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17,302 | Enhanced Neural Machine Translation by Learning from Draft | cs.CL | Neural machine translation (NMT) has recently achieved impressive results. A
potential problem of the existing NMT algorithm, however, is that the decoding
is conducted from left to right, without considering the right context. This
paper proposes an two-stage approach to solve the problem. In the first stage,
a conven... | computer science |
17,303 | Counterfactual Language Model Adaptation for Suggesting Phrases | cs.CL | Mobile devices use language models to suggest words and phrases for use in
text entry. Traditional language models are based on contextual word frequency
in a static corpus of text. However, certain types of phrases, when offered to
writers as suggestions, may be systematically chosen more often than their
frequency wo... | computer science |
17,304 | Syntactic and Semantic Features For Code-Switching Factored Language
Models | cs.CL | This paper presents our latest investigations on different features for
factored language models for Code-Switching speech and their effect on
automatic speech recognition (ASR) performance. We focus on syntactic and
semantic features which can be extracted from Code-Switching text data and
integrate them into factored... | computer science |
17,305 | Machine Learning Based Detection of Clickbait Posts in Social Media | cs.CL | Clickbait (headlines) make use of misleading titles that hide critical
information from or exaggerate the content on the landing target pages to
entice clicks. As clickbaits often use eye-catching wording to attract viewers,
target contents are often of low quality. Clickbaits are especially widespread
on social media ... | computer science |
17,306 | On the Effective Use of Pretraining for Natural Language Inference | cs.CL | Neural networks have excelled at many NLP tasks, but there remain open
questions about the performance of pretrained distributed word representations
and their interaction with weight initialization and other hyperparameters. We
address these questions empirically using attention-based sequence-to-sequence
models for n... | computer science |
17,307 | Indowordnets help in Indian Language Machine Translation | cs.CL | Being less resource languages, Indian-Indian and English-Indian language MT
system developments faces the difficulty to translate various lexical
phenomena. In this paper, we present our work on a comparative study of 440
phrase-based statistical trained models for 110 language pairs across 11 Indian
languages. We have... | computer science |
17,308 | Morphology Generation for Statistical Machine Translation | cs.CL | When translating into morphologically rich languages, Statistical MT
approaches face the problem of data sparsity. The severity of the sparseness
problem will be high when the corpus size of morphologically richer language is
less. Even though we can use factored models to correctly generate
morphological forms of word... | computer science |
17,309 | Machine Translation Evaluation with Neural Networks | cs.CL | We present a framework for machine translation evaluation using neural
networks in a pairwise setting, where the goal is to select the better
translation from a pair of hypotheses, given the reference translation. In this
framework, lexical, syntactic and semantic information from the reference and
the two hypotheses i... | computer science |
17,310 | Phrase Pair Mappings for Hindi-English Statistical Machine Translation | cs.CL | In this paper, we present our work on the creation of lexical resources for
the Machine Translation between English and Hindi. We describes the development
of phrase pair mappings for our experiments and the comparative performance
evaluation between different trained models on top of the baseline Statistical
Machine T... | computer science |
17,311 | BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages | cs.CL | We present BPEmb, a collection of pre-trained subword unit embeddings in 275
languages, based on Byte-Pair Encoding (BPE). In an evaluation using
fine-grained entity typing as testbed, BPEmb performs competitively, and for
some languages bet- ter than alternative subword approaches, while requiring
vastly fewer resourc... | computer science |
17,312 | A Semantic Relevance Based Neural Network for Text Summarization and
Text Simplification | cs.CL | Text summarization and text simplification are two major ways to simplify the
text for poor readers, including children, non-native speakers, and the
functionally illiterate. Text summarization is to produce a brief summary of
the main ideas of the text, while text simplification aims to reduce the
linguistic complexit... | computer science |
17,313 | Czech Text Document Corpus v 2.0 | cs.CL | This paper introduces "Czech Text Document Corpus v 2.0", a collection of
text documents for automatic document classification in Czech language. It is
composed of the text documents provided by the Czech News Agency and is freely
available for research purposes at http://ctdc.kiv.zcu.cz/. This corpus was
created in or... | computer science |
17,314 | Bilingual Words and Phrase Mappings for Marathi and Hindi SMT | cs.CL | Lack of proper linguistic resources is the major challenges faced by the
