gram models and find that neural models make
strong candidates for natural language applications in memory constrained
environments, yet still lag behind traditional models in raw translation
quality. We conclude with a set of recommendations one should follow to build a
scalable neural language model for MT.
| 2,015 | Computation and Language |
Bayesian Optimisation for Machine Translation | This paper presents novel Bayesian optimisation algorithms for minimum error
rate training of statistical machine translation systems. We explore two
classes of algorithms for efficiently exploring the translation space, with the
first based on N-best lists and the second based on a hypergraph representation
that compactly represents an exponential number of translation options. Our
algorithms exhibit faster convergence and are capable of obtaining lower error
rates than the existing translation model specific approaches, all within a
generic Bayesian optimisation framework. Further more, we also introduce a
random embedding algorithm to scale our approach to sparse high dimensional
feature sets.
| 2,014 | Computation and Language |
Reply to the commentary "Be careful when assuming the obvious", by P.
Alday | Here we respond to some comments by Alday concerning headedness in linguistic
theory and the validity of the assumptions of a mathematical model for word
order. For brevity, we focus only on two assumptions: the unit of measurement
of dependency length and the monotonicity of the cost of a dependency as a
function of its length. We also revise the implicit psychological bias in
Alday's comments. Notwithstanding, Alday is indicating the path for linguistic
research with his unusual concerns about parsimony from multiple dimensions.
| 2,015 | Computation and Language |
A prototype Malayalam to Sign Language Automatic Translator | Sign language, which is a medium of communication for deaf people, uses
manual communication and body language to convey meaning, as opposed to using
sound. This paper presents a prototype Malayalam text to sign language
translation system. The proposed system takes Malayalam text as input and
generates corresponding Sign Language. Output animation is rendered using a
computer generated model. This system will help to disseminate information to
the deaf people in public utility places like railways, banks, hospitals etc.
This will also act as an educational tool in learning Sign Language.
| 2,015 | Computation and Language |
Grammar as a Foreign Language | Syntactic constituency parsing is a fundamental problem in natural language
processing and has been the subject of intensive research and engineering for
decades. As a result, the most accurate parsers are domain specific, complex,
and inefficient. In this paper we show that the domain agnostic
attention-enhanced sequence-to-sequence model achieves state-of-the-art results
on the most widely used syntactic constituency parsing dataset, when trained on
a large synthetic corpus that was annotated using existing parsers. It also
matches the performance of standard parsers when trained only on a small
human-annotated dataset, which shows that this model is highly data-efficient,
in contrast to sequence-to-sequence models without the attention mechanism. Our
parser is also fast, processing over a hundred sentences per second with an
unoptimized CPU implementation.
| 2,015 | Computation and Language |
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary | SentiWordNet is an important lexical resource supporting sentiment analysis
in opinion mining applications. In this paper, we propose a novel approach to
construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated
from WordNet in which each synset has numerical scores to indicate its opinion
polarities. Many previous studies obtained these scores by applying a machine
learning method to WordNet. However, Vietnamese WordNet is not available
unfortunately by the time of this paper. Therefore, we propose a method to
construct VSWN from a Vietnamese dictionary, not from WordNet. We show the
effectiveness of the proposed method by generating a VSWN with 39,561 synsets
automatically. The method is experimentally tested with 266 synsets with aspect
of positivity and negativity. It attains a competitive result compared with
English SentiWordNet that is 0.066 and 0.052 differences for positivity and
negativity sets respectively.
| 2,014 | Computation and Language |
Persian Sentiment Analyzer: A Framework based on a Novel Feature
Selection Method | In the recent decade, with the enormous growth of digital content in internet
and databases, sentiment analysis has received more and more attention between
information retrieval and natural language processing researchers. Sentiment
analysis aims to use automated tools to detect subjective information from
reviews. One of the main challenges in sentiment analysis is feature selection.
