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Hrebs and Cohesion Chains as similar tools for semantic text properties
research | In this study it is proven that the Hrebs used in Denotation analysis of
texts and Cohesion Chains (defined as a fusion between Lexical Chains and
Coreference Chains) represent similar linguistic tools. This result gives us
the possibility to extend to Cohesion Chains (CCs) some important indicators
as, for example the Kernel of CCs, the topicality of a CC, text concentration,
CC-diffuseness and mean diffuseness of the text. Let us mention that nowhere in
the Lexical Chains or Coreference Chains literature these kinds of indicators
are introduced and used since now. Similarly, some applications of CCs in the
study of a text (as for example segmentation or summarization of a text) could
be realized starting from hrebs. As an illustration of the similarity between
Hrebs and CCs a detailed analyze of the poem "Lacul" by Mihai Eminescu is
given.
| 2,016 | Computation and Language |
Constructing Reference Sets from Unstructured, Ungrammatical Text | Vast amounts of text on the Web are unstructured and ungrammatical, such as
classified ads, auction listings, forum postings, etc. We call such text
"posts." Despite their inconsistent structure and lack of grammar, posts are
full of useful information. This paper presents work on semi-automatically
building tables of relational information, called "reference sets," by
analyzing such posts directly. Reference sets can be applied to a number of
tasks such as ontology maintenance and information extraction. Our
reference-set construction method starts with just a small amount of background
knowledge, and constructs tuples representing the entities in the posts to form
a reference set. We also describe an extension to this approach for the special
case where even this small amount of background knowledge is impossible to
discover and use. To evaluate the utility of the machine-constructed reference
sets, we compare them to manually constructed reference sets in the context of
reference-set-based information extraction. Our results show the reference sets
constructed by our method outperform manually constructed reference sets. We
also compare the reference-set-based extraction approach using the
machine-constructed reference set to supervised extraction approaches using
generic features. These results demonstrate that using machine-constructed
reference sets outperforms the supervised methods, even though the supervised
methods require training data.
| 2,010 | Computation and Language |
Evaluating Temporal Graphs Built from Texts via Transitive Reduction | Temporal information has been the focus of recent attention in information
extraction, leading to some standardization effort, in particular for the task
of relating events in a text. This task raises the problem of comparing two
annotations of a given text, because relations between events in a story are
intrinsically interdependent and cannot be evaluated separately. A proper
evaluation measure is also crucial in the context of a machine learning
approach to the problem. Finding a common comparison referent at the text level
is not obvious, and we argue here in favor of a shift from event-based measures
to measures on a unique textual object, a minimal underlying temporal graph, or
more formally the transitive reduction of the graph of relations between event
boundaries. We support it by an investigation of its properties on synthetic
data and on a well-know temporal corpus.
| 2,011 | Computation and Language |
Entropy analysis of word-length series of natural language texts:
Effects of text language and genre | We estimate the $n$-gram entropies of natural language texts in word-length
representation and find that these are sensitive to text language and genre. We
attribute this sensitivity to changes in the probability distribution of the
lengths of single words and emphasize the crucial role of the uniformity of
probabilities of having words with length between five and ten. Furthermore,
comparison with the entropies of shuffled data reveals the impact of word
length correlations on the estimated $n$-gram entropies.
| 2,012 | Computation and Language |
Cause Identification from Aviation Safety Incident Reports via Weakly
Supervised Semantic Lexicon Construction | The Aviation Safety Reporting System collects voluntarily submitted reports
on aviation safety incidents to facilitate research work aiming to reduce such
incidents. To effectively reduce these incidents, it is vital to accurately
identify why these incidents occurred. More precisely, given a set of possible
causes, or shaping factors, this task of cause identification involves
identifying all and only those shaping factors that are responsible for the
incidents described in a report. We investigate two approaches to cause
identification. Both approaches exploit information provided by a semantic
lexicon, which is automatically constructed via Thelen and Riloffs Basilisk
framework augmented with our linguistic and algorithmic modifications. The
first approach labels a report using a simple heuristic, which looks for the
words and phrases acquired during the semantic lexicon learning process in the
report. The second approach recasts cause identification as a text
classification problem, employing supervised and transductive text
classification algorithms to learn models from incident reports labeled with
shaping factors and using the models to label unseen reports. Our experiments
show that both the heuristic-based approach and the learning-based approach
(when given sufficient training data) outperform the baseline system
significantly.
| 2,010 | Computation and Language |
Does Syntactic Knowledge help English-Hindi SMT? | In this paper we explore various parameter settings of the state-of-art
Statistical Machine Translation system to improve the quality of the
translation for a `distant' language pair like English-Hindi. We proposed new
techniques for efficient reordering. A slight improvement over the baseline is
reported using these techniques. We also show that a simple pre-processing step
can improve the quality of the translation significantly.
| 2,014 | Computation and Language |
Compositional Operators in Distributional Semantics | This survey presents in some detail the main advances that have been recently
taking place in Computational Linguistics towards the unification of the two
prominent semantic paradigms: the compositional formal semantics view and the
distributional models of meaning based on vector spaces. After an introduction
to these two approaches, I review the most important models that aim to provide
compositionality in distributional semantics. Then I proceed and present in
more detail a particular framework by Coecke, Sadrzadeh and Clark (2010) based
on the abstract mathematical setting of category theory, as a more complete
example capable to demonstrate the diversity of techniques and scientific
disciplines that this kind of research can draw from. This paper concludes with
a discussion about important open issues that need to be addressed by the
researchers in the future.
| 2,014 | Computation and Language |
Learning to Win by Reading Manuals in a Monte-Carlo Framework | Domain knowledge is crucial for effective performance in autonomous control
systems. Typically, human effort is required to encode this knowledge into a
control algorithm. In this paper, we present an approach to language grounding
which automatically interprets text in the context of a complex control
application, such as a game, and uses domain knowledge extracted from the text
to improve control performance. Both text analysis and control strategies are
learned jointly using only a feedback signal inherent to the application. To
effectively leverage textual information, our method automatically extracts the
text segment most relevant to the current game state, and labels it with a
task-centric predicate structure. This labeled text is then used to bias an
action selection policy for the game, guiding it towards promising regions of
the action space. We encode our model for text analysis and game playing in a
multi-layer neural network, representing linguistic decisions via latent
variables in the hidden layers, and game action quality via the output layer.
Operating within the Monte-Carlo Search framework, we estimate model parameters
using feedback from simulated games. We apply our approach to the complex
strategy game Civilization II using the official game manual as the text guide.
Our results show that a linguistically-informed game-playing agent
significantly outperforms its language-unaware counterpart, yielding a 34%
absolute improvement and winning over 65% of games when playing against the
built-in AI of Civilization.
| 2,012 | Computation and Language |
A new keyphrases extraction method based on suffix tree data structure
for arabic documents clustering | Document Clustering is a branch of a larger area of scientific study known as
data mining .which is an unsupervised classification using to find a structure
in a collection of unlabeled data. The useful information in the documents can
be accompanied by a large amount of noise words when using Full Text
Representation, and therefore will affect negatively the result of the
clustering process. So it is with great need to eliminate the noise words and
keeping just the useful information in order to enhance the quality of the
clustering results. This problem occurs with different degree for any language
such as English, European, Hindi, Chinese, and Arabic Language. To overcome
this problem, in this paper, we propose a new and efficient Keyphrases
extraction method based on the Suffix Tree data structure (KpST), the extracted
Keyphrases are then used in the clustering process instead of Full Text
Representation. The proposed method for Keyphrases extraction is language
independent and therefore it may be applied to any language. In this
investigation, we are interested to deal with the Arabic language which is one
of the most complex languages. To evaluate our method, we conduct an
experimental study on Arabic Documents using the most popular Clustering
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with
seven linkage techniques and a variety of distance functions and similarity
measures to perform Arabic Document Clustering task. The obtained results show
that our method for extracting Keyphrases increases the quality of the
clustering results. We propose also to study the effect of using the stemming
for the testing dataset to cluster it with the same documents clustering
techniques and similarity/distance measures.
| 2,013 | Computation and Language |
Generalized Biwords for Bitext Compression and Translation Spotting | Large bilingual parallel texts (also known as bitexts) are usually stored in
a compressed form, and previous work has shown that they can be more
efficiently compressed if the fact that the two texts are mutual translations
is exploited. For example, a bitext can be seen as a sequence of biwords
---pairs of parallel words with a high probability of co-occurrence--- that can
be used as an intermediate representation in the compression process. However,
the simple biword approach described in the literature can only exploit
one-to-one word alignments and cannot tackle the reordering of words. We
therefore introduce a generalization of biwords which can describe multi-word
expressions and reorderings. We also describe some methods for the binary
compression of generalized biword sequences, and compare their performance when
different schemes are applied to the extraction of the biword sequence. In
addition, we show that this generalization of biwords allows for the
implementation of an efficient algorithm to look on the compressed bitext for
words or text segments in one of the texts and retrieve their counterpart
translations in the other text ---an application usually referred to as
translation spotting--- with only some minor modifications in the compression
algorithm.
| 2,012 | Computation and Language |
Sentence Compression as Tree Transduction | This paper presents a tree-to-tree transduction method for sentence
compression. Our model is based on synchronous tree substitution grammar, a
formalism that allows local distortion of the tree topology and can thus
naturally capture structural mismatches. We describe an algorithm for decoding
in this framework and show how the model can be trained discriminatively within
a large margin framework. Experimental results on sentence compression bring
significant improvements over a state-of-the-art model.
| 2,009 | Computation and Language |
Cross-lingual Annotation Projection for Semantic Roles | This article considers the task of automatically inducing role-semantic
annotations in the FrameNet paradigm for new languages. We propose a general
framework that is based on annotation projection, phrased as a graph
optimization problem. It is relatively inexpensive and has the potential to
reduce the human effort involved in creating role-semantic resources. Within
this framework, we present projection models that exploit lexical and syntactic
information. We provide an experimental evaluation on an English-German
parallel corpus which demonstrates the feasibility of inducing high-precision
German semantic role annotation both for manually and automatically annotated
English data.
