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15,402 | On Using Very Large Target Vocabulary for Neural Machine Translation | cs.CL | Neural machine translation, a recently proposed approach to machine
translation based purely on neural networks, has shown promising results
compared to the existing approaches such as phrase-based statistical machine
translation. Despite its recent success, neural machine translation has its
limitation in handling a l... | computer science |
15,403 | Practice in Synonym Extraction at Large Scale | cs.CL | Synonym extraction is an important task in natural language processing and
often used as a submodule in query expansion, question answering and other
applications. Automatic synonym extractor is highly preferred for large scale
applications. Previous studies in synonym extraction are most limited to small
scale dataset... | computer science |
15,404 | Learning Word Representations from Relational Graphs | cs.CL | Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if ... | computer science |
15,405 | Rediscovering the Alphabet - On the Innate Universal Grammar | cs.CL | Universal Grammar (UG) theory has been one of the most important research
topics in linguistics since introduced five decades ago. UG specifies the
restricted set of languages learnable by human brain, and thus, many
researchers believe in its biological roots. Numerous empirical studies of
neurobiological and cognitiv... | computer science |
15,406 | Statistical Patterns in Written Language | cs.CL | Quantitative linguistics has been allowed, in the last few decades, within
the admittedly blurry boundaries of the field of complex systems. A growing
host of applied mathematicians and statistical physicists devote their efforts
to disclose regularities, correlations, patterns, and structural properties of
language st... | computer science |
15,407 | A Robust Transformation-Based Learning Approach Using Ripple Down Rules
for Part-of-Speech Tagging | cs.CL | In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; th... | computer science |
15,408 | Incorporating Both Distributional and Relational Semantics in Word
Representations | cs.CL | We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary res... | computer science |
15,409 | Rule-based Emotion Detection on Social Media: Putting Tweets on
Plutchik's Wheel | cs.CL | We study sentiment analysis beyond the typical granularity of polarity and
instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an
extension to the Rule-Based Emission Model algorithm to deduce such emotions
from human-written messages. We evaluate our approach on two different datasets
and compare ... | computer science |
15,410 | Rehabilitation of Count-based Models for Word Vector Representations | cs.CL | Recent works on word representations mostly rely on predictive models.
Distributed word representations (aka word embeddings) are trained to optimally
predict the contexts in which the corresponding words tend to appear. Such
models have succeeded in capturing word similarties as well as semantic and
syntactic regulari... | computer science |
15,411 | Application of Topic Models to Judgments from Public Procurement Domain | cs.CL | In this work, automatic analysis of themes contained in a large corpora of
judgments from public procurement domain is performed. The employed technique
is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed,
to use LDA in conjunction with recently developed method of unsupervised
keyword extrac... | computer science |
15,412 | Computational Model to Generate Case-Inflected Forms of Masculine Nouns
for Word Search in Sanskrit E-Text | cs.CL | The problem of word search in Sanskrit is inseparable from complexities that
include those caused by euphonic conjunctions and case-inflections. The
case-inflectional forms of a noun normally number 24 owing to the fact that in
Sanskrit there are eight cases and three numbers-singular, dual and plural. The
traditional ... | computer science |
15,413 | Incorporating Both Distributional and Relational Semantics in Word
Representations | cs.CL | We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary res... | computer science |
15,414 | A Simple and Efficient Method To Generate Word Sense Representations | cs.CL | Distributed representations of words have boosted the performance of many
Natural Language Processing tasks. However, usually only one representation per
word is obtained, not acknowledging the fact that some words have multiple