Machine Translation system developments when dealing with the resource poor
languages. In this paper, we describe effective ways to utilize the lexical
resources to improve the quality of statistical machine translation. Our
research on the usage ... | computer science |
17,315 | Learning Word Embeddings for Hyponymy with Entailment-Based
Distributional Semantics | cs.CL | Lexical entailment, such as hyponymy, is a fundamental issue in the semantics
of natural language. This paper proposes distributional semantic models which
efficiently learn word embeddings for entailment, using a recently-proposed
framework for modelling entailment in a vector-space. These models postulate a
latent ve... | computer science |
17,316 | On the Challenges of Sentiment Analysis for Dynamic Events | cs.CL | With the proliferation of social media over the last decade, determining
people's attitude with respect to a specific topic, document, interaction or
events has fueled research interest in natural language processing and
introduced a new channel called sentiment and emotion analysis. For instance,
businesses routinely ... | computer science |
17,317 | Low-resource bilingual lexicon extraction using graph based word
embeddings | cs.CL | In this work we focus on the task of automatically extracting bilingual
lexicon for the language pair Spanish-Nahuatl. This is a low-resource setting
where only a small amount of parallel corpus is available. Most of the
downstream methods do not work well under low-resources conditions. This is
specially true for the ... | computer science |
17,318 | Low-Rank RNN Adaptation for Context-Aware Language Modeling | cs.CL | A context-aware language model uses location, user and/or domain metadata
(context) to adapt its predictions. In neural language models, context
information is typically represented as an embedding and it is given to the RNN
as an additional input, which has been shown to be useful in many applications.
We introduce a ... | computer science |
17,319 | Group Sparse CNNs for Question Classification with Answer Sets | cs.CL | Question classification is an important task with wide applications. However,
traditional techniques treat questions as general sentences, ignoring the
corresponding answer data. In order to consider answer information into
question modeling, we first introduce novel group sparse autoencoders which
refine question repr... | computer science |
17,320 | OSU Multimodal Machine Translation System Report | cs.CL | This paper describes Oregon State University's submissions to the shared
WMT'17 task "multimodal translation task I". In this task, all the sentence
pairs are image captions in different languages. The key difference between
this task and conventional machine translation is that we have corresponding
images as addition... | computer science |
17,321 | Multi-Document Summarization using Distributed Bag-of-Words Model | cs.CL | As the number of documents on the web is growing exponentially,
multi-document summarization is becoming more and more important since it can
provide the main ideas in a document set in short time. In this paper, we
present an unsupervised centroid-based document-level reconstruction framework
using distributed bag of ... | computer science |
17,322 | The IIT Bombay English-Hindi Parallel Corpus | cs.CL | We present the IIT Bombay English-Hindi Parallel Corpus. The corpus is a
compilation of parallel corpora previously available in the public domain as
well as new parallel corpora we collected. The corpus contains 1.49 million
parallel segments, of which 694k segments were not previously available in the
public domain. ... | computer science |
17,323 | Natural Language Inference from Multiple Premises | cs.CL | We define a novel textual entailment task that requires inference over
multiple premise sentences. We present a new dataset for this task that
minimizes trivial lexical inferences, emphasizes knowledge of everyday events,
and presents a more challenging setting for textual entailment. We evaluate
several strong neural ... | computer science |
17,324 | Page Stream Segmentation with Convolutional Neural Nets Combining
Textual and Visual Features | cs.CL | In recent years, (retro-)digitizing paper-based files became a major
undertaking for private and public archives as well as an important task in
electronic mailroom applications. As a first step, the workflow involves
scanning and Optical Character Recognition (OCR) of documents. Preservation of
document contexts of si... | computer science |
17,325 | Deep Learning Paradigm with Transformed Monolingual Word Embeddings for
Multilingual Sentiment Analysis | cs.CL | The surge of social media use brings huge demand of multilingual sentiment
analysis (MSA) for unveiling cultural difference. So far, traditional methods
resorted to machine translation---translating texts in other languages to
English, and then adopt the methods once worked in English. However, this
paradigm is conditi... | computer science |
17,326 | What does Attention in Neural Machine Translation Pay Attention to? | cs.CL | Attention in neural machine translation provides the possibility to encode
relevant parts of the source sentence at each translation step. As a result,