Feature selection is widely used as the first stage of analysis and
classification tasks to reduce the dimension of problem, and improve speed by
the elimination of irrelevant and redundant features. Up to now as there are
few researches conducted on feature selection in sentiment analysis, there are
very rare works for Persian sentiment analysis. This paper considers the
problem of sentiment classification using different feature selection methods
for online customer reviews in Persian language. Three of the challenges of
Persian text are using of a wide variety of declensional suffixes, different
word spacing and many informal or colloquial words. In this paper we study
these challenges by proposing a model for sentiment classification of Persian
review documents. The proposed model is based on lemmatization and feature
selection and is employed Naive Bayes algorithm for classification. We evaluate
the performance of the model on a manually gathered collection of cellphone
reviews, where the results show the effectiveness of the proposed approaches.
| 2,014 | Computation and Language |
Quantifying origin and character of long-range correlations in narrative
texts | In natural language using short sentences is considered efficient for
communication. However, a text composed exclusively of such sentences looks
technical and reads boring. A text composed of long ones, on the other hand,
demands significantly more effort for comprehension. Studying characteristics
of the sentence length variability (SLV) in a large corpus of world-famous
literary texts shows that an appealing and aesthetic optimum appears somewhere
in between and involves selfsimilar, cascade-like alternation of various
lengths sentences. A related quantitative observation is that the power spectra
S(f) of thus characterized SLV universally develop a convincing `1/f^beta'
scaling with the average exponent beta =~ 1/2, close to what has been
identified before in musical compositions or in the brain waves. An
overwhelming majority of the studied texts simply obeys such fractal attributes
but especially spectacular in this respect are hypertext-like, "stream of
consciousness" novels. In addition, they appear to develop structures
characteristic of irreducibly interwoven sets of fractals called multifractals.
Scaling of S(f) in the present context implies existence of the long-range
correlations in texts and appearance of multifractality indicates that they
carry even a nonlinear component. A distinct role of the full stops in inducing
the long-range correlations in texts is evidenced by the fact that the above
quantitative characteristics on the long-range correlations manifest themselves
in variation of the full stops recurrence times along texts, thus in SLV, but
to a much lesser degree in the recurrence times of the most frequent words. In
this latter case the nonlinear correlations, thus multifractality, disappear
even completely for all the texts considered. Treated as one extra word, the
full stops at the same time appear to obey the Zipfian rank-frequency
distribution, however.
| 2,016 | Computation and Language |
Simple Image Description Generator via a Linear Phrase-Based Approach | Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
on the recently release Microsoft COCO dataset.
| 2,015 | Computation and Language |
Probing the topological properties of complex networks modeling short
written texts | In recent years, graph theory has been widely employed to probe several
language properties. More specifically, the so-called word adjacency model has
been proven useful for tackling several practical problems, especially those
relying on textual stylistic analysis. The most common approach to treat texts
as networks has simply considered either large pieces of texts or entire books.
This approach has certainly worked well -- many informative discoveries have
been made this way -- but it raises an uncomfortable question: could there be
important topological patterns in small pieces of texts? To address this
problem, the topological properties of subtexts sampled from entire books was
probed. Statistical analyzes performed on a dataset comprising 50 novels
revealed that most of the traditional topological measurements are stable for
short subtexts. When the performance of the authorship recognition task was
analyzed, it was found that a proper sampling yields a discriminability similar
to the one found with full texts. Surprisingly, the support vector machine
classification based on the characterization of short texts outperformed the
one performed with entire books. These findings suggest that a local
topological analysis of large documents might improve its global
characterization. Most importantly, it was verified, as a proof of principle,
that short texts can be analyzed with the methods and concepts of complex
networks. As a consequence, the techniques described here can be extended in a
straightforward fashion to analyze texts as time-varying complex networks.