meanings. This has a negative effect on the individual word representations and
the languag... | computer science |
15,415 | Supertagging: Introduction, learning, and application | cs.CL | Supertagging is an approach originally developed by Bangalore and Joshi
(1999) to improve the parsing efficiency. In the beginning, the scholars used
small training datasets and somewhat na\"ive smoothing techniques to learn the
probability distributions of supertags. Since its inception, the applicability
of Supertags... | computer science |
15,416 | N-gram-Based Low-Dimensional Representation for Document Classification | cs.CL | The bag-of-words (BOW) model is the common approach for classifying
documents, where words are used as feature for training a classifier. This
generally involves a huge number of features. Some techniques, such as Latent
Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been
designed to summarize docum... | computer science |
15,417 | Leveraging Monolingual Data for Crosslingual Compositional Word
Representations | cs.CL | In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both biling... | computer science |
15,418 | Embedding Word Similarity with Neural Machine Translation | cs.CL | Neural language models learn word representations, or embeddings, that
capture rich linguistic and conceptual information. Here we investigate the
embeddings learned by neural machine translation models, a recently-developed
class of neural language model. We show that embeddings from translation models
outperform thos... | computer science |
15,419 | Embedding Entities and Relations for Learning and Inference in Knowledge
Bases | cs.CL | We consider learning representations of entities and relations in KBs using
the neural-embedding approach. We show that most existing models, including NTN
(Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized
under a unified learning framework, where entities are low-dimensional vectors
learned f... | computer science |
15,420 | Pragmatic Neural Language Modelling in Machine Translation | cs.CL | This paper presents an in-depth investigation on integrating neural language
models in translation systems. Scaling neural language models is a difficult
task, but crucial for real-world applications. This paper evaluates the impact
on end-to-end MT quality of both new and existing scaling techniques. We show
when expl... | computer science |
15,421 | A prototype Malayalam to Sign Language Automatic Translator | cs.CL | Sign language, which is a medium of communication for deaf people, uses
manual communication and body language to convey meaning, as opposed to using
sound. This paper presents a prototype Malayalam text to sign language
translation system. The proposed system takes Malayalam text as input and
generates corresponding S... | computer science |
15,422 | Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary | cs.CL | SentiWordNet is an important lexical resource supporting sentiment analysis
in opinion mining applications. In this paper, we propose a novel approach to
construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated
from WordNet in which each synset has numerical scores to indicate its opinion
polarit... | computer science |
15,423 | Chasing the Ghosts of Ibsen: A computational stylistic analysis of drama
in translation | cs.CL | Research into the stylistic properties of translations is an issue which has
received some attention in computational stylistics. Previous work by Rybicki
(2006) on the distinguishing of character idiolects in the work of Polish
author Henryk Sienkiewicz and two corresponding English translations using
Burrow's Delta m... | computer science |
15,424 | Un résumeur à base de graphes, indépéndant de la langue | cs.CL | In this paper we present REG, a graph-based approach for study a fundamental
problem of Natural Language Processing (NLP): the automatic text summarization.
The algorithm maps a document as a graph, then it computes the weight of their
sentences. We have applied this approach to summarize documents in three
languages. | computer science |
15,425 | Unknown Words Analysis in POS tagging of Sinhala Language | cs.CL | Part of Speech (POS) is a very vital topic in Natural Language Processing
(NLP) task in any language, which involves analysing the construction of the
language, behaviours and the dynamics of the language, the knowledge that could
be utilized in computational linguistics analysis and automation applications.