attention is considered to be an alignment model as well. However, there is no
work that specifically studies attention and provides analysis of what is being
learned b... | computer science |
17,327 | MoNoise: Modeling Noise Using a Modular Normalization System | cs.CL | We propose MoNoise: a normalization model focused on generalizability and
efficiency, it aims at being easily reusable and adaptable. Normalization is
the task of translating texts from a non- canonical domain to a more canonical
domain, in our case: from social media data to standard language. Our proposed
model is ba... | computer science |
17,328 | A Very Low Resource Language Speech Corpus for Computational Language
Documentation Experiments | cs.CL | Most speech and language technologies are trained with massive amounts of
speech and text information. However, most of the world languages do not have
such resources or stable orthography. Systems constructed under these almost
zero resource conditions are not only promising for speech technology but also
for computat... | computer science |
17,329 | Confidence through Attention | cs.CL | Attention distributions of the generated translations are a useful bi-product
of attention-based recurrent neural network translation models and can be
treated as soft alignments between the input and output tokens. In this work,
we use attention distributions as a confidence metric for output translations.
We present ... | computer science |
17,330 | The Galactic Dependencies Treebanks: Getting More Data by Synthesizing
New Languages | cs.CL | We release Galactic Dependencies 1.0---a large set of synthetic languages not
found on Earth, but annotated in Universal Dependencies format. This new
resource aims to provide training and development data for NLP methods that aim
to adapt to unfamiliar languages. Each synthetic treebank is produced from a
real treeban... | computer science |
17,331 | Fine-Grained Prediction of Syntactic Typology: Discovering Latent
Structure with Supervised Learning | cs.CL | We show how to predict the basic word-order facts of a novel language given
only a corpus of part-of-speech (POS) sequences. We predict how often direct
objects follow their verbs, how often adjectives follow their nouns, and in
general the directionalities of all dependency relations. Such typological
properties could... | computer science |
17,332 | Decision support from financial disclosures with deep neural networks
and transfer learning | cs.CL | Company disclosures greatly aid in the process of financial decision-making;
therefore, they are consulted by financial investors and automated traders
before exercising ownership in stocks. While humans are usually able to
correctly interpret the content, the same is rarely true of computerized
decision support system... | computer science |
17,333 | DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset | cs.CL | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is
intriguing in several aspects. The language is human-written and less noisy.
The dialogues in the dataset reflect our daily communication way and cover
various topics about our daily life. We also manually label the developed
dataset with commun... | computer science |
17,334 | Word Translation Without Parallel Data | cs.CL | State-of-the-art methods for learning cross-lingual word embeddings have
relied on bilingual dictionaries or parallel corpora. Recent studies showed
that the need for parallel data supervision can be alleviated with
character-level information. While these methods showed encouraging results,
they are not on par with th... | computer science |
17,335 | Using Context Events in Neural Network Models for Event Temporal Status
Identification | cs.CL | Focusing on the task of identifying event temporal status, we find that
events directly or indirectly governing the target event in a dependency tree
are most important contexts. Therefore, we extract dependency chains containing
context events and use them as input in neural network models, which
consistently outperfo... | computer science |
17,336 | Revisiting the Design Issues of Local Models for Japanese
Predicate-Argument Structure Analysis | cs.CL | The research trend in Japanese predicate-argument structure (PAS) analysis is
shifting from pointwise prediction models with local features to global models
designed to search for globally optimal solutions. However, the existing global
models tend to employ only relatively simple local features; therefore, the
overall... | computer science |
17,337 | Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR | cs.CL | This thesis introduces the sequence to sequence model with Luong's attention
mechanism for end-to-end ASR. It also describes various neural network
algorithms including Batch normalization, Dropout and Residual network which
constitute the convolutional attention-based seq2seq neural network. Finally
the proposed model... | computer science |
17,338 | Auto Analysis of Customer Feedback using CNN and GRU Network | cs.CL | Analyzing customer feedback is the best way to channelize the data into new
marketing strategies that benefit entrepreneurs as well as customers. Therefore
an automated system which can analyze the customer behavior is in great demand.