| 2,015 | Computation and Language |
Chasing the Ghosts of Ibsen: A computational stylistic analysis of drama
in translation | Research into the stylistic properties of translations is an issue which has
received some attention in computational stylistics. Previous work by Rybicki
(2006) on the distinguishing of character idiolects in the work of Polish
author Henryk Sienkiewicz and two corresponding English translations using
Burrow's Delta method concluded that idiolectal differences could be observed
in the source texts and this variation was preserved to a large degree in both
translations. This study also found that the two translations were also highly
distinguishable from one another. Burrows (2002) examined English translations
of Juvenal also using the Delta method, results of this work suggest that some
translators are more adept at concealing their own style when translating the
works of another author whereas other authors tend to imprint their own style
to a greater extent on the work they translate. Our work examines the writing
of a single author, Norwegian playwright Henrik Ibsen, and these writings
translated into both German and English from Norwegian, in an attempt to
investigate the preservation of characterization, defined here as the
distinctiveness of textual contributions of characters.
| 2,009 | Computation and Language |
Un r\'esumeur \`a base de graphes, ind\'ep\'endant de la langue | In this paper we present REG, a graph-based approach for study a fundamental
problem of Natural Language Processing (NLP): the automatic text summarization.
The algorithm maps a document as a graph, then it computes the weight of their
sentences. We have applied this approach to summarize documents in three
languages.
| 2,015 | Computation and Language |
Unknown Words Analysis in POS tagging of Sinhala Language | Part of Speech (POS) is a very vital topic in Natural Language Processing
(NLP) task in any language, which involves analysing the construction of the
language, behaviours and the dynamics of the language, the knowledge that could
be utilized in computational linguistics analysis and automation applications.
In this context, dealing with unknown words (words do not appear in the lexicon
referred as unknown words) is also an important task, since growing NLP systems
are used in more and more new applications. One aid of predicting lexical
categories of unknown words is the use of syntactical knowledge of the
language. The distinction between open class words and closed class words
together with syntactical features of the language used in this research to
predict lexical categories of unknown words in the tagging process. An
experiment is performed to investigate the ability of the approach to parse
unknown words using syntactical knowledge without human intervention. This
experiment shows that the performance of the tagging process is enhanced when
word class distinction is used together with syntactic rules to parse sentences
containing unknown words in Sinhala language.
| 2,015 | Computation and Language |
Roman Urdu Opinion Mining System (RUOMiS) | Convincing a customer is always considered as a challenging task in every
business. But when it comes to online business, this task becomes even more
difficult. Online retailers try everything possible to gain the trust of the
customer. One of the solutions is to provide an area for existing users to
leave their comments. This service can effectively develop the trust of the
customer however normally the customer comments about the product in their
native language using Roman script. If there are hundreds of comments this
makes difficulty even for the native customers to make a buying decision. This
research proposes a system which extracts the comments posted in Roman Urdu,
translate them, find their polarity and then gives us the rating of the
product. This rating will help the native and non-native customers to make
buying decision efficiently from the comments posted in Roman Urdu.
| 2,015 | Computation and Language |
The Hebrew Bible as Data: Laboratory - Sharing - Experiences | The systematic study of ancient texts including their production,
transmission and interpretation is greatly aided by the digital methods that
started taking off in the 1970s. But how is that research in turn transmitted
to new generations of researchers? We tell a story of Bible and computer across
the decades and then point out the current challenges: (1) finding a stable
data representation for changing methods of computation; (2) sharing results in
inter- and intra-disciplinary ways, for reproducibility and
cross-fertilization. We report recent developments in meeting these challenges.
The scene is the text database of the Hebrew Bible, constructed by the Eep
Talstra Centre for Bible and Computer (ETCBC), which is still growing in detail
and sophistication. We show how a subtle mix of computational ingredients
enable scholars to research the transmission and interpretation of the Hebrew
Bible in new ways: (1) a standard data format, Linguistic Annotation Framework
(LAF); (2) the methods of scientific computing, made accessible by
(interactive) Python and its associated ecosystem. Additionally, we show how
these efforts have culminated in the construction of a new, publicly accessible
search engine SHEBANQ, where the text of the Hebrew Bible and its underlying
data can be queried in a simple, yet powerful query language MQL, and where
those queries can be saved and shared.