In this co... | computer science |
15,426 | Quantifying Scripts: Defining metrics of characters for quantitative and
descriptive analysis | cs.CL | Analysis of scripts plays an important role in paleography and in
quantitative linguistics. Especially in the field of digital paleography
quantitative features are much needed to differentiate glyphs. We describe an
elaborate set of metrics that quantify qualitative information contained in
characters and hence indire... | computer science |
15,427 | Navigating the Semantic Horizon using Relative Neighborhood Graphs | cs.CL | This paper is concerned with nearest neighbor search in distributional
semantic models. A normal nearest neighbor search only returns a ranked list of
neighbors, with no information about the structure or topology of the local
neighborhood. This is a potentially serious shortcoming of the mode of querying
a distributio... | computer science |
15,428 | Annotating Cognates and Etymological Origin in Turkic Languages | cs.CL | Turkic languages exhibit extensive and diverse etymological relationships
among lexical items. These relationships make the Turkic languages promising
for exploring automated translation lexicon induction by leveraging cognate and
other etymological information. However, due to the extent and diversity of the
types of ... | computer science |
15,429 | Phrase Based Language Model For Statistical Machine Translation | cs.CL | We consider phrase based Language Models (LM), which generalize the commonly
used word level models. Similar concept on phrase based LMs appears in speech
recognition, which is rather specialized and thus less suitable for machine
translation (MT). In contrast to the dependency LM, we first introduce the
exhaustive phr... | computer science |
15,430 | Phrase Based Language Model for Statistical Machine Translation:
Empirical Study | cs.CL | Reordering is a challenge to machine translation (MT) systems. In MT, the
widely used approach is to apply word based language model (LM) which considers
the constituent units of a sentence as words. In speech recognition (SR), some
phrase based LM have been proposed. However, those LMs are not necessarily
suitable or ... | computer science |
15,431 | Survey:Natural Language Parsing For Indian Languages | cs.CL | Syntactic parsing is a necessary task which is required for NLP applications
including machine translation. It is a challenging task to develop a
qualitative parser for morphological rich and agglutinative languages.
Syntactic analysis is used to understand the grammatical structure of a natural
language sentence. It o... | computer science |
15,432 | Learning to Understand Phrases by Embedding the Dictionary | cs.CL | Distributional models that learn rich semantic word representations are a
success story of recent NLP research. However, developing models that learn
useful representations of phrases and sentences has proved far harder. We
propose using the definitions found in everyday dictionaries as a means of
bridging this gap bet... | computer science |
15,433 | Bengali to Assamese Statistical Machine Translation using Moses (Corpus
Based) | cs.CL | Machine dialect interpretation assumes a real part in encouraging man-machine
correspondence and in addition men-men correspondence in Natural Language
Processing (NLP). Machine Translation (MT) alludes to utilizing machine to
change one dialect to an alternate. Statistical Machine Translation is a type
of MT consistin... | computer science |
15,434 | A Metric to Classify Style of Spoken Speech | cs.CL | The ability to classify spoken speech based on the style of speaking is an
important problem. With the advent of BPO's in recent times, specifically those
that cater to a population other than the local population, it has become
necessary for BPO's to identify people with certain style of speaking
(American, British et... | computer science |
15,435 | Voice based self help System: User Experience Vs Accuracy | cs.CL | In general, self help systems are being increasingly deployed by service
based industries because they are capable of delivering better customer service
and increasingly the switch is to voice based self help systems because they
provide a natural interface for a human to interact with a machine. A speech
based self he... | computer science |
15,436 | Jointly Embedding Relations and Mentions for Knowledge Population | cs.CL | This paper contributes a joint embedding model for predicting relations
between a pair of entities in the scenario of relation inference. It differs
from most stand-alone approaches which separately operate on either knowledge
bases or free texts. The proposed model simultaneously learns low-dimensional
vector represen... | computer science |
15,437 | Concentric network symmetry grasps authors' styles in word adjacency
networks | cs.CL | Several characteristics of written texts have been inferred from statistical
analysis derived from networked models. Even though many network measurements
have been adapted to study textual properties at several levels of complexity,