Users may write feedbacks in any language, and hence mining appropriate
information ... | computer science |
17,339 | End-to-end Network for Twitter Geolocation Prediction and Hashing | cs.CL | We propose an end-to-end neural network to predict the geolocation of a
tweet. The network takes as input a number of raw Twitter metadata such as the
tweet message and associated user account information. Our model is language
independent, and despite minimal feature engineering, it is interpretable and
capable of lea... | computer science |
17,340 | Complex Word Identification: Challenges in Data Annotation and System
Performance | cs.CL | This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes... | computer science |
17,341 | Learning Phrase Embeddings from Paraphrases with GRUs | cs.CL | Learning phrase representations has been widely explored in many Natural
Language Processing (NLP) tasks (e.g., Sentiment Analysis, Machine Translation)
and has shown promising improvements. Previous studies either learn
non-compositional phrase representations with general word embedding learning
techniques or learn c... | computer science |
17,342 | Clickbait Detection in Tweets Using Self-attentive Network | cs.CL | Clickbait detection in tweets remains an elusive challenge. In this paper, we
describe the solution for the Zingel Clickbait Detector at the Clickbait
Challenge 2017, which is capable of evaluating each tweet's level of click
baiting. We first reformat the regression problem as a multi-classification
problem, based on ... | computer science |
17,343 | NoReC: The Norwegian Review Corpus | cs.CL | This paper presents the Norwegian Review Corpus (NoReC), created for training
and evaluating models for document-level sentiment analysis. The full-text
reviews have been collected from major Norwegian news sources and cover a range
of different domains, including literature, movies, video games, restaurants,
music and... | computer science |
17,344 | Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in
Social Media | cs.CL | With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable dete... | computer science |
17,345 | BKTreebank: Building a Vietnamese Dependency Treebank | cs.CL | Dependency treebank is an important resource in any language. In this paper,
we present our work on building BKTreebank, a dependency treebank for
Vietnamese. Important points on designing POS tagset, dependency relations, and
annotation guidelines are discussed. We describe experiments on POS tagging and
dependency pa... | computer science |
17,346 | Aligning Script Events with Narrative Texts | cs.CL | Script knowledge plays a central role in text understanding and is relevant
for a variety of downstream tasks. In this paper, we consider two recent
datasets which provide a rich and general representation of script events in
terms of paraphrase sets. We introduce the task of mapping event mentions in
narrative texts t... | computer science |
17,347 | Convolutional Neural Networks for Sentiment Classification on Business
Reviews | cs.CL | Recently Convolutional Neural Networks (CNNs) models have proven remarkable
results for text classification and sentiment analysis. In this paper, we
present our approach on the task of classifying business reviews using word
embeddings on a large-scale dataset provided by Yelp: Yelp 2017 challenge
dataset. We compare ... | computer science |
17,348 | CASICT Tibetan Word Segmentation System for MLWS2017 | cs.CL | We participated in the MLWS 2017 on Tibetan word segmentation task, our
system is trained in a unrestricted way, by introducing a baseline system and
76w tibetan segmented sentences of ours. In the system character sequence is
processed by the baseline system into word sequence, then a subword unit (BPE
algorithm) spli... | computer science |
17,349 | Paying Attention to Multi-Word Expressions in Neural Machine Translation | cs.CL | Processing of multi-word expressions (MWEs) is a known problem for any
natural language processing task. Even neural machine translation (NMT)
struggles to overcome it. This paper presents results of experiments on
investigating NMT attention allocation to the MWEs and improving automated
translation of sentences that ... | computer science |
17,350 | Specialising Word Vectors for Lexical Entailment | cs.CL | We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing
method that transforms any input word vector space to emphasise the asymmetric
relation of lexical entailment (LE), also known as the is-a or