| 2,015 | Computation and Language |
Quantifying Scripts: Defining metrics of characters for quantitative and
descriptive analysis | Analysis of scripts plays an important role in paleography and in
quantitative linguistics. Especially in the field of digital paleography
quantitative features are much needed to differentiate glyphs. We describe an
elaborate set of metrics that quantify qualitative information contained in
characters and hence indirectly also quantify the scribal features. We broadly
divide the metrics into several categories and describe each individual metric
with its underlying qualitative significance. The metrics are largely derived
from the related area of gesture design and recognition. We also propose
several novel metrics. The proposed metrics are soundly grounded on the
principles of handwriting production and handwriting analysis. These computed
metrics could serve as descriptors for scripts and also be used for comparing
and analyzing scripts. We illustrate some quantitative analysis based on the
proposed metrics by applying it to the paleographic evolution of the medieval
Tamil script from Brahmi. We also outline future work.
| 2,015 | Computation and Language |
Autodetection and Classification of Hidden Cultural City Districts from
Yelp Reviews | Topic models are a way to discover underlying themes in an otherwise
unstructured collection of documents. In this study, we specifically used the
Latent Dirichlet Allocation (LDA) topic model on a dataset of Yelp reviews to
classify restaurants based off of their reviews. Furthermore, we hypothesize
that within a city, restaurants can be grouped into similar "clusters" based on
both location and similarity. We used several different clustering methods,
including K-means Clustering and a Probabilistic Mixture Model, in order to
uncover and classify districts, both well-known and hidden (i.e. cultural areas
like Chinatown or hearsay like "the best street for Italian restaurants")
within a city. We use these models to display and label different clusters on a
map. We also introduce a topic similarity heatmap that displays the similarity
distribution in a city to a new restaurant.
| 2,015 | Computation and Language |
Combining Language and Vision with a Multimodal Skip-gram Model | We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed to visual representations of the objects they denote
(extracted from natural images), and must predict linguistic and visual
features jointly. The MMSKIP-GRAM models achieve good performance on a variety
of semantic benchmarks. Moreover, since they propagate visual information to
all words, we use them to improve image labeling and retrieval in the zero-shot
setup, where the test concepts are never seen during model training. Finally,
the MMSKIP-GRAM models discover intriguing visual properties of abstract words,
paving the way to realistic implementations of embodied theories of meaning.
| 2,015 | Computation and Language |
Navigating the Semantic Horizon using Relative Neighborhood Graphs | This paper is concerned with nearest neighbor search in distributional
semantic models. A normal nearest neighbor search only returns a ranked list of
neighbors, with no information about the structure or topology of the local
neighborhood. This is a potentially serious shortcoming of the mode of querying
a distributional semantic model, since a ranked list of neighbors may conflate
several different senses. We argue that the topology of neighborhoods in
semantic space provides important information about the different senses of
terms, and that such topological structures can be used for word-sense
induction. We also argue that the topology of the neighborhoods in semantic
space can be used to determine the semantic horizon of a point, which we define
as the set of neighbors that have a direct connection to the point. We
introduce relative neighborhood graphs as method to uncover the topological
properties of neighborhoods in semantic models. We also provide examples of
relative neighborhood graphs for three well-known semantic models; the PMI
model, the GloVe model, and the skipgram model.
| 2,015 | Computation and Language |
From Visual Attributes to Adjectives through Decompositional
Distributional Semantics | As automated image analysis progresses, there is increasing interest in
richer linguistic annotation of pictures, with attributes of objects (e.g.,
furry, brown...) attracting most attention. By building on the recent
"zero-shot learning" approach, and paying attention to the linguistic nature of
attributes as noun modifiers, and specifically adjectives, we show that it is
possible to tag images with attribute-denoting adjectives even when no training
data containing the relevant annotation are available. Our approach relies on
two key observations. First, objects can be seen as bundles of attributes,
typically expressed as adjectival modifiers (a dog is something furry, brown,
etc.), and thus a function trained to map visual representations of objects to
nominal labels can implicitly learn to map attributes to adjectives. Second,
objects and attributes come together in pictures (the same thing is a dog and
it is brown). We can thus achieve better attribute (and object) label retrieval
by treating images as "visual phrases", and decomposing their linguistic
representation into an attribute-denoting adjective and an object-denoting
noun. Our approach performs comparably to a method exploiting manual attribute
annotation, it outperforms various competitive alternatives in both attribute
and object annotation, and it automatically constructs attribute-centric
representations that significantly improve performance in supervised object
recognition.