some textual aspects have been disregarded. In this paper, we study the
symmetry of wo... | computer science |
15,438 | Leveraging Twitter for Low-Resource Conversational Speech Language
Modeling | cs.CL | In applications involving conversational speech, data sparsity is a limiting
factor in building a better language model. We propose a simple,
language-independent method to quickly harvest large amounts of data from
Twitter to supplement a smaller training set that is more closely matched to
the domain. The techniques ... | computer science |
15,439 | A Knowledge-poor Pronoun Resolution System for Turkish | cs.CL | A pronoun resolution system which requires limited syntactic knowledge to
identify the antecedents of personal and reflexive pronouns in Turkish is
presented. As in its counterparts for languages like English, Spanish and
French, the core of the system is the constraints and preferences determined
empirically. In the e... | computer science |
15,440 | Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word:
The Impact of Word Representation on Sequence Labelling Tasks | cs.CL | Word embeddings -- distributed word representations that can be learned from
unlabelled data -- have been shown to have high utility in many natural
language processing applications. In this paper, we perform an extrinsic
evaluation of five popular word embedding methods in the context of four
sequence labelling tasks:... | computer science |
15,441 | On the Stability of Online Language Features: How Much Text do you Need
to know a Person? | cs.CL | In recent years, numerous studies have inferred personality and other traits
from people's online writing. While these studies are encouraging, more
information is needed in order to use these techniques with confidence. How do
linguistic features vary across different online media, and how much text is
required to hav... | computer science |
15,442 | Leveraging Deep Neural Networks and Knowledge Graphs for Entity
Disambiguation | cs.CL | Entity Disambiguation aims to link mentions of ambiguous entities to a
knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for
this task based on the assumption that information from the same semantic
context tends to belong to the same topic. This paper presents a novel deep
semantic relatedness mo... | computer science |
15,443 | Detecting and ordering adjectival scalemates | cs.CL | This paper presents a pattern-based method that can be used to infer
adjectival scales, such as <lukewarm, warm, hot>, from a corpus. Specifically,
the proposed method uses lexical patterns to automatically identify and order
pairs of scalemates, followed by a filtering phase in which unrelated pairs are
discarded. For... | computer science |
15,444 | Texts in, meaning out: neural language models in semantic similarity
task for Russian | cs.CL | Distributed vector representations for natural language vocabulary get a lot
of attention in contemporary computational linguistics. This paper summarizes
the experience of applying neural network language models to the task of
calculating semantic similarity for Russian. The experiments were performed in
the course of... | computer science |
15,445 | Using Syntactic Features for Phishing Detection | cs.CL | This paper reports on the comparison of the subject and object of verbs in
their usage between phishing emails and legitimate emails. The purpose of this
research is to explore whether the syntactic structures and subjects and
objects of verbs can be distinguishable features for phishing detection. To
achieve the objec... | computer science |
15,446 | Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme
Conversion | cs.CL | Sequence-to-sequence translation methods based on generation with a
side-conditioned language model have recently shown promising results in
several tasks. In machine translation, models conditioned on source side words
have been used to produce target-language text, and in image captioning, models
conditioned images h... | computer science |
15,447 | Diversity in Spectral Learning for Natural Language Parsing | cs.CL | We describe an approach to create a diverse set of predictions with spectral
learning of latent-variable PCFGs (L-PCFGs). Our approach works by creating
multiple spectral models where noise is added to the underlying features in the
training set before the estimation of each model. We describe three ways to
decode with... | computer science |
15,448 | Modeling Relation Paths for Representation Learning of Knowledge Bases | cs.CL | Representation learning of knowledge bases (KBs) aims to embed both entities
and relations into a low-dimensional space. Most existing methods only consider
direct relations in representation learning. We argue that multiple-step
relation paths also contain rich inference patterns between entities, and
propose a path-b... | computer science |
15,449 | Monolingually Derived Phrase Scores for Phrase Based SMT Using Neural
Networks Vector Representations | cs.CL | In this paper, we propose two new features for estimating phrase-based