hyponymy-hypernymy relation. By injecting external linguistic constraints
(e.g., WordNet links) into t... | computer science |
17,351 | RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and
CNN | cs.CL | This article presents classifiers based on SVM and Convolutional Neural
Networks (CNN) for the TASS 2017 challenge on tweets sentiment analysis. The
classifier with the best performance in general uses a combination of SVM and
CNN. The use of word embeddings was particularly useful for improving the
classifiers perform... | computer science |
17,352 | Unsupervised Sentence Representations as Word Information Series:
Revisiting TF--IDF | cs.CL | Sentence representation at the semantic level is a challenging task for
Natural Language Processing and Artificial Intelligence. Despite the advances
in word embeddings (i.e. word vector representations), capturing sentence
meaning is an open question due to complexities of semantic interactions among
words. In this pa... | computer science |
17,353 | Basic tasks of sentiment analysis | cs.CL | Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, ... | computer science |
17,354 | Honk: A PyTorch Reimplementation of Convolutional Neural Networks for
Keyword Spotting | cs.CL | We describe Honk, an open-source PyTorch reimplementation of convolutional
neural networks for keyword spotting that are included as examples in
TensorFlow. These models are useful for recognizing "command triggers" in
speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for
audio recordings of utte... | computer science |
17,355 | Towards a Seamless Integration of Word Senses into Downstream NLP
Applications | cs.CL | Lexical ambiguity can impede NLP systems from accurate understanding of
semantics. Despite its potential benefits, the integration of sense-level
information into NLP systems has remained understudied. By incorporating a
novel disambiguation algorithm into a state-of-the-art classification model, we
create a pipeline t... | computer science |
17,356 | Build Fast and Accurate Lemmatization for Arabic | cs.CL | In this paper we describe the complexity of building a lemmatizer for Arabic
which has a rich and complex derivational morphology, and we discuss the need
for a fast and accurate lammatization to enhance Arabic Information Retrieval
(IR) results. We also introduce a new data set that can be used to test
lemmatization a... | computer science |
17,357 | Annotating High-Level Structures of Short Stories and Personal Anecdotes | cs.CL | Stories are a vital form of communication in human culture; they are employed
daily to persuade, to elicit sympathy, or to convey a message. Computational
understanding of human narratives, especially high-level narrative structures,
remain limited to date. Multiple literary theories for narrative structures
exist, but... | computer science |
17,358 | OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for
Multilingual Customer Feedback Analysis | cs.CL | This paper describes our systems for IJCNLP 2017 Shared Task on Customer
Feedback Analysis. We experimented with simple neural architectures that gave
competitive performance on certain tasks. This includes shallow CNN and
Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model.
Our best performi... | computer science |
17,359 | Embedding-Based Speaker Adaptive Training of Deep Neural Networks | cs.CL | An embedding-based speaker adaptive training (SAT) approach is proposed and
investigated in this paper for deep neural network acoustic modeling. In this
approach, speaker embedding vectors, which are a constant given a particular
speaker, are mapped through a control network to layer-dependent element-wise
affine tran... | computer science |
17,360 | SLING: A framework for frame semantic parsing | cs.CL | We describe SLING, a framework for parsing natural language into semantic
frames. SLING supports general transition-based, neural-network parsing with
bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU)
for output decoding. The parsing model is trained end-to-end using only the
text tokens as... | computer science |
17,361 | Unsupervised Context-Sensitive Spelling Correction of English and Dutch
Clinical Free-Text with Word and Character N-Gram Embeddings | cs.CL | We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized repres... | computer science |
17,362 | Multi-Task Label Embedding for Text Classification | cs.CL | Multi-task learning in text classification leverages implicit correlations
among related tasks to extract common features and yield performance gains.