| 2,015 | Computation and Language |
Annotating Cognates and Etymological Origin in Turkic Languages | Turkic languages exhibit extensive and diverse etymological relationships
among lexical items. These relationships make the Turkic languages promising
for exploring automated translation lexicon induction by leveraging cognate and
other etymological information. However, due to the extent and diversity of the
types of relationships between words, it is not clear how to annotate such
information. In this paper, we present a methodology for annotating cognates
and etymological origin in Turkic languages. Our method strives to balance the
amount of research effort the annotator expends with the utility of the
annotations for supporting research on improving automated translation lexicon
induction.
| 2,012 | Computation and Language |
Phrase Based Language Model For Statistical Machine Translation | We consider phrase based Language Models (LM), which generalize the commonly
used word level models. Similar concept on phrase based LMs appears in speech
recognition, which is rather specialized and thus less suitable for machine
translation (MT). In contrast to the dependency LM, we first introduce the
exhaustive phrase-based LMs tailored for MT use. Preliminary experimental
results show that our approach outperform word based LMs with the respect to
perplexity and translation quality.
| 2,015 | Computation and Language |
Deep Belief Nets for Topic Modeling | Applying traditional collaborative filtering to digital publishing is
challenging because user data is very sparse due to the high volume of
documents relative to the number of users. Content based approaches, on the
other hand, is attractive because textual content is often very informative. In
this paper we describe large-scale content based collaborative filtering for
digital publishing. To solve the digital publishing recommender problem we
compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets
(DBN) that both find low-dimensional latent representations for documents.
Efficient retrieval can be carried out in the latent representation. We work
both on public benchmarks and digital media content provided by Issuu, an
online publishing platform. This article also comes with a newly developed deep
belief nets toolbox for topic modeling tailored towards performance evaluation
of the DBN model and comparisons to the LDA model.
| 2,015 | Computation and Language |
Phrase Based Language Model for Statistical Machine Translation:
Empirical Study | Reordering is a challenge to machine translation (MT) systems. In MT, the
widely used approach is to apply word based language model (LM) which considers
the constituent units of a sentence as words. In speech recognition (SR), some
phrase based LM have been proposed. However, those LMs are not necessarily
suitable or optimal for reordering. We propose two phrase based LMs which
considers the constituent units of a sentence as phrases. Experiments show that
our phrase based LMs outperform the word based LM with the respect of
perplexity and n-best list re-ranking.
| 2,015 | Computation and Language |
Deep Multimodal Learning for Audio-Visual Speech Recognition | In this paper, we present methods in deep multimodal learning for fusing
speech and visual modalities for Audio-Visual Automatic Speech Recognition
(AV-ASR). First, we study an approach where uni-modal deep networks are trained
separately and their final hidden layers fused to obtain a joint feature space
in which another deep network is built. While the audio network alone achieves
a phone error rate (PER) of $41\%$ under clean condition on the IBM large
vocabulary audio-visual studio dataset, this fusion model achieves a PER of
$35.83\%$ demonstrating the tremendous value of the visual channel in phone
classification even in audio with high signal to noise ratio. Second, we
present a new deep network architecture that uses a bilinear softmax layer to
account for class specific correlations between modalities. We show that
combining the posteriors from the bilinear networks with those from the fused
model mentioned above results in a further significant phone error rate
reduction, yielding a final PER of $34.03\%$.
| 2,015 | Computation and Language |
Survey:Natural Language Parsing For Indian Languages | Syntactic parsing is a necessary task which is required for NLP applications
including machine translation. It is a challenging task to develop a
qualitative parser for morphological rich and agglutinative languages.