machine translation parameters from mainly monolingual data. Our method is
based on two recently introduced neural network vector representation models
for words and sentences. It is the first time that these models have been used
in an end to end p... | computer science |
15,450 | Medical Synonym Extraction with Concept Space Models | cs.CL | In this paper, we present a novel approach for medical synonym extraction. We
aim to integrate the term embedding with the medical domain knowledge for
healthcare applications. One advantage of our method is that it is very
scalable. Experiments on a dataset with more than 1M term pairs show that the
proposed approach ... | computer science |
15,451 | Statistical Machine Translation Features with Multitask Tensor Networks | cs.CL | We present a three-pronged approach to improving Statistical Machine
Translation (SMT), building on recent success in the application of neural
networks to SMT. First, we propose new features based on neural networks to
model various non-local translation phenomena. Second, we augment the
architecture of the neural net... | computer science |
15,452 | The Influence of Context on Dialogue Act Recognition | cs.CL | This article presents an analysis of the influence of context information on
dialog act recognition. We performed experiments on the widely explored
Switchboard corpus, as well as on data annotated according to the recent ISO
24617-2 standard. The latter was obtained from the Tilburg DialogBank and
through the mapping ... | computer science |
15,453 | A Hierarchical Neural Autoencoder for Paragraphs and Documents | cs.CL | Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragr... | computer science |
15,454 | Visualizing and Understanding Neural Models in NLP | cs.CL | While neural networks have been successfully applied to many NLP tasks the
resulting vector-based models are very difficult to interpret. For example it's
not clear how they achieve {\em compositionality}, building sentence meaning
from the meanings of words and phrases. In this paper we describe four
strategies for vi... | computer science |
15,455 | Do Multi-Sense Embeddings Improve Natural Language Understanding? | cs.CL | Learning a distinct representation for each sense of an ambiguous word could
lead to more powerful and fine-grained models of vector-space representations.
Yet while `multi-sense' methods have been proposed and tested on artificial
word-similarity tasks, we don't know if they improve real natural language
understanding... | computer science |
15,456 | A Hybrid Model for Enhancing Lexical Statistical Machine Translation
(SMT) | cs.CL | The interest in statistical machine translation systems increases currently
due to political and social events in the world. A proposed Statistical Machine
Translation (SMT) based model that can be used to translate a sentence from the
source Language (English) to the target language (Arabic) automatically through
effi... | computer science |
15,457 | Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial
Sentiment Analysis | cs.CL | Although, the fair amount of works in sentiment analysis (SA) and opinion
mining (OM) systems in the last decade and with respect to the performance of
these systems, but it still not desired performance, especially for
morphologically-Rich Language (MRL) such as Arabic, due to the complexities and
challenges exist in ... | computer science |
15,458 | Content Translation: Computer-assisted translation tool for Wikipedia
articles | cs.CL | The quality and quantity of articles in each Wikipedia language varies
greatly. Translating from another Wikipedia is a natural way to add more
content, but the translation process is not properly supported in the software
used by Wikipedia. Past computer-assisted translation tools built for Wikipedia
are not commonly ... | computer science |
15,459 | Sparse Overcomplete Word Vector Representations | cs.CL | Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sp... | computer science |
15,460 | Confounds and Consequences in Geotagged Twitter Data | cs.CL | Twitter is often used in quantitative studies that identify
geographically-preferred topics, writing styles, and entities. These studies
rely on either GPS coordinates attached to individual messages, or on the
user-supplied location field in each profile. In this paper, we compare these
data acquisition techniques and... | computer science |
15,461 | SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and
Implicature | cs.CL | We introduce a corpus of 7,032 sentences rated by human annotators for
formality, informativeness, and implicature on a 1-7 scale. The corpus was
annotated using Amazon Mechanical Turk. Reliability in the obtained judgments
was examined by comparing mean ratings across two MTurk experiments, and
correlation with pilot ... | computer science |
15,462 | Modeling Order in Neural Word Embeddings at Scale | cs.CL | Natural Language Processing (NLP) systems commonly leverage bag-of-words
co-occurrence techniques to capture semantic and syntactic word relationships.