| 2,009 | Computation and Language |
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches | We demonstrate the effectiveness of multilingual learning for unsupervised
part-of-speech tagging. The central assumption of our work is that by combining
cues from multiple languages, the structure of each becomes more apparent. We
consider two ways of applying this intuition to the problem of unsupervised
part-of-speech tagging: a model that directly merges tag structures for a pair
of languages into a single sequence and a second model which instead
incorporates multilingual context using latent variables. Both approaches are
formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo
sampling techniques for inference. Our results demonstrate that by
incorporating multilingual evidence we can achieve impressive performance gains
across a range of scenarios. We also found that performance improves steadily
as the number of available languages increases.
| 2,009 | Computation and Language |
Unsupervised Methods for Determining Object and Relation Synonyms on the
Web | The task of identifying synonymous relations and objects, or synonym
resolution, is critical for high-quality information extraction. This paper
investigates synonym resolution in the context of unsupervised information
extraction, where neither hand-tagged training examples nor domain knowledge is
available. The paper presents a scalable, fully-implemented system that runs in
O(KN log N) time in the number of extractions, N, and the maximum number of
synonyms per word, K. The system, called Resolver, introduces a probabilistic
relational model for predicting whether two strings are co-referential based on
the similarity of the assertions containing them. On a set of two million
assertions extracted from the Web, Resolver resolves objects with 78% precision
and 68% recall, and resolves relations with 90% precision and 35% recall.
Several variations of resolvers probabilistic model are explored, and
experiments demonstrate that under appropriate conditions these variations can
improve F1 by 5%. An extension to the basic Resolver system allows it to handle
polysemous names with 97% precision and 95% recall on a data set from the TREC
corpus.
| 2,009 | Computation and Language |
Wikipedia-based Semantic Interpretation for Natural Language Processing | Adequate representation of natural language semantics requires access to vast
amounts of common sense and domain-specific world knowledge. Prior work in the
field was based on purely statistical techniques that did not make use of
background knowledge, on limited lexicographic knowledge bases such as WordNet,
or on huge manual efforts such as the CYC project. Here we propose a novel
method, called Explicit Semantic Analysis (ESA), for fine-grained semantic
interpretation of unrestricted natural language texts. Our method represents
meaning in a high-dimensional space of concepts derived from Wikipedia, the
largest encyclopedia in existence. We explicitly represent the meaning of any
text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our
method on text categorization and on computing the degree of semantic
relatedness between fragments of natural language text. Using ESA results in
significant improvements over the previous state of the art in both tasks.
Importantly, due to the use of natural concepts, the ESA model is easy to
explain to human users.
| 2,009 | Computation and Language |
Identification of Pleonastic It Using the Web | In a significant minority of cases, certain pronouns, especially the pronoun
it, can be used without referring to any specific entity. This phenomenon of
pleonastic pronoun usage poses serious problems for systems aiming at even a
shallow understanding of natural language texts. In this paper, a novel
approach is proposed to identify such uses of it: the extrapositional cases are
identified using a series of queries against the web, and the cleft cases are
identified using a simple set of syntactic rules. The system is evaluated with
four sets of news articles containing 679 extrapositional cases as well as 78
cleft constructs. The identification results are comparable to those obtained
by human efforts.
| 2,009 | Computation and Language |
Text Relatedness Based on a Word Thesaurus | The computation of relatedness between two fragments of text in an automated
manner requires taking into account a wide range of factors pertaining to the
meaning the two fragments convey, and the pairwise relations between their
words. Without doubt, a measure of relatedness between text segments must take
into account both the lexical and the semantic relatedness between words. Such
a measure that captures well both aspects of text relatedness may help in many
tasks, such as text retrieval, classification and clustering. In this paper we
present a new approach for measuring the semantic relatedness between words
based on their implicit semantic links. The approach exploits only a word
thesaurus in order to devise implicit semantic links between words. Based on
this approach, we introduce Omiotis, a new measure of semantic relatedness
between texts which capitalizes on the word-to-word semantic relatedness
measure (SR) and extends it to measure the relatedness between texts. We
gradually validate our method: we first evaluate the performance of the
semantic relatedness measure between individual words, covering word-to-word
similarity and relatedness, synonym identification and word analogy; then, we
proceed with evaluating the performance of our method in measuring text-to-text
semantic relatedness in two tasks, namely sentence-to-sentence similarity and
paraphrase recognition. Experimental evaluation shows that the proposed method
outperforms every lexicon-based method of semantic relatedness in the selected
tasks and the used data sets, and competes well against corpus-based and hybrid
approaches.
| 2,010 | Computation and Language |
Inferring Shallow-Transfer Machine Translation Rules from Small Parallel
Corpora | This paper describes a method for the automatic inference of structural
transfer rules to be used in a shallow-transfer machine translation (MT) system
from small parallel corpora. The structural transfer rules are based on
alignment templates, like those used in statistical MT. Alignment templates are
extracted from sentence-aligned parallel corpora and extended with a set of
restrictions which are derived from the bilingual dictionary of the MT system
and control their application as transfer rules. The experiments conducted
using three different language pairs in the free/open-source MT platform
Apertium show that translation quality is improved as compared to word-for-word
translation (when no transfer rules are used), and that the resulting
translation quality is close to that obtained using hand-coded transfer rules.
The method we present is entirely unsupervised and benefits from information in
the rest of modules of the MT system in which the inferred rules are applied.
| 2,009 | Computation and Language |
Reasoning about Meaning in Natural Language with Compact Closed
Categories and Frobenius Algebras | Compact closed categories have found applications in modeling quantum
information protocols by Abramsky-Coecke. They also provide semantics for
Lambek's pregroup algebras, applied to formalizing the grammatical structure of
natural language, and are implicit in a distributional model of word meaning
based on vector spaces. Specifically, in previous work Coecke-Clark-Sadrzadeh
used the product category of pregroups with vector spaces and provided a
distributional model of meaning for sentences. We recast this theory in terms
of strongly monoidal functors and advance it via Frobenius algebras over vector
spaces. The former are used to formalize topological quantum field theories by
Atiyah and Baez-Dolan, and the latter are used to model classical data in
quantum protocols by Coecke-Pavlovic-Vicary. The Frobenius algebras enable us
to work in a single space in which meanings of words, phrases, and sentences of
any structure live. Hence we can compare meanings of different language
constructs and enhance the applicability of the theory. We report on
experimental results on a number of language tasks and verify the theoretical
predictions.
| 2,014 | Computation and Language |
Integrative Semantic Dependency Parsing via Efficient Large-scale
Feature Selection | Semantic parsing, i.e., the automatic derivation of meaning representation
such as an instantiated predicate-argument structure for a sentence, plays a
critical role in deep processing of natural language. Unlike all other top
systems of semantic dependency parsing that have to rely on a pipeline
framework to chain up a series of submodels each specialized for a specific
subtask, the one presented in this article integrates everything into one
model, in hopes of achieving desirable integrity and practicality for real
applications while maintaining a competitive performance. This integrative
approach tackles semantic parsing as a word pair classification problem using a
maximum entropy classifier. We leverage adaptive pruning of argument candidates
and large-scale feature selection engineering to allow the largest feature
space ever in use so far in this field, it achieves a state-of-the-art
performance on the evaluation data set for CoNLL-2008 shared task, on top of
all but one top pipeline system, confirming its feasibility and effectiveness.
| 2,013 | Computation and Language |
Identifying Bengali Multiword Expressions using Semantic Clustering | One of the key issues in both natural language understanding and generation
is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge
problem to the precise language processing due to their idiosyncratic nature
and diversity in lexical, syntactical and semantic properties. The semantics of
a MWE cannot be expressed after combining the semantics of its constituents.
Therefore, the formalism of semantic clustering is often viewed as an
instrument for extracting MWEs especially for resource constraint languages
like Bengali. The present semantic clustering approach contributes to locate
clusters of the synonymous noun tokens present in the document. These clusters
in turn help measure the similarity between the constituent words of a
potentially candidate phrase using a vector space model and judge the
suitability of this phrase to be a MWE. In this experiment, we apply the
semantic clustering approach for noun-noun bigram MWEs, though it can be
extended to any types of MWEs. In parallel, the well known statistical models,
namely Point-wise Mutual Information (PMI), Log Likelihood Ratio (LLR),
Significance function are also employed to extract MWEs from the Bengali
corpus. The comparative evaluation shows that the semantic clustering approach
outperforms all other competing statistical models. As a by-product of this
experiment, we have started developing a standard lexicon in Bengali that
serves as a productive Bengali linguistic thesaurus.
| 2,014 | Computation and Language |
Controlling Complexity in Part-of-Speech Induction | We consider the problem of fully unsupervised learning of grammatical
(part-of-speech) categories from unlabeled text. The standard
maximum-likelihood hidden Markov model for this task performs poorly, because
of its weak inductive bias and large model capacity. We address this problem by
refining the model and modifying the learning objective to control its capacity
via para- metric and non-parametric constraints. Our approach enforces
word-category association sparsity, adds morphological and orthographic
features, and eliminates hard-to-estimate parameters for rare words. We develop
an efficient learning algorithm that is not much more computationally intensive
than standard training. We also provide an open-source implementation of the
algorithm. Our experiments on five diverse languages (Bulgarian, Danish,
English, Portuguese, Spanish) achieve significant improvements compared with
previous methods for the same task.
| 2,011 | Computation and Language |
Word-length entropies and correlations of natural language written texts | We study the frequency distributions and correlations of the word lengths of
ten European languages. Our findings indicate that a) the word-length
distribution of short words quantified by the mean value and the entropy
distinguishes the Uralic (Finnish) corpus from the others, b) the tails at long
words, manifested in the high-order moments of the distributions, differentiate
the Germanic languages (except for English) from the Romanic languages and
Greek and c) the correlations between nearby word lengths measured by the
comparison of the real entropies with those of the shuffled texts are found to
be smaller in the case of Germanic and Finnish languages.