Syntactic analysis is used to understand the grammatical structure of a natural
language sentence. It outputs all the grammatical information of each word and
its constituent. Also issues related to it help us to understand the language
in a more detailed way. This literature survey is groundwork to understand the
different parser development for Indian languages and various approaches that
are used to develop such tools and techniques. This paper provides a survey of
research papers from well known journals and conferences.
| 2,015 | Computation and Language |
Scaling Recurrent Neural Network Language Models | This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more costly to train, RNNLMs obtain much lower perplexities on
standard benchmarks than n-gram models. We train the largest known RNNs and
present relative word error rates gains of 18% on an ASR task. We also present
the new lowest perplexities on the recently released billion word language
modelling benchmark, 1 BLEU point gain on machine translation and a 17%
relative hit rate gain in word prediction.
| 2,015 | Computation and Language |
Open System Categorical Quantum Semantics in Natural Language Processing | Originally inspired by categorical quantum mechanics (Abramsky and Coecke,
LiCS'04), the categorical compositional distributional model of natural
language meaning of Coecke, Sadrzadeh and Clark provides a conceptually
motivated procedure to compute the meaning of a sentence, given its grammatical
structure within a Lambek pregroup and a vectorial representation of the
meaning of its parts. The predictions of this first model have outperformed
that of other models in mainstream empirical language processing tasks on large
scale data. Moreover, just like CQM allows for varying the model in which we
interpret quantum axioms, one can also vary the model in which we interpret
word meaning.
In this paper we show that further developments in categorical quantum
mechanics are relevant to natural language processing too. Firstly, Selinger's
CPM-construction allows for explicitly taking into account lexical ambiguity
and distinguishing between the two inherently different notions of homonymy and
polysemy. In terms of the model in which we interpret word meaning, this means
a passage from the vector space model to density matrices. Despite this change
of model, standard empirical methods for comparing meanings can be easily
adopted, which we demonstrate by a small-scale experiment on real-world data.
This experiment moreover provides preliminary evidence of the validity of our
proposed new model for word meaning.
Secondly, commutative classical structures as well as their non-commutative
counterparts that arise in the image of the CPM-construction allow for encoding
relative pronouns, verbs and adjectives, and finally, iteration of the
CPM-construction, something that has no counterpart in the quantum realm,
enables one to accommodate both entailment and ambiguity.
| 2,015 | Computation and Language |
Authorship recognition via fluctuation analysis of network topology and
word intermittency | Statistical methods have been widely employed in many practical natural
language processing applications. More specifically, complex networks concepts
and methods from dynamical systems theory have been successfully applied to
recognize stylistic patterns in written texts. Despite the large amount of
studies devoted to represent texts with physical models, only a few studies
have assessed the relevance of attributes derived from the analysis of
stylistic fluctuations. Because fluctuations represent a pivotal factor for
characterizing a myriad of real systems, this study focused on the analysis of
the properties of stylistic fluctuations in texts via topological analysis of
complex networks and intermittency measurements. The results showed that
different authors display distinct fluctuation patterns. In particular, it was
found that it is possible to identify the authorship of books using the
intermittency of specific words. Taken together, the results described here
suggest that the patterns found in stylistic fluctuations could be used to
analyze other related complex systems. Furthermore, the discovery of novel
patterns related to textual stylistic fluctuations indicates that these
patterns could be useful to improve the state of the art of many
stylistic-based natural language processing tasks.
| 2,015 | Computation and Language |
INRIASAC: Simple Hypernym Extraction Methods | Given a set of terms from a given domain, how can we structure them into a
taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here
we present our simple taxonomy structuring techniques which, despite their
simplicity, ranked first in this 2015 benchmark. We use large quantities of
text (English Wikipedia) and simple heuristics such as term overlap and
document and sentence co-occurrence to produce hypernym lists. We describe
these techniques and pre-sent an initial evaluation of results.
| 2,016 | Computation and Language |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.