The resulting word-level distributed representations often ignore morphological
information, though character-level embeddings have proven valuable to NLP
tasks. We pro... | computer science |
15,463 | Connotation Frames: A Data-Driven Investigation | cs.CL | Through a particular choice of a predicate (e.g., "x violated y"), a writer
can subtly connote a range of implied sentiments and presupposed facts about
the entities x and y: (1) writer's perspective: projecting x as an
"antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes
x, (3) effect: somet... | computer science |
15,464 | Robust Subgraph Generation Improves Abstract Meaning Representation
Parsing | cs.CL | The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of ... | computer science |
15,465 | A cognitive neural architecture able to learn and communicate through
natural language | cs.CL | Communicative interactions involve a kind of procedural knowledge that is
used by the human brain for processing verbal and nonverbal inputs and for
language production. Although considerable work has been done on modeling human
language abilities, it has been difficult to bring them together to a
comprehensive tabula ... | computer science |
15,466 | Combining Temporal Information and Topic Modeling for Cross-Document
Event Ordering | cs.CL | Building unified timelines from a collection of written news articles
requires cross-document event coreference resolution and temporal relation
extraction. In this paper we present an approach event coreference resolution
according to: a) similar temporal information, and b) similar semantic
arguments. Temporal inform... | computer science |
15,467 | From Paraphrase Database to Compositional Paraphrase Model and Back | cs.CL | The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic)
confidence estimates. However, it is still unclear how it can best be used, due
to the heuristic nature of the confidences and its necessarily incomplete
coverage. We propos... | computer science |
15,468 | Learning language through pictures | cs.CL | We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently pred... | computer science |
15,469 | A Publicly Available Cross-Platform Lemmatizer for Bulgarian | cs.CL | Our dictionary-based lemmatizer for the Bulgarian language presented here is
distributed as free software, publicly available to download and use under the
GPL v3 license. The presented software is written entirely in Java and is
distributed as a GATE plugin. To our best knowledge, at the time of writing
this article, ... | computer science |
15,470 | Evaluation of the Accuracy of the BGLemmatizer | cs.CL | This paper reveals the results of an analysis of the accuracy of developed
software for automatic lemmatization for the Bulgarian language. This
lemmatization software is written entirely in Java and is distributed as a GATE
plugin. Certain statistical methods are used to define the accuracy of this
software. The resul... | computer science |
15,471 | A Bayesian Model for Generative Transition-based Dependency Parsing | cs.CL | We propose a simple, scalable, fully generative model for transition-based
dependency parsing with high accuracy. The model, parameterized by Hierarchical
Pitman-Yor Processes, overcomes the limitations of previous generative models
by allowing fast and accurate inference. We propose an efficient decoding
algorithm bas... | computer science |
15,472 | Parsing Natural Language Sentences by Semi-supervised Methods | cs.CL | We present our work on semi-supervised parsing of natural language sentences,
focusing on multi-source crosslingual transfer of delexicalized dependency
parsers. We first evaluate the influence of treebank annotation styles on
parsing performance, focusing on adposition attachment style. Then, we present
KLcpos3, an em... | computer science |
15,473 | Recognize Foreign Low-Frequency Words with Similar Pairs | cs.CL | Low-frequency words place a major challenge for automatic speech recognition
(ASR). The probabilities of these words, which are often important name
entities, are generally under-estimated by the language model (LM) due to their
limited occurrences in the training data. Recently, we proposed a word-pair
approach to dea... | computer science |
15,474 | Non-distributional Word Vector Representations | cs.CL | Data-driven representation learning for words is a technique of central
importance in NLP. While indisputably useful as a source of features in
downstream tasks, such vectors tend to consist of uninterpretable components
whose relationship to the categories of traditional lexical semantic theories
is tenuous at best. W... | computer science |
15,475 | Pragmatic Side Effects | cs.CL | In the quest to give a formal compositional semantics to natural languages,
semanticists have started turning their attention to phenomena that have been