| 2,014 | Computation and Language |
A Statistical Parsing Framework for Sentiment Classification | We present a statistical parsing framework for sentence-level sentiment
classification in this article. Unlike previous works that employ syntactic
parsing results for sentiment analysis, we develop a statistical parser to
directly analyze the sentiment structure of a sentence. We show that
complicated phenomena in sentiment analysis (e.g., negation, intensification,
and contrast) can be handled the same as simple and straightforward sentiment
expressions in a unified and probabilistic way. We formulate the sentiment
grammar upon Context-Free Grammars (CFGs), and provide a formal description of
the sentiment parsing framework. We develop the parsing model to obtain
possible sentiment parse trees for a sentence, from which the polarity model is
proposed to derive the sentiment strength and polarity, and the ranking model
is dedicated to selecting the best sentiment tree. We train the parser directly
from examples of sentences annotated only with sentiment polarity labels but
without any syntactic annotations or polarity annotations of constituents
within sentences. Therefore we can obtain training data easily. In particular,
we train a sentiment parser, s.parser, from a large amount of review sentences
with users' ratings as rough sentiment polarity labels. Extensive experiments
on existing benchmark datasets show significant improvements over baseline
sentiment classification approaches.
| 2,015 | Computation and Language |
Automatic Aggregation by Joint Modeling of Aspects and Values | We present a model for aggregation of product review snippets by joint aspect
identification and sentiment analysis. Our model simultaneously identifies an
underlying set of ratable aspects presented in the reviews of a product (e.g.,
sushi and miso for a Japanese restaurant) and determines the corresponding
sentiment of each aspect. This approach directly enables discovery of
highly-rated or inconsistent aspects of a product. Our generative model admits
an efficient variational mean-field inference algorithm. It is also easily
extensible, and we describe several modifications and their effects on model
structure and inference. We test our model on two tasks, joint aspect
identification and sentiment analysis on a set of Yelp reviews and aspect
identification alone on a set of medical summaries. We evaluate the performance
of the model on aspect identification, sentiment analysis, and per-word
labeling accuracy. We demonstrate that our model outperforms applicable
baselines by a considerable margin, yielding up to 32% relative error reduction
on aspect identification and up to 20% relative error reduction on sentiment
analysis.
| 2,013 | Computation and Language |
A Machine Learning Approach for the Identification of Bengali Noun-Noun
Compound Multiword Expressions | This paper presents a machine learning approach for identification of Bengali
multiword expressions (MWE) which are bigram nominal compounds. Our proposed
approach has two steps: (1) candidate extraction using chunk information and
various heuristic rules and (2) training the machine learning algorithm called
Random Forest to classify the candidates into two groups: bigram nominal
compound MWE or not bigram nominal compound MWE. A variety of association
measures, syntactic and linguistic clues and a set of WordNet-based similarity
features have been used for our MWE identification task. The approach presented
in this paper can be used to identify bigram nominal compound MWE in Bengali
running text.
| 2,013 | Computation and Language |
Keyword and Keyphrase Extraction Using Centrality Measures on
Collocation Networks | Keyword and keyphrase extraction is an important problem in natural language
processing, with applications ranging from summarization to semantic search to
document clustering. Graph-based approaches to keyword and keyphrase extraction
avoid the problem of acquiring a large in-domain training corpus by applying
variants of PageRank algorithm on a network of words. Although graph-based
approaches are knowledge-lean and easily adoptable in online systems, it
remains largely open whether they can benefit from centrality measures other
than PageRank. In this paper, we experiment with an array of centrality
measures on word and noun phrase collocation networks, and analyze their
performance on four benchmark datasets. Not only are there centrality measures
that perform as well as or better than PageRank, but they are much simpler
(e.g., degree, strength, and neighborhood size). Furthermore, centrality-based
methods give results that are competitive with and, in some cases, better than
two strong unsupervised baselines.
| 2,014 | Computation and Language |
Deverbal semantics and the Montagovian generative lexicon | We propose a lexical account of action nominals, in particular of deverbal
nominalisations, whose meaning is related to the event expressed by their base
verb. The literature about nominalisations often assumes that the semantics of
the base verb completely defines the structure of action nominals. We argue
that the information in the base verb is not sufficient to completely determine
the semantics of action nominals. We exhibit some data from different
languages, especially from Romance language, which show that nominalisations
focus on some aspects of the verb semantics. The selected aspects, however,
seem to be idiosyncratic and do not automatically result from the internal
structure of the verb nor from its interaction with the morphological suffix.
We therefore propose a partially lexicalist approach view of deverbal nouns. It
is made precise and computable by using the Montagovian Generative Lexicon, a
type theoretical framework introduced by Bassac, Mery and Retor\'e in this
journal in 2010. This extension of Montague semantics with a richer type system
easily incorporates lexical phenomena like the semantics of action nominals in
particular deverbals, including their polysemy and (in)felicitous
copredications.
| 2,014 | Computation and Language |
Context-based Word Acquisition for Situated Dialogue in a Virtual World | To tackle the vocabulary problem in conversational systems, previous work has
applied unsupervised learning approaches on co-occurring speech and eye gaze
during interaction to automatically acquire new words. Although these
approaches have shown promise, several issues related to human language
behavior and human-machine conversation have not been addressed. First,
psycholinguistic studies have shown certain temporal regularities between human
eye movement and language production. While these regularities can potentially
guide the acquisition process, they have not been incorporated in the previous
unsupervised approaches. Second, conversational systems generally have an
existing knowledge base about the domain and vocabulary. While the existing
knowledge can potentially help bootstrap and constrain the acquired new words,
it has not been incorporated in the previous models. Third, eye gaze could
serve different functions in human-machine conversation. Some gaze streams may
not be closely coupled with speech stream, and thus are potentially detrimental
to word acquisition. Automated recognition of closely-coupled speech-gaze
streams based on conversation context is important. To address these issues, we
developed new approaches that incorporate user language behavior, domain
knowledge, and conversation context in word acquisition. We evaluated these
approaches in the context of situated dialogue in a virtual world. Our
experimental results have shown that incorporating the above three types of
contextual information significantly improves word acquisition performance.
| 2,010 | Computation and Language |
Improving Statistical Machine Translation for a Resource-Poor Language
Using Related Resource-Rich Languages | We propose a novel language-independent approach for improving machine
translation for resource-poor languages by exploiting their similarity to
resource-rich ones. More precisely, we improve the translation from a
resource-poor source language X_1 into a resource-rich language Y given a
bi-text containing a limited number of parallel sentences for X_1-Y and a
larger bi-text for X_2-Y for some resource-rich language X_2 that is closely
related to X_1. This is achieved by taking advantage of the opportunities that
vocabulary overlap and similarities between the languages X_1 and X_2 in
spelling, word order, and syntax offer: (1) we improve the word alignments for
the resource-poor language, (2) we further augment it with additional
translation options, and (3) we take care of potential spelling differences
through appropriate transliteration. The evaluation for Indonesian- >English
using Malay and for Spanish -> English using Portuguese and pretending Spanish
is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points,
respectively, which is an improvement over the best rivaling approaches, while
using much less additional data. Overall, our method cuts the amount of
necessary "real training data by a factor of 2--5.
| 2,012 | Computation and Language |
Quantifying literature quality using complexity criteria | We measured entropy and symbolic diversity for English and Spanish texts
including literature Nobel laureates and other famous authors. Entropy, symbol
diversity and symbol frequency profiles were compared for these four groups. We
also built a scale sensitive to the quality of writing and evaluated its
relationship with the Flesch's readability index for English and the
Szigriszt's perspicuity index for Spanish. Results suggest a correlation
between entropy and word diversity with quality of writing. Text genre also
influences the resulting entropy and diversity of the text. Results suggest the
plausibility of automated quality assessment of texts.
| 2,017 | Computation and Language |
Experiments with Three Approaches to Recognizing Lexical Entailment | Inference in natural language often involves recognizing lexical entailment
(RLE); that is, identifying whether one word entails another. For example,
"buy" entails "own". Two general strategies for RLE have been proposed: One
strategy is to manually construct an asymmetric similarity measure for context
vectors (directional similarity) and another is to treat RLE as a problem of
learning to recognize semantic relations using supervised machine learning
techniques (relation classification). In this paper, we experiment with two
recent state-of-the-art representatives of the two general strategies. The
first approach is an asymmetric similarity measure (an instance of the
directional similarity strategy), designed to capture the degree to which the
contexts of a word, a, form a subset of the contexts of another word, b. The
second approach (an instance of the relation classification strategy)
represents a word pair, a:b, with a feature vector that is the concatenation of
the context vectors of a and b, and then applies supervised learning to a
training set of labeled feature vectors. Additionally, we introduce a third
approach that is a new instance of the relation classification strategy. The
third approach represents a word pair, a:b, with a feature vector in which the
features are the differences in the similarities of a and b to a set of
reference words. All three approaches use vector space models (VSMs) of
semantics, based on word-context matrices. We perform an extensive evaluation
of the three approaches using three different datasets. The proposed new
approach (similarity differences) performs significantly better than the other
two approaches on some datasets and there is no dataset for which it is
significantly worse. Our results suggest it is beneficial to make connections
between the research in lexical entailment and the research in semantic
relation classification.
| 2,015 | Computation and Language |
How Does Latent Semantic Analysis Work? A Visualisation Approach | By using a small example, an analogy to photographic compression, and a
simple visualization using heatmaps, we show that latent semantic analysis
(LSA) is able to extract what appears to be semantic meaning of words from a
set of documents by blurring the distinctions between the words.
| 2,014 | Computation and Language |
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine
Translation | Although, Chinese and Spanish are two of the most spoken languages in the
world, not much research has been done in machine translation for this language
pair. This paper focuses on investigating the state-of-the-art of
Chinese-to-Spanish statistical machine translation (SMT), which nowadays is one
of the most popular approaches to machine translation. For this purpose, we
report details of the available parallel corpus which are Basic Traveller
Expressions Corpus (BTEC), Holy Bible and United Nations (UN). Additionally, we
conduct experimental work with the largest of these three corpora to explore
alternative SMT strategies by means of using a pivot language. Three
alternatives are considered for pivoting: cascading, pseudo-corpus and
triangulation. As pivot language, we use either English, Arabic or French.