However, most previous works treat labels of each task as independent and
meaningless one-hot vectors, which cause a loss of potential information and
makes it difficult... | computer science |
17,363 | Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation
Models | cs.CL | Building a persona-based conversation agent is challenging owing to the lack
of large amounts of speaker-specific conversation data for model training. This
paper addresses the problem by proposing a multi-task learning approach to
training neural conversation models that leverages both conversation data
across speaker... | computer science |
17,364 | Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised
Two-path Bootstrapping Approach | cs.CL | In the wake of a polarizing election, social media is laden with hateful
content. To address various limitations of supervised hate speech
classification methods including corpus bias and huge cost of annotation, we
propose a weakly supervised two-path bootstrapping approach for an online hate
speech detection model le... | computer science |
17,365 | Detecting Online Hate Speech Using Context Aware Models | cs.CL | In the wake of a polarizing election, the cyber world is laden with hate
speech. Context accompanying a hate speech text is useful for identifying hate
speech, which however has been largely overlooked in existing datasets and hate
speech detection models. In this paper, we provide an annotated corpus of hate
speech wi... | computer science |
17,366 | A Semantically Motivated Approach to Compute ROUGE Scores | cs.CL | ROUGE is one of the first and most widely used evaluation metrics for text
summarization. However, its assessment merely relies on surface similarities
between peer and model summaries. Consequently, ROUGE is unable to fairly
evaluate abstractive summaries including lexical variations and paraphrasing.
Exploring the ef... | computer science |
17,367 | Local Word Vectors Guiding Keyphrase Extraction | cs.CL | Automated keyphrase extraction is a fundamental textual information
processing task concerned with the selection of representative phrases from a
document that summarize its content. This work presents a novel unsupervised
method for keyphrase extraction, whose main innovation is the use of local word
embeddings (in pa... | computer science |
17,368 | Verb Pattern: A Probabilistic Semantic Representation on Verbs | cs.CL | Verbs are important in semantic understanding of natural language.
Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on
verbs' roles. These roles are too coarse to represent verbs' semantics. In this
paper, we introduce verb patterns to represent verbs' semantics, such that each
pattern corre... | computer science |
17,369 | Text Coherence Analysis Based on Deep Neural Network | cs.CL | In this paper, we propose a novel deep coherence model (DCM) using a
convolutional neural network architecture to capture the text coherence. The
text coherence problem is investigated with a new perspective of learning
sentence distributional representation and text coherence modeling
simultaneously. In particular, th... | computer science |
17,370 | How big is big enough? Unsupervised word sense disambiguation using a
very large corpus | cs.CL | In this paper, the problem of disambiguating a target word for Polish is
approached by searching for related words with known meaning. These relatives
are used to build a training corpus from unannotated text. This technique is
improved by proposing new rich sources of replacements that substitute the
traditional requi... | computer science |
17,371 | Bringing Semantic Structures to User Intent Detection in Online Medical
Queries | cs.CL | The Internet has revolutionized healthcare by offering medical information
ubiquitously to patients via web search. The healthcare status, complex medical
information needs of patients are expressed diversely and implicitly in their
medical text queries. Aiming to better capture a focused picture of user's
medical-rela... | computer science |
17,372 | A First Step in Combining Cognitive Event Features and Natural Language
Representations to Predict Emotions | cs.CL | We explore the representational space of emotions by combining methods from
different academic fields. Cognitive science has proposed appraisal theory as a
view on human emotion with previous research showing how human-rated abstract
event features can predict fine-grained emotions and capture the similarity
space of n... | computer science |
17,373 | Testing the limits of unsupervised learning for semantic similarity | cs.CL | Semantic Similarity between two sentences can be defined as a way to
determine how related or unrelated two sentences are. The task of Semantic
Similarity in terms of distributed representations can be thought to be
generating sentence embeddings (dense vectors) which take both context and
meaning of sentence in accoun... | computer science |
17,374 | Attending to All Mention Pairs for Full Abstract Biological Relation
Extraction | cs.CL | Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
However, many relation types, particularly in biomedical text, are expressed
across sentences or require a large context to disambiguate. We propose a model
to conside... | computer science |
17,375 | Deep Health Care Text Classification | cs.CL | Health related social media mining is a valuable apparatus for the early
recognition of the diverse antagonistic medicinal conditions. Mostly, the
existing methods are based on machine learning with knowledge-based learning.
