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16,402 | Social media mining for identification and exploration of health-related
information from pregnant women | cs.CL | Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper,... | computer science |
16,403 | Neural Machine Translation with Source-Side Latent Graph Parsing | cs.CL | This paper presents a novel neural machine translation model which jointly
learns translation and source-side latent graph representations of sentences.
Unlike existing pipelined approaches using syntactic parsers, our end-to-end
model learns a latent graph parser as part of the encoder of an attention-based
neural mac... | computer science |
16,404 | Automatically Annotated Turkish Corpus for Named Entity Recognition and
Text Categorization using Large-Scale Gazetteers | cs.CL | Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC)
dataset is a collection of automatically categorized and annotated sentences
obtained from Wikipedia. We constructed large-scale gazetteers by using a graph
crawler algorithm to extract relevant entity and domain information from a
semantic kn... | computer science |
16,405 | Iterative Multi-document Neural Attention for Multiple Answer Prediction | cs.CL | People have information needs of varying complexity, which can be solved by
an intelligent agent able to answer questions formulated in a proper way,
eventually considering user context and preferences. In a scenario in which the
user profile can be considered as a question, intelligent agents able to answer
questions ... | computer science |
16,406 | A Hybrid Convolutional Variational Autoencoder for Text Generation | cs.CL | In this paper we explore the effect of architectural choices on learning a
Variational Autoencoder (VAE) for text generation. In contrast to the
previously introduced VAE model for text where both the encoder and decoder are
RNNs, we propose a novel hybrid architecture that blends fully feed-forward
convolutional and d... | computer science |
16,407 | Exploiting Domain Knowledge via Grouped Weight Sharing with Application
to Text Categorization | cs.CL | A fundamental advantage of neural models for NLP is their ability to learn
representations from scratch. However, in practice this often means ignoring
existing external linguistic resources, e.g., WordNet or domain specific
ontologies such as the Unified Medical Language System (UMLS). We propose a
general, novel meth... | computer science |
16,408 | Predicting Audience's Laughter Using Convolutional Neural Network | cs.CL | For the purpose of automatically evaluating speakers' humor usage, we build a
presentation corpus containing humorous utterances based on TED talks. Compared
to previous data resources supporting humor recognition research, ours has
several advantages, including (a) both positive and negative instances coming
from a ho... | computer science |
16,409 | Local System Voting Feature for Machine Translation System Combination | cs.CL | In this paper, we enhance the traditional confusion network system
combination approach with an additional model trained by a neural network. This
work is motivated by the fact that the commonly used binary system voting
models only assign each input system a global weight which is responsible for
the global impact of ... | computer science |
16,410 | UsingWord Embedding for Cross-Language Plagiarism Detection | cs.CL | This paper proposes to use distributed representation of words (word
embeddings) in cross-language textual similarity detection. The main
contributions of this paper are the following: (a) we introduce new
cross-language similarity detection methods based on distributed representation
of words; (b) we combine the diffe... | computer science |
16,411 | Universal Semantic Parsing | cs.CL | Universal Dependencies (UD) offer a uniform cross-lingual syntactic
representation, with the aim of advancing multilingual applications. Recent
work shows that semantic parsing can be accomplished by transforming syntactic
dependencies to logical forms. However, this work is limited to English, and
cannot process depen... | computer science |
16,412 | Universal Dependencies to Logical Forms with Negation Scope | cs.CL | Many language technology applications would benefit from the ability to
represent negation and its scope on top of widely-used linguistic resources. In
this paper, we investigate the possibility of obtaining a first-order logic
representation with negation scope marked using Universal Dependencies. To do
so, we enhance... | computer science |
16,413 | Learning Concept Embeddings for Efficient Bag-of-Concepts Densification | cs.CL | Explicit concept space models have proven efficacy for text representation in
many natural language and text mining applications. The idea is to embed
textual structures into a semantic space of concepts which captures the main
topics of these structures. That so called bag-of-concepts representation
suffers from data ... | computer science |
16,414 | Vector Embedding of Wikipedia Concepts and Entities | cs.CL | Using deep learning for different machine learning tasks such as image
classification and word embedding has recently gained many attentions. Its
appealing performance reported across specific Natural Language Processing
(NLP) tasks in comparison with other approaches is the reason for its
popularity. Word embedding is... | computer science |
16,415 | Learning to Parse and Translate Improves Neural Machine Translation | cs.CL | There has been relatively little attention to incorporating linguistic prior
to neural machine translation. Much of the previous work was further
constrained to considering linguistic prior on the source side. In this paper,
we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
by combining the... | computer science |
16,416 | A Morphology-aware Network for Morphological Disambiguation | cs.CL | Agglutinative languages such as Turkish, Finnish and Hungarian require
morphological disambiguation before further processing due to the complex
morphology of words. A morphological disambiguator is used to select the
correct morphological analysis of a word. Morphological disambiguation is
important because it general... | computer science |
16,417 | Multitask Learning with Deep Neural Networks for Community Question
Answering | cs.CL | In this paper, we developed a deep neural network (DNN) that learns to solve
simultaneously the three tasks of the cQA challenge proposed by the