Results show that, for a phrase-based SMT system, English is the best pivot
language between Chinese and Spanish. We propose a system output combination
using the pivot strategies which is capable of outperforming the direct
translation strategy. The main objective of this work is motivating and
involving the research community to work in this important pair of languages
given their demographic impact.
| 2,012 | Computation and Language |
Learning to Predict from Textual Data | Given a current news event, we tackle the problem of generating plausible
predictions of future events it might cause. We present a new methodology for
modeling and predicting such future news events using machine learning and data
mining techniques. Our Pundit algorithm generalizes examples of causality pairs
to infer a causality predictor. To obtain precisely labeled causality examples,
we mine 150 years of news articles and apply semantic natural language modeling
techniques to headlines containing certain predefined causality patterns. For
generalization, the model uses a vast number of world knowledge ontologies.
Empirical evaluation on real news articles shows that our Pundit algorithm
performs as well as non-expert humans.
| 2,012 | Computation and Language |
Natural Language Inference for Arabic Using Extended Tree Edit Distance
with Subtrees | Many natural language processing (NLP) applications require the computation
of similarities between pairs of syntactic or semantic trees. Many researchers
have used tree edit distance for this task, but this technique suffers from the
drawback that it deals with single node operations only. We have extended the
standard tree edit distance algorithm to deal with subtree transformation
operations as well as single nodes. The extended algorithm with subtree
operations, TED+ST, is more effective and flexible than the standard algorithm,
especially for applications that pay attention to relations among nodes (e.g.
in linguistic trees, deleting a modifier subtree should be cheaper than the sum
of deleting its components individually). We describe the use of TED+ST for
checking entailment between two Arabic text snippets. The preliminary results
of using TED+ST were encouraging when compared with two string-based approaches
and with the standard algorithm.
| 2,013 | Computation and Language |
Topic Segmentation and Labeling in Asynchronous Conversations | Topic segmentation and labeling is often considered a prerequisite for
higher-level conversation analysis and has been shown to be useful in many
Natural Language Processing (NLP) applications. We present two new corpora of
email and blog conversations annotated with topics, and evaluate annotator
reliability for the segmentation and labeling tasks in these asynchronous
conversations. We propose a complete computational framework for topic
segmentation and labeling in asynchronous conversations. Our approach extends
state-of-the-art methods by considering a fine-grained structure of an
asynchronous conversation, along with other conversational features by applying
recent graph-based methods for NLP. For topic segmentation, we propose two
novel unsupervised models that exploit the fine-grained conversational
structure, and a novel graph-theoretic supervised model that combines lexical,
conversational and topic features. For topic labeling, we propose two novel
(unsupervised) random walk models that respectively capture conversation
specific clues from two different sources: the leading sentences and the
fine-grained conversational structure. Empirical evaluation shows that the
segmentation and the labeling performed by our best models beat the
state-of-the-art, and are highly correlated with human annotations.
| 2,013 | Computation and Language |
An Autoencoder Approach to Learning Bilingual Word Representations | Cross-language learning allows us to use training data from one language to
build models for a different language. Many approaches to bilingual learning
require that we have word-level alignment of sentences from parallel corpora.
In this work we explore the use of autoencoder-based methods for cross-language
learning of vectorial word representations that are aligned between two
languages, while not relying on word-level alignments. We show that by simply
learning to reconstruct the bag-of-words representations of aligned sentences,
within and between languages, we can in fact learn high-quality representations
and do without word alignments. Since training autoencoders on word
observations presents certain computational issues, we propose and compare
different variations adapted to this setting. We also propose an explicit
correlation maximizing regularizer that leads to significant improvement in the
performance. We empirically investigate the success of our approach on the
problem of cross-language test classification, where a classifier trained on a
given language (e.g., English) must learn to generalize to a different language
(e.g., German). These experiments demonstrate that our approaches are
competitive with the state-of-the-art, achieving up to 10-14 percentage point
improvements over the best reported results on this task.
| 2,014 | Computation and Language |
The CQC Algorithm: Cycling in Graphs to Semantically Enrich and Enhance
a Bilingual Dictionary | Bilingual machine-readable dictionaries are knowledge resources useful in
many automatic tasks. However, compared to monolingual computational lexicons
like WordNet, bilingual dictionaries typically provide a lower amount of
structured information, such as lexical and semantic relations, and often do
not cover the entire range of possible translations for a word of interest. In
this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the
automated disambiguation of ambiguous translations in the lexical entries of a
bilingual machine-readable dictionary. The dictionary is represented as a
graph, and cyclic patterns are sought in the graph to assign an appropriate
sense tag to each translation in a lexical entry. Further, we use the
algorithms output to improve the quality of the dictionary itself, by
suggesting accurate solutions to structural problems such as misalignments,
partial alignments and missing entries. Finally, we successfully apply CQC to
the task of synonym extraction.
| 2,012 | Computation and Language |
PR2: A Language Independent Unsupervised Tool for Personality
Recognition from Text | We present PR2, a personality recognition system available online, that
performs instance-based classification of Big5 personality types from
unstructured text, using language-independent features. It has been tested on
English and Italian, achieving performances up to f=.68.
| 2,014 | Computation and Language |
Event Structure of Transitive Verb: A MARVS perspective | Module-Attribute Representation of Verbal Semantics (MARVS) is a theory of
the representation of verbal semantics that is based on Mandarin Chinese data
(Huang et al. 2000). In the MARVS theory, there are two different types of
modules: Event Structure Modules and Role Modules. There are also two sets of
attributes: Event-Internal Attributes and Role-Internal Attributes, which are
linked to the Event Structure Module and the Role Module, respectively. In this
study, we focus on four transitive verbs as chi1(eat), wan2(play),
huan4(change) and shao1(burn) and explore their event structures by the MARVS
theory.
| 2,012 | Computation and Language |
Software Requirement Specification Using Reverse Speech Technology | Speech analysis had been taken to a new level with the discovery of Reverse
Speech (RS). RS is the discovery of hidden messages, referred as reversals, in
normal speech. Works are in progress for exploiting the relevance of RS in
different real world applications such as investigation, medical field etc. In
this paper we represent an innovative method for preparing a reliable Software
Requirement Specification (SRS) document with the help of reverse speech. As
SRS act as the backbone for the successful completion of any project, a
reliable method is needed to overcome the inconsistencies. Using RS such a
reliable method for SRS documentation was developed.
| 2,014 | Computation and Language |
An evaluative baseline for geo-semantic relatedness and similarity | In geographic information science and semantics, the computation of semantic
similarity is widely recognised as key to supporting a vast number of tasks in
information integration and retrieval. By contrast, the role of geo-semantic
relatedness has been largely ignored. In natural language processing, semantic
relatedness is often confused with the more specific semantic similarity. In
this article, we discuss a notion of geo-semantic relatedness based on Lehrer's
semantic fields, and we compare it with geo-semantic similarity. We then
describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a
new open dataset designed to evaluate computational measures of geo-semantic
relatedness and similarity. This dataset is larger than existing datasets of
this kind, and includes 97 geographic terms combined into 50 term pairs rated
by 203 human subjects. GeReSiD is available online and can be used as an
evaluation baseline to determine empirically to what degree a given
computational model approximates geo-semantic relatedness and similarity.
| 2,014 | Computation and Language |
Machine Learning of Phonologically Conditioned Noun Declensions For
Tamil Morphological Generators | This paper presents machine learning solutions to a practical problem of
Natural Language Generation (NLG), particularly the word formation in
agglutinative languages like Tamil, in a supervised manner. The morphological
generator is an important component of Natural Language Processing in
Artificial Intelligence. It generates word forms given a root and affixes. The
morphophonemic changes like addition, deletion, alternation etc., occur when
two or more morphemes or words joined together. The Sandhi rules should be
explicitly specified in the rule based morphological analyzers and generators.
In machine learning framework, these rules can be learned automatically by the
system from the training samples and subsequently be applied for new inputs. In
this paper we proposed the machine learning models which learn the
morphophonemic rules for noun declensions from the given training data. These
models are trained to learn sandhi rules using various learning algorithms and
the performance of those algorithms are presented. From this we conclude that
machine learning of morphological processing such as word form generation can
be successfully learned in a supervised manner, without explicit description of
rules. The performance of Decision trees and Bayesian machine learning
algorithms on noun declensions are discussed.
| 2,014 | Computation and Language |
Authorship Analysis based on Data Compression | This paper proposes to perform authorship analysis using the Fast Compression
Distance (FCD), a similarity measure based on compression with dictionaries
directly extracted from the written texts. The FCD computes a similarity
between two documents through an effective binary search on the intersection
set between the two related dictionaries. In the reported experiments the
proposed method is applied to documents which are heterogeneous in style,
written in five different languages and coming from different historical
periods. Results are comparable to the state of the art and outperform
traditional compression-based methods.
| 2,014 | Computation and Language |
Auto Spell Suggestion for High Quality Speech Synthesis in Hindi | The goal of Text-to-Speech (TTS) synthesis in a particular language is to
convert arbitrary input text to intelligible and natural sounding speech.