This working note presents the Recurrent neural network (RNN) and Long
short-term memory (LSTM)... | computer science |
17,376 | Combining Lexical Features and a Supervised Learning Approach for Arabic
Sentiment Analysis | cs.CL | The importance of building sentiment analysis tools for Arabic social media
has been recognized during the past couple of years, especially with the rapid
increase in the number of Arabic social media users. One of the main
difficulties in tackling this problem is that text within social media is
mostly colloquial, wit... | computer science |
17,377 | NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis | cs.CL | This paper describes two systems that were used by the authors for addressing
Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors
participated in three Arabic related subtasks which are: Subtask A (Message
Polarity Classification), Sub-task B (Topic-Based Message Polarity
classification) and Subtask ... | computer science |
17,378 | Automatic Generation of Benchmarks for Entity Recognition and Linking | cs.CL | Benchmarks are central to the improvement of named entity recognition and
entity linking solutions. However, recent works have shown that manually
created benchmarks often contain mistakes. We hence investigate the automatic
generation of benchmarks for named entity recognition and linking from Linked
Data as a complem... | computer science |
17,379 | Clickbait Identification using Neural Networks | cs.CL | This paper presents the results of our participation in the Clickbait
Detection Challenge 2017. The system relies on a fusion of neural networks,
incorporating different types of available informations. It does not require
any linguistic preprocessing, and hence generalizes more easily to new domains
and languages. The... | computer science |
17,380 | Linking Tweets with Monolingual and Cross-Lingual News using Transformed
Word Embeddings | cs.CL | Social media platforms have grown into an important medium to spread
information about an event published by the traditional media, such as news
articles. Grouping such diverse sources of information that discuss the same
topic in varied perspectives provide new insights. But the gap in word usage
between informal soci... | computer science |
17,381 | A Simple Text Analytics Model To Assist Literary Criticism: comparative
approach and example on James Joyce against Shakespeare and the Bible | cs.CL | Literary analysis, criticism or studies is a largely valued field with
dedicated journals and researchers which remains mostly within the humanities
scope. Text analytics is the computer-aided process of deriving information
from texts. In this article we describe a simple and generic model for
performing literary anal... | computer science |
17,382 | Exploring the Use of Text Classification in the Legal Domain | cs.CL | In this paper, we investigate the application of text classification methods
to support law professionals. We present several experiments applying machine
learning techniques to predict with high accuracy the ruling of the French
Supreme Court and the law area to which a case belongs to. We also investigate
the influen... | computer science |
17,383 | Non-Projective Dependency Parsing with Non-Local Transitions | cs.CL | We present a novel transition system, based on the Covington non-projective
parser, introducing non-local transitions that can directly create arcs
involving nodes to the left of the current focus positions. This avoids the
need for long sequences of No-Arc transitions to create long-distance arcs,
thus alleviating err... | computer science |
17,384 | ALL-IN-1: Short Text Classification with One Model for All Languages | cs.CL | We present ALL-IN-1, a simple model for multilingual text classification that
does not require any parallel data. It is based on a traditional Support Vector
Machine classifier exploiting multilingual word embeddings and character
n-grams. Our model is simple, easily extendable yet very effective, overall
ranking 1st (... | computer science |
17,385 | Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence
Models | cs.CL | We develop streaming keyword spotting systems using a recurrent neural
network transducer (RNN-T) model: an all-neural, end-to-end trained,
sequence-to-sequence model which jointly learns acoustic and language model
components. Our models are trained to predict either phonemes or graphemes as
subword units, thus allowi... | computer science |
17,386 | Impact of Coreference Resolution on Slot Filling | cs.CL | In this paper, we demonstrate the importance of coreference resolution for
natural language processing on the example of the TAC Slot Filling shared task.
We illustrate the strengths and weaknesses of automatic coreference resolution
systems and provide experimental results to show that they improve performance
in the ... | computer science |
17,387 | CANDiS: Coupled & Attention-Driven Neural Distant Supervision | cs.CL | Distant Supervision for Relation Extraction uses heuristically aligned text
data with an existing knowledge base as training data. The unsupervised nature
of this technique allows it to scale to web-scale relation extraction tasks, at
the expense of noise in the training data. Previous work has explored
relationships a... | computer science |
17,388 | Deep Residual Learning for Small-Footprint Keyword Spotting | cs.CL | We explore the application of deep residual learning and dilated convolutions
to the keyword spotting task, using the recently-released Google Speech
Commands Dataset as our benchmark. Our best residual network (ResNet)
implementation significantly outperforms Google's previous convolutional neural
networks in terms of... | computer science |
17,389 | A Study of All-Convolutional Encoders for Connectionist Temporal
Classification | cs.CL | Connectionist temporal classification (CTC) is a popular sequence prediction
approach for automatic speech recognition that is typically used with models
based on recurrent neural networks (RNNs). We explore whether deep
convolutional neural networks (CNNs) can be used effectively instead of RNNs as
the "encoder" in CT... | computer science |
17,390 | Inducing Regular Grammars Using Recurrent Neural Networks | cs.CL | Grammar induction is the task of learning a grammar from a set of examples.