also considered as parts of pragmatics (e.g., discourse anaphora and
presupposition projection). To account for these phenomena, the very kinds of
meanings assigned t... | computer science |
15,476 | Comparing the writing style of real and artificial papers | cs.CL | Recent years have witnessed the increase of competition in science. While
promoting the quality of research in many cases, an intense competition among
scientists can also trigger unethical scientific behaviors. To increase the
total number of published papers, some authors even resort to software tools
that are able t... | computer science |
15,477 | "The Sum of Its Parts": Joint Learning of Word and Phrase
Representations with Autoencoders | cs.CL | Recently, there has been a lot of effort to represent words in continuous
vector spaces. Those representations have been shown to capture both semantic
and syntactic information about words. However, distributed representations of
phrases remain a challenge. We introduce a novel model that jointly learns word
vector re... | computer science |
15,478 | A Neural Conversational Model | cs.CL | Conversational modeling is an important task in natural language
understanding and machine intelligence. Although previous approaches exist,
they are often restricted to specific domains (e.g., booking an airline ticket)
and require hand-crafted rules. In this paper, we present a simple approach for
this task which use... | computer science |
15,479 | Structured Training for Neural Network Transition-Based Parsing | cs.CL | We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron ... | computer science |
15,480 | deltaBLEU: A Discriminative Metric for Generation Tasks with
Intrinsically Diverse Targets | cs.CL | We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic
evaluation of generated text in tasks that admit a diverse range of possible
outputs. Reference strings are scored for quality by human raters on a scale of
[-1, +1] to weight multi-reference BLEU. In tasks involving generation of
conversational... | computer science |
15,481 | New Approach to translation of Isolated Units in English-Korean Machine
Translation | cs.CL | It is the most effective way for quick translation of tremendous amount of
explosively increasing science and technique information material to develop a
practicable machine translation system and introduce it into translation
practice. This essay treats problems arising from translation of isolated units
on the basis ... | computer science |
15,482 | Automagically encoding Adverse Drug Reactions in MedDRA | cs.CL | Pharmacovigilance is the field of science devoted to the collection, analysis
and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the
extraction of information about ADRs from free text resources are essential to
support the work of experts, employed in the crucial task of detecting and
classifyin... | computer science |
15,483 | A complex network approach to stylometry | cs.CL | Statistical methods have been widely employed to study the fundamental
properties of language. In recent years, methods from complex and dynamical
systems proved useful to create several language models. Despite the large
amount of studies devoted to represent texts with physical models, only a
limited number of studie... | computer science |
15,484 | Prior Polarity Lexical Resources for the Italian Language | cs.CL | In this paper we present SABRINA (Sentiment Analysis: a Broad Resource for
Italian Natural language Applications) a manually annotated prior polarity
lexical resource for Italian natural language applications in the field of
opinion mining and sentiment induction. The resource consists in two different
sets, an Italian... | computer science |
15,485 | Simple, Fast Semantic Parsing with a Tensor Kernel | cs.CL | We describe a simple approach to semantic parsing based on a tensor product
kernel. We extract two feature vectors: one for the query and one for each
candidate logical form. We then train a classifier using the tensor product of
the two vectors. Using very simple features for both, our system achieves an
average F1 sc... | computer science |
15,486 | AutoExtend: Extending Word Embeddings to Embeddings for Synsets and
Lexemes | cs.CL | We present \textit{AutoExtend}, a system to learn embeddings for synsets and
lexemes. It is flexible in that it can take any word embeddings as input and
does not need an additional training corpus. The synset/lexeme embeddings
obtained live in the same vector space as the word embeddings. A sparse tensor
formalization... | computer science |
15,487 | Correspondence Factor Analysis of Big Data Sets: A Case Study of 30
Million Words; and Contrasting Analytics using Apache Solr and Correspondence
Analysis in R | cs.CL | We consider a large number of text data sets. These are cooking recipes. Term
distribution and other distributional properties of the data are investigated.