However, for a particular language like Hindi, which is a highly confusing
language (due to very close spellings), it is not an easy task to identify
errors/mistakes in input text and an incorrect text degrade the quality of
output speech hence this paper is a contribution to the development of high
quality speech synthesis with the involvement of Spellchecker which generates
spell suggestions for misspelled words automatically. Involvement of
spellchecker would increase the efficiency of speech synthesis by providing
spell suggestions for incorrect input text. Furthermore, we have provided the
comparative study for evaluating the resultant effect on to phonetic text by
adding spellchecker on to input text.
| 2,014 | Computation and Language |
word2vec Explained: deriving Mikolov et al.'s negative-sampling
word-embedding method | The word2vec software of Tomas Mikolov and colleagues
(https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and
provides state-of-the-art word embeddings. The learning models behind the
software are described in two research papers. We found the description of the
models in these papers to be somewhat cryptic and hard to follow. While the
motivations and presentation may be obvious to the neural-networks
language-modeling crowd, we had to struggle quite a bit to figure out the
rationale behind the equations.
This note is an attempt to explain equation (4) (negative sampling) in
"Distributed Representations of Words and Phrases and their Compositionality"
by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.
| 2,014 | Computation and Language |
A Comparative Study of Machine Learning Methods for Verbal Autopsy Text
Classification | A Verbal Autopsy is the record of an interview about the circumstances of an
uncertified death. In developing countries, if a death occurs away from health
facilities, a field-worker interviews a relative of the deceased about the
circumstances of the death; this Verbal Autopsy can be reviewed off-site. We
report on a comparative study of the processes involved in Text Classification
applied to classifying Cause of Death: feature value representation; machine
learning classification algorithms; and feature reduction strategies in order
to identify the suitable approaches applicable to the classification of Verbal
Autopsy text. We demonstrate that normalised term frequency and the standard
TFiDF achieve comparable performance across a number of classifiers. The
results also show Support Vector Machine is superior to other classification
algorithms employed in this research. Finally, we demonstrate the effectiveness
of employing a "locally-semi-supervised" feature reduction strategy in order to
increase performance accuracy.
| 2,013 | Computation and Language |
When Learners Surpass their Sources: Mathematical Modeling of Learning
from an Inconsistent Source | We present a new algorithm to model and investigate the learning process of a
learner mastering a set of grammatical rules from an inconsistent source. The
compelling interest of human language acquisition is that the learning succeeds
in virtually every case, despite the fact that the input data are formally
inadequate to explain the success of learning. Our model explains how a learner
can successfully learn from or even surpass its imperfect source without
possessing any additional biases or constraints about the types of patterns
that exist in the language. We use the data collected by Singleton and Newport
(2004) on the performance of a 7-year boy Simon, who mastered the American Sign
Language (ASL) by learning it from his parents, both of whom were imperfect
speakers of ASL. We show that the algorithm possesses a frequency-boosting
property, whereby the frequency of the most common form of the source is
increased by the learner. We also explain several key features of Simon's ASL.
| 2,014 | Computation and Language |
Detecting Opinions in Tweets | Given the incessant growth of documents describing the opinions of different
people circulating on the web, including Web 2.0 has made it possible to give
an opinion on any product in the net. In this paper, we examine the various
opinions expressed in the tweets and classify them positive, negative or
neutral by using the emoticons for the Bayesian method and adjectives and
adverbs for the Turney's method
| 2,013 | Computation and Language |
Modelling the Lexicon in Unsupervised Part of Speech Induction | Automatically inducing the syntactic part-of-speech categories for words in
text is a fundamental task in Computational Linguistics. While the performance
of unsupervised tagging models has been slowly improving, current
state-of-the-art systems make the obviously incorrect assumption that all
tokens of a given word type must share a single part-of-speech tag. This
one-tag-per-type heuristic counters the tendency of Hidden Markov Model based
taggers to over generate tags for a given word type. However, it is clearly
incompatible with basic syntactic theory. In this paper we extend a
state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model
of the lexicon. In doing so we are able to incorporate a soft bias towards
inducing few tags per type. We develop a particle filter for drawing samples
from the posterior of our model and present empirical results that show that
our model is competitive with and faster than the state-of-the-art without
making any unrealistic restrictions.
| 2,014 | Computation and Language |
It's distributions all the way down!: Second order changes in
statistical distributions also occur | The textual, big-data literature misses Bentley, OBrien, & Brocks (Bentley et
als) message on distributions; it largely examines the first-order effects of
how a single, signature distribution can predict population behaviour,
neglecting second-order effects involving distributional shifts, either between
signature distributions or within a given signature distribution. Indeed,
Bentley et al. themselves under-emphasise the potential richness of the latter,
within-distribution effects.
| 2,014 | Computation and Language |
Semantics, Modelling, and the Problem of Representation of Meaning -- a
Brief Survey of Recent Literature | Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.
| 2,014 | Computation and Language |
TBX goes TEI -- Implementing a TBX basic extension for the Text Encoding
Initiative guidelines | This paper presents an attempt to customise the TEI (Text Encoding
Initiative) guidelines in order to offer the possibility to incorporate TBX
(TermBase eXchange) based terminological entries within any kind of TEI
documents. After presenting the general historical, conceptual and technical
contexts, we describe the various design choices we had to take while creating
this customisation, which in turn have led to make various changes in the
actual TBX serialisation. Keeping in mind the objective to provide the TEI
guidelines with, again, an onomasiological model, we try to identify the best
comprise in maintaining both the isomorphism with the existing TBX Basic
standard and the characteristics of the TEI framework.
| 2,014 | Computation and Language |
We Tweet Like We Talk and Other Interesting Observations: An Analysis of
English Communication Modalities | Modalities of communication for human beings are gradually increasing in
number with the advent of new forms of technology. Many human beings can
readily transition between these different forms of communication with little
or no effort, which brings about the question: How similar are these different
communication modalities? To understand technology$\text{'}$s influence on
English communication, four different corpora were analyzed and compared:
Writing from Books using the 1-grams database from the Google Books project,
Twitter, IRC Chat, and transcribed Talking. Multi-word confusion matrices
revealed that Talking has the most similarity when compared to the other modes
of communication, while 1-grams were the least similar form of communication
analyzed. Based on the analysis of word usage, word usage frequency
distributions, and word class usage, among other things, Talking is also the
most similar to Twitter and IRC Chat. This suggests that communicating using
Twitter and IRC Chat evolved from Talking rather than Writing. When we
communicate online, even though we are writing, we do not Tweet or Chat how we
write books; we Tweet and Chat how we Speak. Nonfiction and Fiction writing
were clearly differentiable from our analysis with Twitter and Chat being much
more similar to Fiction than Nonfiction writing. These hypotheses were then
tested using author and journalists Cory Doctorow. Mr. Doctorow$\text{'}$s
Writing, Twitter usage, and Talking were all found to have very similar
vocabulary usage patterns as the amalgamized populations, as long as the
writing was Fiction. However, Mr. Doctorow$\text{'}$s Nonfiction writing is
different from 1-grams and other collected Nonfiction writings. This data could
perhaps be used to create more entertaining works of Nonfiction.
| 2,014 | Computation and Language |
Is getting the right answer just about choosing the right words? The
role of syntactically-informed features in short answer scoring | Developments in the educational landscape have spurred greater interest in
the problem of automatically scoring short answer questions. A recent shared
task on this topic revealed a fundamental divide in the modeling approaches
that have been applied to this problem, with the best-performing systems split
between those that employ a knowledge engineering approach and those that
almost solely leverage lexical information (as opposed to higher-level
syntactic information) in assigning a score to a given response. This paper
aims to introduce the NLP community to the largest corpus currently available
for short-answer scoring, provide an overview of methods used in the shared
task using this data, and explore the extent to which more
syntactically-informed features can contribute to the short answer scoring task
in a way that avoids the question-specific manual effort of the knowledge
engineering approach.
| 2,014 | Computation and Language |
Latent Semantic Word Sense Disambiguation Using Global Co-occurrence
Information | In this paper, I propose a novel word sense disambiguation method based on
the global co-occurrence information using NMF. When I calculate the dependency
relation matrix, the existing method tends to produce very sparse co-occurrence
matrix from a small training set. Therefore, the NMF algorithm sometimes does
not converge to desired solutions. To obtain a large number of co-occurrence
relations, I propose to use co-occurrence frequencies of dependency relations
between word features in the whole training set. This enables us to solve data
sparseness problem and induce more effective latent features. To evaluate the
efficiency of the method of word sense disambiguation, I make some experiments
to compare with the result of the two baseline methods. The results of the
experiments show this method is effective for word sense disambiguation in
comparison with the all baseline methods. Moreover, the proposed method is
effective for obtaining a stable effect by analyzing the global co-occurrence
information.
| 2,014 | Computation and Language |
Authorship detection of SMS messages using unigrams | SMS messaging is a popular media of communication. Because of its popularity
and privacy, it could be used for many illegal purposes. Additionally, since
they are part of the day to day life, SMSes can be used as evidence for many
legal disputes. Since a cellular phone might be accessible to people close to
the owner, it is important to establish the fact that the sender of the message
is indeed the owner of the phone. For this purpose, the straight forward
solutions seem to be the use of popular stylometric methods. However, in
comparison with the data used for stylometry in the literature, SMSes have
unusual characteristics making it hard or impossible to apply these methods in
a conventional way. Our target is to come up with a method of authorship
detection of SMS messages that could still give a usable accuracy. We argue
that, considering the methods of author attribution, the best method that could
be applied to SMS messages is an n-gram method. To prove our point, we checked
two different methods of distribution comparison with varying number of
training and testing data. We specifically try to compare how well our
algorithms work under less amount of testing data and large number of candidate
authors (which we believe to be the real world scenario) against controlled
tests with less number of authors and selected SMSes with large number of
words. To counter the lack of information in an SMS message, we propose the
method of stacking together few SMSes.
| 2,013 | Computation and Language |
Learning Soft Linear Constraints with Application to Citation Field
Extraction | Accurately segmenting a citation string into fields for authors, titles, etc.