Recently, neural networks have been shown to be powerful learning machines that
can identify patterns in streams of data. In this work we investigate their
effectiveness in inducing a regular grammar from data, without any assumptions
about the... | computer science |
17,391 | Phase Conductor on Multi-layered Attentions for Machine Comprehension | cs.CL | Attention models have been intensively studied to improve NLP tasks such as
machine comprehension via both question-aware passage attention model and
self-matching attention model. Our research proposes phase conductor
(PhaseCond) for attention models in two meaningful ways. First, PhaseCond, an
architecture of multi-l... | computer science |
17,392 | A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue
Modeling | cs.CL | Ever since the successful application of sequence to sequence learning for
neural machine translation systems, interest has surged in its applicability
towards language generation in other problem domains. Recent work has
investigated the use of these neural architectures towards modeling open-domain
conversational dia... | computer science |
17,393 | Personalized word representations Carrying Personalized Semantics
Learned from Social Network Posts | cs.CL | Distributed word representations have been shown to be very useful in various
natural language processing (NLP) application tasks. These word vectors learned
from huge corpora very often carry both semantic and syntactic information of
words. However, it is well known that each individual user has his own language
patt... | computer science |
17,394 | Path-Based Attention Neural Model for Fine-Grained Entity Typing | cs.CL | Fine-grained entity typing aims to assign entity mentions in the free text
with types arranged in a hierarchical structure. Traditional distant
supervision based methods employ a structured data source as a weak supervision
and do not need hand-labeled data, but they neglect the label noise in the
automatically labeled... | computer science |
17,395 | Evaluation of Automatic Video Captioning Using Direct Assessment | cs.CL | We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics ... | computer science |
17,396 | Finding Dominant User Utterances And System Responses in Conversations | cs.CL | There are several dialog frameworks which allow manual specification of
intents and rule based dialog flow. The rule based framework provides good
control to dialog designers at the expense of being more time consuming and
laborious. The job of a dialog designer can be reduced if we could identify
pairs of user intents... | computer science |
17,397 | JESC: Japanese-English Subtitle Corpus | cs.CL | In this paper we describe the Japanese-English Subtitle Corpus (JESC). JESC
is a large Japanese-English parallel corpus covering the underrepresented
domain of conversational dialogue. It consists of more than 3.2 million
examples, making it the largest freely available dataset of its kind. The
corpus was assembled by ... | computer science |
17,398 | Simple and Effective Multi-Paragraph Reading Comprehension | cs.CL | We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during... | computer science |
17,399 | Machine Translation of Low-Resource Spoken Dialects: Strategies for
Normalizing Swiss German | cs.CL | The goal of this work is to design a machine translation (MT) system for a
low-resource family of dialects, collectively known as Swiss German, which are
widely spoken in Switzerland but seldom written. We collected a significant
number of parallel written resources to start with, up to a total of about 60k
words. More... | computer science |
17,400 | Creation of an Annotated Corpus of Spanish Radiology Reports | cs.CL | This paper presents a new annotated corpus of 513 anonymized radiology
reports written in Spanish. Reports were manually annotated with entities,
negation and uncertainty terms and relations. The corpus was conceived as an
evaluation resource for named entity recognition and relation extraction
algorithms, and as input... | computer science |
17,401 | Improving Social Media Text Summarization by Learning Sentence Weight
Distribution | cs.CL | Recently, encoder-decoder models are widely used in social media text
summarization. However, these models sometimes select noise words in irrelevant
sentences as part of a summary by error, thus declining the performance. In
order to inhibit irrelevant sentences and focus on key information, we propose
an effective ap... | computer science |
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