Our aim is to look at various analytical approaches which allow for mining of
information on both high and low detail scales. Metric space embedding is
fundamental... | computer science |
15,488 | Reflections on Sentiment/Opinion Analysis | cs.CL | In this paper, we described possible directions for deeper understanding,
helping bridge the gap between psychology / cognitive science and computational
approaches in sentiment/opinion analysis literature. We focus on the opinion
holder's underlying needs and their resultant goals, which, in a utilitarian
model of sen... | computer science |
15,489 | A Survey and Classification of Controlled Natural Languages | cs.CL | What is here called controlled natural language (CNL) has traditionally been
given many different names. Especially during the last four decades, a wide
variety of such languages have been designed. They are applied to improve
communication among humans, to improve translation, or to provide natural and
intuitive repre... | computer science |
15,490 | Hindi to English Transfer Based Machine Translation System | cs.CL | In large societies like India there is a huge demand to convert one human
language into another. Lots of work has been done in this area. Many transfer
based MTS have developed for English to other languages, as MANTRA CDAC Pune,
MATRA CDAC Pune, SHAKTI IISc Bangalore and IIIT Hyderabad. Still there is a
little work do... | computer science |
15,491 | What Your Username Says About You | cs.CL | Usernames are ubiquitous on the Internet, and they are often suggestive of
user demographics. This work looks at the degree to which gender and language
can be inferred from a username alone by making use of unsupervised morphology
induction to decompose usernames into sub-units. Experimental results on the
two tasks d... | computer science |
15,492 | Multi-Document Summarization via Discriminative Summary Reranking | cs.CL | Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models... | computer science |
15,493 | Learning to Mine Chinese Coordinate Terms Using the Web | cs.CL | Coordinate relation refers to the relation between instances of a concept and
the relation between the directly hyponyms of a concept. In this paper, we
focus on the task of extracting terms which are coordinate with a user given
seed term in Chinese, and grouping the terms which belong to different concepts
if the see... | computer science |
15,494 | FAQ-based Question Answering via Word Alignment | cs.CL | In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction m... | computer science |
15,495 | Classifier-Based Text Simplification for Improved Machine Translation | cs.CL | Machine Translation is one of the research fields of Computational
Linguistics. The objective of many MT Researchers is to develop an MT System
that produce good quality and high accuracy output translations and which also
covers maximum language pairs. As internet and Globalization is increasing day
by day, we need a ... | computer science |
15,496 | Supervised Hierarchical Classification for Student Answer Scoring | cs.CL | This paper describes a hierarchical system that predicts one label at a time
for automated student response analysis. For the task, we build a
classification binary tree that delays more easily confused labels to later
stages using hierarchical processes. In particular, the paper describes how the
hierarchical classifi... | computer science |
15,497 | Incremental LSTM-based Dialog State Tracker | cs.CL | A dialog state tracker is an important component in modern spoken dialog
systems. We present an incremental dialog state tracker, based on LSTM
networks. It directly uses automatic speech recognition hypotheses to track the
state. We also present the key non-standard aspects of the model that bring its
performance clos... | computer science |
15,498 | Recurrent Polynomial Network for Dialogue State Tracking | cs.CL | Dialogue state tracking (DST) is a process to estimate the distribution of
the dialogue states as a dialogue progresses. Recent studies on constrained
Markov Bayesian polynomial (CMBP) framework take the first step towards
bridging the gap between rule-based and statistical approaches for DST. In this
paper, the gap is... | computer science |
15,499 | Associative Measures and Multi-word Unit Extraction in Turkish | cs.CL | Associative measures are "mathematical formulas determining the strength of
association between two or more words based on their occurrences and
cooccurrences in a text corpus" (Pecina, 2010, p. 138). The purpose of this
paper is to test the 12 associative measures that Text-NSP (Banerjee &
Pedersen, 2003) contains on ... | computer science |
15,500 | How to Generate a Good Word Embedding? | cs.CL | We analyze three critical components of word embedding training: the model,
the corpus, and the training parameters. We systematize existing
neural-network-based word embedding algorithms and compare them using the same
corpus. We evaluate each word embedding in three ways: analyzing its semantic
properties, using it a... | computer science |
15,501 | Notes About a More Aware Dependency Parser | cs.CL | In this paper I explain the reasons that led me to research and conceive a
novel technology for dependency parsing, mixing together the strengths of
data-driven transition-based and constraint-based approaches. In particular I
highlight the problem to infer the reliability of the results of a data-driven
transition-bas... | computer science |
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