is a challenging task because the output typically obeys various global
constraints. Previous work has shown that modeling soft constraints, where the
model is encouraged, but not require to obey the constraints, can substantially
improve segmentation performance. On the other hand, for imposing hard
constraints, dual decomposition is a popular technique for efficient prediction
given existing algorithms for unconstrained inference. We extend the technique
to perform prediction subject to soft constraints. Moreover, with a technique
for performing inference given soft constraints, it is easy to automatically
generate large families of constraints and learn their costs with a simple
convex optimization problem during training. This allows us to obtain
substantial gains in accuracy on a new, challenging citation extraction
dataset.
| 2,014 | Computation and Language |
Finding Eyewitness Tweets During Crises | Disaster response agencies have started to incorporate social media as a
source of fast-breaking information to understand the needs of people affected
by the many crises that occur around the world. These agencies look for tweets
from within the region affected by the crisis to get the latest updates of the
status of the affected region. However only 1% of all tweets are geotagged with
explicit location information. First responders lose valuable information
because they cannot assess the origin of many of the tweets they collect. In
this work we seek to identify non-geotagged tweets that originate from within
the crisis region. Towards this, we address three questions: (1) is there a
difference between the language of tweets originating within a crisis region
and tweets originating outside the region, (2) what are the linguistic patterns
that can be used to differentiate within-region and outside-region tweets, and
(3) for non-geotagged tweets, can we automatically identify those originating
within the crisis region in real-time?
| 2,022 | Computation and Language |
Natural Language Feature Selection via Cooccurrence | Specificity is important for extracting collocations, keyphrases, multi-word
and index terms [Newman et al. 2012]. It is also useful for tagging, ontology
construction [Ryu and Choi 2006], and automatic summarization of documents
[Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency and
inverse-document frequency (TF-IDF) are typically used to do this, but fail to
take advantage of the semantic relationships between terms [Church and Gale
1995]. The result is that general idiomatic terms are mistaken for specific
terms. We demonstrate use of relational data for estimation of term
specificity. The specificity of a term can be learned from its distribution of
relations with other terms. This technique is useful for identifying relevant
words or terms for other natural language processing tasks.
| 2,014 | Computation and Language |
Generating Music from Literature | We present a system, TransProse, that automatically generates musical pieces
from text. TransProse uses known relations between elements of music such as
tempo and scale, and the emotions they evoke. Further, it uses a novel
mechanism to determine sequences of notes that capture the emotional activity
in the text. The work has applications in information visualization, in
creating audio-visual e-books, and in developing music apps.
| 2,014 | Computation and Language |
Parsing using a grammar of word association vectors | This paper was was first drafted in 2001 as a formalization of the system
described in U.S. patent U.S. 7,392,174. It describes a system for implementing
a parser based on a kind of cross-product over vectors of contextually similar
words. It is being published now in response to nascent interest in vector
combination models of syntax and semantics. The method used aggressive
substitution of contextually similar words and word groups to enable product
vectors to stay in the same space as their operands and make entire sentences
comparable syntactically, and potentially semantically. The vectors generated
had sufficient representational strength to generate parse trees at least
comparable with contemporary symbolic parsers.
| 2,014 | Computation and Language |
HPS: a hierarchical Persian stemming method | In this paper, a novel hierarchical Persian stemming approach based on the
Part-Of-Speech of the word in a sentence is presented. The implemented stemmer
includes hash tables and several deterministic finite automata in its different
levels of hierarchy for removing the prefixes and suffixes of the words. We had
two intentions in using hash tables in our method. The first one is that the
DFA don't support some special words, so hash table can partly solve the
addressed problem. the second goal is to speed up the implemented stemmer with
omitting the time that deterministic finite automata need. Because of the
hierarchical organization, this method is fast and flexible enough. Our
experiments on test sets from Hamshahri collection and security news (istna.ir)
show that our method has the average accuracy of 95.37% which is even improved
in using the method on a test set with common topics.
| 2,014 | Computation and Language |
ARSENAL: Automatic Requirements Specification Extraction from Natural
Language | Requirements are informal and semi-formal descriptions of the expected
behavior of a complex system from the viewpoints of its stakeholders
(customers, users, operators, designers, and engineers). However, for the
purpose of design, testing, and verification for critical systems, we can
transform requirements into formal models that can be analyzed automatically.
ARSENAL is a framework and methodology for systematically transforming natural
language (NL) requirements into analyzable formal models and logic
specifications. These models can be analyzed for consistency and
implementability. The ARSENAL methodology is specialized to individual domains,
but the approach is general enough to be adapted to new domains.
| 2,016 | Computation and Language |
Semantic Unification A sheaf theoretic approach to natural language | Language is contextual and sheaf theory provides a high level mathematical
framework to model contextuality. We show how sheaf theory can model the
contextual nature of natural language and how gluing can be used to provide a
global semantics for a discourse by putting together the local logical
semantics of each sentence within the discourse. We introduce a presheaf
structure corresponding to a basic form of Discourse Representation Structures.
Within this setting, we formulate a notion of semantic unification --- gluing
meanings of parts of a discourse into a coherent whole --- as a form of
sheaf-theoretic gluing. We illustrate this idea with a number of examples where
it can used to represent resolutions of anaphoric references. We also discuss
multivalued gluing, described using a distributions functor, which can be used
to represent situations where multiple gluings are possible, and where we may
need to rank them using quantitative measures.
Dedicated to Jim Lambek on the occasion of his 90th birthday.
| 2,014 | Computation and Language |
Language Heedless of Logic - Philosophy Mindful of What? Failures of
Distributive and Absorption Laws | Much of philosophical logic and all of philosophy of language make empirical
claims about the vernacular natural language. They presume semantics under
which `and' and `or' are related by the dually paired distributive and
absorption laws. However, at least one of each pair of laws fails in the
vernacular. `Implicature'-based auxiliary theories associated with the
programme of H.P. Grice do not prove remedial. Conceivable alternatives that
might replace the familiar logics as descriptive instruments are briefly noted:
(i) substructural logics and (ii) meaning composition in linear algebras over
the reals, occasionally constrained by norms of classical logic. Alternative
(ii) locates the problem in violations of one of the idempotent laws. Reasons
for a lack of curiosity about elementary and easily testable implications of
the received theory are considered. The concept of `reflective equilibrium' is
critically examined for its role in reconciling normative desiderata and
descriptive commitments.
| 2,014 | Computation and Language |
Measuring Global Similarity between Texts | We propose a new similarity measure between texts which, contrary to the
current state-of-the-art approaches, takes a global view of the texts to be
compared. We have implemented a tool to compute our textual distance and
conducted experiments on several corpuses of texts. The experiments show that
our methods can reliably identify different global types of texts.
| 2,014 | Computation and Language |
A hybrid formalism to parse Sign Languages | Sign Language (SL) linguistic is dependent on the expensive task of
annotating. Some automation is already available for low-level information (eg.
body part tracking) and the lexical level has shown significant progresses. The
syntactic level lacks annotated corpora as well as complete and consistent
models. This article presents a solution for the automatic annotation of SL
syntactic elements. It exposes a formalism able to represent both
constituency-based and dependency-based models. The first enable the
representation the structures one may want to annotate, the second aims at
fulfilling the holes of the first. A parser is presented and used to conduct
two experiments on the solution. One experiment is on a real corpus, the other
is on a synthetic corpus.
| 2,014 | Computation and Language |
Sign Language Gibberish for syntactic parsing evaluation | Sign Language (SL) automatic processing slowly progresses bottom-up. The
field has seen proposition to handle the video signal, to recognize and
synthesize sublexical and lexical units. It starts to see the development of
supra-lexical processing. But the recognition, at this level, lacks data. The
syntax of SL appears very specific as it uses massively the multiplicity of
articulators and its access to the spatial dimensions. Therefore new parsing
techniques are developed. However these need to be evaluated. The shortage on
real data restrains the corpus-based models to small sizes. We propose here a
solution to produce data-sets for the evaluation of parsers on the specific
properties of SL. The article first describes the general model used to
generates dependency grammars and the phrase generation from these lasts. It
then discusses the limits of approach. The solution shows to be of particular
interest to evaluate the scalability of the techniques on big models.
| 2,014 | Computation and Language |
Spelling Error Trends and Patterns in Sindhi | Statistical error Correction technique is the most accurate and widely used
approach today, but for a language like Sindhi which is a low resourced
language the trained corpora's are not available, so the statistical techniques
are not possible at all. Instead a useful alternative would be to exploit
various spelling error trends in Sindhi by using a Rule based approach. For
designing such technique an essential prerequisite would be to study the
various error patterns in a language. This pa per presents various studies of
spelling error trends and their types in Sindhi Language. The research shows
that the error trends common to all languages are also encountered in Sindhi
but their do exist some error patters that are catered specifically to a Sindhi
language.
| 2,012 | Computation and Language |
Using Entropy Estimates for DAG-Based Ontologies | Motivation: Entropy measurements on hierarchical structures have been used in
methods for information retrieval and natural language modeling. Here we
explore its application to semantic similarity. By finding shared ontology
terms, semantic similarity can be established between annotated genes. A common
procedure for establishing semantic similarity is to calculate the
descriptiveness (information content) of ontology terms and use these values to
determine the similarity of annotations. Most often information content is
calculated for an ontology term by analyzing its frequency in an annotation
corpus. The inherent problems in using these values to model functional
similarity motivates our work. Summary: We present a novel calculation for
establishing the entropy of a DAG-based ontology, which can be used in an
alternative method for establishing the information content of its terms. We
also compare our IC metric to two others using semantic and sequence
similarity.
| 2,017 | Computation and Language |
Clinical TempEval | We describe the Clinical TempEval task which is currently in preparation for
the SemEval-2015 evaluation exercise. This task involves identifying and
describing events, times and the relations between them in clinical text. Six
discrete subtasks are included, focusing on recognising mentions of times and
events, describing those mentions for both entity types, identifying the
relation between an event and the document creation time, and identifying
narrative container relations.
| 2,014 | Computation and Language |
A Lemma Based Evaluator for Semitic Language Text Summarization Systems | Matching texts in highly inflected languages such as Arabic by simple
stemming strategy is unlikely to perform well. In this paper, we present a
strategy for automatic text matching technique for for inflectional languages,
using Arabic as the test case. The system is an extension of ROUGE test in
which texts are matched on token's lemma level. The experimental results show
an enhancement of detecting similarities between different sentences having
same semantics but written in different lexical forms..
| 2,014 | Computation and Language |
Ensemble Detection of Single & Multiple Events at Sentence-Level | Event classification at sentence level is an important Information Extraction
task with applications in several NLP, IR, and personalization systems.
Multi-label binary relevance (BR) are the state-of-art methods. In this work,
we explored new multi-label methods known for capturing relations between event
types. These new methods, such as the ensemble Chain of Classifiers, improve
the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The
low occurrence of multi-label sentences motivated the reduction of the hard
imbalanced multi-label classification problem with low number of occurrences of
multiple labels per instance to an more tractable imbalanced multiclass problem
with better results (+ 4.6%). We report the results of adding new features,
such as sentiment strength, rhetorical signals, domain-id (source-id and date),
and key-phrases in both single-label and multi-label event classification
scenarios.
| 2,014 | Computation and Language |
An efficiency dependency parser using hybrid approach for tamil language | Natural language processing is a prompt research area across the country.
Parsing is one of the very crucial tool in language analysis system which aims
to forecast the structural relationship among the words in a given sentence.
Many researchers have already developed so many language tools but the accuracy
is not meet out the human expectation level, thus the research is still exists.
Machine translation is one of the major application area under Natural Language
Processing. While translation between one language to another language, the
structure identification of a sentence play a key role. This paper introduces
the hybrid way to solve the identification of relationship among the given
words in a sentence. In existing system is implemented using rule based
approach, which is not suited in huge amount of data. The machine learning
approaches is suitable for handle larger amount of data and also to get better
accuracy via learning and training the system. The proposed approach takes a
Tamil sentence as an input and produce the result of a dependency relation as a
tree like structure using hybrid approach. This proposed tool is very helpful
for researchers and act as an odd-on improve the quality of existing
approaches.
| 2,014 | Computation and Language |
Implementation of an Automatic Sign Language Lexical Annotation
Framework based on Propositional Dynamic Logic | In this paper, we present the implementation of an automatic Sign Language
(SL) sign annotation framework based on a formal logic, the Propositional
Dynamic Logic (PDL). Our system relies heavily on the use of a specific variant
of PDL, the Propositional Dynamic Logic for Sign Language (PDLSL), which lets
us describe SL signs as formulae and corpora videos as labeled transition
systems (LTSs). Here, we intend to show how a generic annotation system can be
constructed upon these underlying theoretical principles, regardless of the
tracking technologies available or the input format of corpora. With this in
mind, we generated a development framework that adapts the system to specific
use cases. Furthermore, we present some results obtained by our application
when adapted to one distinct case, 2D corpora analysis with pre-processed
tracking information. We also present some insights on how such a technology
can be used to analyze 3D real-time data, captured with a depth device.
| 2,014 | Computation and Language |
Sign Language Lexical Recognition With Propositional Dynamic Logic | This paper explores the use of Propositional Dynamic Logic (PDL) as a
suitable formal framework for describing Sign Language (SL), the language of
deaf people, in the context of natural language processing. SLs are visual,
complete, standalone languages which are just as expressive as oral languages.
Signs in SL usually correspond to sequences of highly specific body postures
interleaved with movements, which make reference to real world objects,
characters or situations. Here we propose a formal representation of SL signs,
that will help us with the analysis of automatically-collected hand tracking
data from French Sign Language (FSL) video corpora. We further show how such a
representation could help us with the design of computer aided SL verification
tools, which in turn would bring us closer to the development of an automatic
recognition system for these languages.
| 2,013 | Computation and Language |
Emotion Analysis Platform on Chinese Microblog | Weibo, as the largest social media service in China, has billions of messages
generated every day. The huge number of messages contain rich sentimental
information. In order to analyze the emotional changes in accordance with time
and space, this paper presents an Emotion Analysis Platform (EAP), which
explores the emotional distribution of each province, so that can monitor the
global pulse of each province in China. The massive data of Weibo and the
real-time requirements make the building of EAP challenging. In order to solve
the above problems, emoticons, emotion lexicon and emotion-shifting rules are
adopted in EAP to analyze the emotion of each tweet. In order to verify the
effectiveness of the platform, case study on the Sichuan earthquake is done,
and the analysis result of the platform accords with the fact. In order to
analyze from quantity, we manually annotate a test set and conduct experiment
on it. The experimental results show that the macro-Precision of EAP reaches
80% and the EAP works effectively.
| 2,014 | Computation and Language |
Hybrid Approach to English-Hindi Name Entity Transliteration | Machine translation (MT) research in Indian languages is still in its
infancy. Not much work has been done in proper transliteration of name entities
in this domain. In this paper we address this issue. We have used English-Hindi
language pair for our experiments and have used a hybrid approach. At first we
have processed English words using a rule based approach which extracts
individual phonemes from the words and then we have applied statistical
approach which converts the English into its equivalent Hindi phoneme and in
turn the corresponding Hindi word. Through this approach we have attained
83.40% accuracy.
| 2,014 | Computation and Language |
Evaluation and Ranking of Machine Translated Output in Hindi Language
using Precision and Recall Oriented Metrics | Evaluation plays a crucial role in development of Machine translation
systems. In order to judge the quality of an existing MT system i.e. if the
translated output is of human translation quality or not, various automatic
metrics exist. We here present the implementation results of different metrics
when used on Hindi language along with their comparisons, illustrating how
effective are these metrics on languages like Hindi (free word order language).
| 2,014 | Computation and Language |
Int\'egration des donn\'ees d'un lexique syntaxique dans un analyseur
syntaxique probabiliste | This article reports the evaluation of the integration of data from a
syntactic-semantic lexicon, the Lexicon-Grammar of French, into a syntactic
parser. We show that by changing the set of labels for verbs and predicational
nouns, we can improve the performance on French of a non-lexicalized
probabilistic parser.
| 2,014 | Computation and Language |
Polish and English wordnets -- statistical analysis of interconnected
networks | Wordnets are semantic networks containing nouns, verbs, adjectives, and
adverbs organized according to linguistic principles, by means of semantic
relations. In this work, we adopt a complex network perspective to perform a
comparative analysis of the English and Polish wordnets. We determine their
similarities and show that the networks exhibit some of the typical
characteristics observed in other real-world networks. We analyse interlingual
relations between both wordnets and deliberate over the problem of mapping the
Polish lexicon onto the English one.
| 2,014 | Computation and Language |
Aspect-Based Opinion Extraction from Customer reviews | Text is the main method of communicating information in the digital age.
Messages, blogs, news articles, reviews, and opinionated information abound on
the Internet. People commonly purchase products online and post their opinions
about purchased items. This feedback is displayed publicly to assist others
with their purchasing decisions, creating the need for a mechanism with which
to extract and summarize useful information for enhancing the decision-making
process. Our contribution is to improve the accuracy of extraction by combining
different techniques from three major areas, named Data Mining, Natural
Language Processing techniques and Ontologies. The proposed framework
sequentially mines products aspects and users opinions, groups representative
aspects by similarity, and generates an output summary. This paper focuses on
the task of extracting product aspects and users opinions by extracting all
possible aspects and opinions from reviews using natural language, ontology,
and frequent (tag) sets. The proposed framework, when compared with an existing
baseline model, yielded promising results.
| 2,014 | Computation and Language |
Extracting a bilingual semantic grammar from FrameNet-annotated corpora | We present the creation of an English-Swedish FrameNet-based grammar in
Grammatical Framework. The aim of this research is to make existing framenets
computationally accessible for multilingual natural language applications via a
common semantic grammar API, and to facilitate the porting of such grammar to
other languages. In this paper, we describe the abstract syntax of the semantic
grammar while focusing on its automatic extraction possibilities. We have
extracted a shared abstract syntax from ~58,500 annotated sentences in Berkeley
FrameNet (BFN) and ~3,500 annotated sentences in Swedish FrameNet (SweFN). The
abstract syntax defines 769 frame-specific valence patterns that cover 77.8%
examples in BFN and 74.9% in SweFN belonging to the shared set of 471 frames.
As a side result, we provide a unified method for comparing semantic and
syntactic valence patterns across framenets.
| 2,014 | Computation and Language |
A Convolutional Neural Network for Modelling Sentences | The ability to accurately represent sentences is central to language
understanding. We describe a convolutional architecture dubbed the Dynamic
Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of
sentences. The network uses Dynamic k-Max Pooling, a global pooling operation
over linear sequences. The network handles input sentences of varying length
and induces a feature graph over the sentence that is capable of explicitly
capturing short and long-range relations. The network does not rely on a parse
tree and is easily applicable to any language. We test the DCNN in four
experiments: small scale binary and multi-class sentiment prediction, six-way
question classification and Twitter sentiment prediction by distant
supervision. The network achieves excellent performance in the first three
tasks and a greater than 25% error reduction in the last task with respect to
the strongest baseline.
| 2,014 | Computation and Language |
Overview of Stemming Algorithms for Indian and Non-Indian Languages | Stemming is a pre-processing step in Text Mining applications as well as a
very common requirement of Natural Language processing functions. Stemming is
the process for reducing inflected words to their stem. The main purpose of
stemming is to reduce different grammatical forms / word forms of a word like
its noun, adjective, verb, adverb etc. to its root form. Stemming is widely
uses in Information Retrieval system and reduces the size of index files. We
can say that the goal of stemming is to reduce inflectional forms and sometimes
derivationally related forms of a word to a common base form. In this paper we
have discussed different stemming algorithm for non-Indian and Indian language,
methods of stemming, accuracy and errors.
| 2,014 | Computation and Language |
Automatic Detection of Reuses and Citations in Literary Texts | For more than forty years now, modern theories of literature (Compagnon,
1979) insist on the role of paraphrases, rewritings, citations, reciprocal
borrowings and mutual contributions of any kinds. The notions of
intertextuality, transtextuality, hypertextuality/hypotextuality, were
introduced in the seventies and eighties to approach these phenomena. The
careful analysis of these references is of particular interest in evaluating
the distance that the creator voluntarily introduces with his/her masters.
Phoebus is collaborative project that makes computer scientists from the
University Pierre and Marie Curie (LIP6-UPMC) collaborate with the literary
teams of Paris-Sorbonne University with the aim to develop efficient tools for
literary studies that take advantage of modern computer science techniques. In
this context, we have developed a piece of software that automatically detects
and explores networks of textual reuses in classical literature. This paper
describes the principles on which is based this program, the significant
results that have already been obtained and the perspectives for the near
future.
| 2,014 | Computation and Language |
Pagination: It's what you say, not how long it takes to say it | Pagination - the process of determining where to break an article across
pages in a multi-article layout is a common layout challenge for most
commercially printed newspapers and magazines. To date, no one has created an
algorithm that determines a minimal pagination break point based on the content
of the article. Existing approaches for automatic multi-article layout focus
exclusively on maximizing content (number of articles) and optimizing aesthetic
presentation (e.g., spacing between articles). However, disregarding the
semantic information within the article can lead to overly aggressive cutting,
thereby eliminating key content and potentially confusing the reader, or
setting too generous of a break point, thereby leaving in superfluous content
and making automatic layout more difficult. This is one of the remaining
challenges on the path from manual layouts to fully automated processes that
still ensure article content quality. In this work, we present a new approach
to calculating a document minimal break point for the task of pagination. Our
approach uses a statistical language model to predict minimal break points
based on the semantic content of an article. We then compare 4 novel candidate
approaches, and 4 baselines (currently in use by layout algorithms). Results
from this experiment show that one of our approaches strongly outperforms the
baselines and alternatives. Results from a second study suggest that humans are
not able to agree on a single "best" break point. Therefore, this work shows
that a semantic-based lower bound break point prediction is necessary for ideal
automated document synthesis within a real-world context.
| 2,014 | Computation and Language |
A Generalized Language Model as the Combination of Skipped n-grams and
Modified Kneser-Ney Smoothing | We introduce a novel approach for building language models based on a
systematic, recursive exploration of skip n-gram models which are interpolated
using modified Kneser-Ney smoothing. Our approach generalizes language models
as it contains the classical interpolation with lower order models as a special
case. In this paper we motivate, formalize and present our approach. In an
extensive empirical experiment over English text corpora we demonstrate that
our generalized language models lead to a substantial reduction of perplexity
between 3.1% and 12.7% in comparison to traditional language models using
modified Kneser-Ney smoothing. Furthermore, we investigate the behaviour over
three other languages and a domain specific corpus where we observed consistent
improvements. Finally, we also show that the strength of our approach lies in
its ability to cope in particular with sparse training data. Using a very small
training data set of only 736 KB text we yield improvements of even 25.7%
reduction of perplexity.
| 2,014 | Computation and Language |
Meta-evaluation of comparability metrics using parallel corpora | Metrics for measuring the comparability of corpora or texts need to be
developed and evaluated systematically. Applications based on a corpus, such as
training Statistical MT systems in specialised narrow domains, require finding
a reasonable balance between the size of the corpus and its consistency, with
controlled and benchmarked levels of comparability for any newly added
sections. In this article we propose a method that can meta-evaluate
comparability metrics by calculating monolingual comparability scores
separately on the 'source' and 'target' sides of parallel corpora. The range of
scores on the source side is then correlated (using Pearson's r coefficient)
with the range of 'target' scores; the higher the correlation - the more
reliable is the metric. The intuition is that a good metric should yield the
same distance between different domains in different languages. Our method
gives consistent results for the same metrics on different data sets, which
indicates that it is reliable and can be used for metric comparison or for
optimising settings of parametrised metrics.
| 2,014 | Computation and Language |
Complexity of Grammar Induction for Quantum Types | Most categorical models of meaning use a functor from the syntactic category
to the semantic category. When semantic information is available, the problem
of grammar induction can therefore be defined as finding preimages of the
semantic types under this forgetful functor, lifting the information flow from
the semantic level to a valid reduction at the syntactic level. We study the
complexity of grammar induction, and show that for a variety of type systems,
including pivotal and compact closed categories, the grammar induction problem
is NP-complete. Our approach could be extended to linguistic type systems such
as autonomous or bi-closed categories.
| 2,014 | Computation and Language |
Assessing the Quality of MT Systems for Hindi to English Translation | Evaluation plays a vital role in checking the quality of MT output. It is
done either manually or automatically. Manual evaluation is very time consuming
and subjective, hence use of automatic metrics is done most of the times. This
paper evaluates the translation quality of different MT Engines for
Hindi-English (Hindi data is provided as input and English is obtained as
output) using various automatic metrics like BLEU, METEOR etc. Further the
comparison automatic evaluation results with Human ranking have also been
given.
| 2,014 | Computation and Language |
An Empirical Comparison of Parsing Methods for Stanford Dependencies | Stanford typed dependencies are a widely desired representation of natural
language sentences, but parsing is one of the major computational bottlenecks
in text analysis systems. In light of the evolving definition of the Stanford
dependencies and developments in statistical dependency parsing algorithms,
this paper revisits the question of Cer et al. (2010): what is the tradeoff
between accuracy and speed in obtaining Stanford dependencies in particular? We
also explore the effects of input representations on this tradeoff:
part-of-speech tags, the novel use of an alternative dependency representation
as input, and distributional representaions of words. We find that direct
dependency parsing is a more viable solution than it was found to be in the
past. An accompanying software release can be found at:
http://www.ark.cs.cmu.edu/TBSD
| 2,014 | Computation and Language |
Open Question Answering with Weakly Supervised Embedding Models | Building computers able to answer questions on any subject is a long standing
goal of artificial intelligence. Promising progress has recently been achieved
by methods that learn to map questions to logical forms or database queries.
Such approaches can be effective but at the cost of either large amounts of
human-labeled data or by defining lexicons and grammars tailored by
practitioners. In this paper, we instead take the radical approach of learning
to map questions to vectorial feature representations. By mapping answers into
the same space one can query any knowledge base independent of its schema,
without requiring any grammar or lexicon. Our method is trained with a new
optimization procedure combining stochastic gradient descent followed by a
fine-tuning step using the weak supervision provided by blending automatically
and collaboratively generated resources. We empirically demonstrate that our
model can capture meaningful signals from its noisy supervision leading to
major improvements over paralex, the only existing method able to be trained on
similar weakly labeled data.
| 2,014 | Computation and Language |
The First Parallel Multilingual Corpus of Persian: Toward a Persian
BLARK | In this article, we have introduced the first parallel corpus of Persian with
more than 10 other European languages. This article describes primary steps
toward preparing a Basic Language Resources Kit (BLARK) for Persian. Up to now,
we have proposed morphosyntactic specification of Persian based on
EAGLE/MULTEXT guidelines and specific resources of MULTEXT-East. The article
introduces Persian Language, with emphasis on its orthography and
morphosyntactic features, then a new Part-of-Speech categorization and
orthography for Persian in digital environments is proposed. Finally, the
corpus and related statistic will be analyzed.
| 2,014 | Computation and Language |
Multilingual Models for Compositional Distributed Semantics | We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance between those of dissimilar sentences. The models do not rely on word
alignments or any syntactic information and are successfully applied to a
number of diverse languages. We extend our approach to learn semantic
representations at the document level, too. We evaluate these models on two
cross-lingual document classification tasks, outperforming the prior state of
the art. Through qualitative analysis and the study of pivoting effects we
demonstrate that our representations are semantically plausible and can capture
semantic relationships across languages without parallel data.
| 2,014 | Computation and Language |
Radical-Enhanced Chinese Character Embedding | We present a method to leverage radical for learning Chinese character
embedding. Radical is a semantic and phonetic component of Chinese character.
It plays an important role as characters with the same radical usually have
similar semantic meaning and grammatical usage. However, existing Chinese
processing algorithms typically regard word or character as the basic unit but
ignore the crucial radical information. In this paper, we fill this gap by
leveraging radical for learning continuous representation of Chinese character.
We develop a dedicated neural architecture to effectively learn character
embedding and apply it on Chinese character similarity judgement and Chinese
word segmentation. Experiment results show that our radical-enhanced method
outperforms existing embedding learning algorithms on both tasks.
| 2,014 | Computation and Language |
Challenges in Persian Electronic Text Analysis | Farsi, also known as Persian, is the official language of Iran and Tajikistan
and one of the two main languages spoken in Afghanistan. Farsi enjoys a unified
Arabic script as its writing system. In this paper we briefly introduce the
writing standards of Farsi and highlight problems one would face when analyzing
Farsi electronic texts, especially during development of Farsi corpora
regarding to transcription and encoding of Farsi e-texts. The pointes mentioned
may sounds easy but they are crucial when developing and processing written
corpora of Farsi.
| 2,014 | Computation and Language |
The Frobenius anatomy of word meanings I: subject and object relative
pronouns | This paper develops a compositional vector-based semantics of subject and
object relative pronouns within a categorical framework. Frobenius algebras are
used to formalise the operations required to model the semantics of relative
pronouns, including passing information between the relative clause and the
modified noun phrase, as well as copying, combining, and discarding parts of
the relative clause. We develop two instantiations of the abstract semantics,
one based on a truth-theoretic approach and one based on corpus statistics.
| 2,013 | Computation and Language |
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