SemEval-2016 Task 3, i.e., question-comment similarity, question-question
similarity and new question-comment similarity. The latter is the main task,
which can exploit the pr... | computer science |
16,418 | Towards speech-to-text translation without speech recognition | cs.CL | We explore the problem of translating speech to text in low-resource
scenarios where neither automatic speech recognition (ASR) nor machine
translation (MT) are available, but we have training data in the form of audio
paired with text translations. We present the first system for this problem
applied to a realistic mu... | computer science |
16,419 | The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations
Annotated with Compositional Meaning Representations | cs.CL | The Parallel Meaning Bank is a corpus of translations annotated with shared,
formal meaning representations comprising over 11 million words divided over
four languages (English, German, Italian, and Dutch). Our approach is based on
cross-lingual projection: automatically produced (and manually corrected)
semantic anno... | computer science |
16,420 | JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction | cs.CL | We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
developing and evaluating grammatical error correction (GEC). Unlike other
corpora, it represents a broad range of language proficiency levels and uses
holistic fluency edits to not only correct grammatical errors but also make the
original t... | computer science |
16,421 | Detection of Slang Words in e-Data using semi-Supervised Learning | cs.CL | The proposed algorithmic approach deals with finding the sense of a word in
an electronic data. Now a day,in different communication mediums like internet,
mobile services etc. people use few words, which are slang in nature. This
approach detects those abusive words using supervised learning procedure. But
in the real... | computer science |
16,422 | On the Relevance of Auditory-Based Gabor Features for Deep Learning in
Automatic Speech Recognition | cs.CL | Previous studies support the idea of merging auditory-based Gabor features
with deep learning architectures to achieve robust automatic speech
recognition, however, the cause behind the gain of such combination is still
unknown. We believe these representations provide the deep learning decoder
with more discriminable ... | computer science |
16,423 | A case study on using speech-to-translation alignments for language
documentation | cs.CL | For many low-resource or endangered languages, spoken language resources are
more likely to be annotated with translations than with transcriptions. Recent
work exploits such annotations to produce speech-to-translation alignments,
without access to any text transcriptions. We investigate whether providing
such informa... | computer science |
16,424 | Automated Phrase Mining from Massive Text Corpora | cs.CL | As one of the fundamental tasks in text analysis, phrase mining aims at
extracting quality phrases from a text corpus. Phrase mining is important in
various tasks such as information extraction/retrieval, taxonomy construction,
and topic modeling. Most existing methods rely on complex, trained linguistic
analyzers, and... | computer science |
16,425 | Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a
Novel Neural Network | cs.CL | Recent studies have shown effectiveness in using neural networks for Chinese
word segmentation. However, these models rely on large-scale data and are less
effective for low-resource datasets because of insufficient training data. We
propose a transfer learning method to improve low-resource word segmentation by
levera... | computer science |
16,426 | A Dependency-Based Neural Reordering Model for Statistical Machine
Translation | cs.CL | In machine translation (MT) that involves translating between two languages
with significant differences in word order, determining the correct word order
of translated words is a major challenge. The dependency parse tree of a source
sentence can help to determine the correct word order of the translated words.
In thi... | computer science |
16,427 | Automated Identification of Drug-Drug Interactions in Pediatric
Congestive Heart Failure Patients | cs.CL | Congestive Heart Failure, or CHF, is a serious medical condition that can
result in fluid buildup in the body as a result of a weak heart. When the heart
can't pump enough blood to efficiently deliver nutrients and oxygen to the
body, kidney function may be impaired, resulting in fluid retention. CHF
patients require a... | computer science |
16,428 | An Analysis of Ability in Deep Neural Networks | cs.CL | Deep neural networks (DNNs) have made significant progress in a number of
Machine Learning applications. However without a consistent set of evaluation
tasks, interpreting performance across test datasets is impossible. In most
previous work, characteristics of individual data points are not considered
during evaluatio... | computer science |
16,429 | Fast and unsupervised methods for multilingual cognate clustering | cs.CL | In this paper we explore the use of unsupervised methods for detecting
cognates in multilingual word lists. We use online EM to train sound segment
similarity weights for computing similarity between two words. We tested our
online systems on geographically spread sixteen different language groups of
the world and show... | computer science |
16,430 | Addressing the Data Sparsity Issue in Neural AMR Parsing | cs.CL | Neural attention models have achieved great success in different NLP tasks.
How- ever, they have not fulfilled their promise on the AMR parsing task due to
the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence
model for AMR parsing and present different ways to tackle the data sparsity
problem. W... | computer science |
16,431 | Experiment Segmentation in Scientific Discourse as Clause-level
Structured Prediction using Recurrent Neural Networks | cs.CL | We propose a deep learning model for identifying structure within experiment
narratives in scientific literature. We take a sequence labeling approach to
this problem, and label clauses within experiment narratives to identify the
different parts of the experiment. Our dataset consists of paragraphs taken
from open acc... | computer science |
16,432 | Analysis and Optimization of fastText Linear Text Classifier | cs.CL | The paper [1] shows that simple linear classifier can compete with complex
deep learning algorithms in text classification applications. Combining bag of
words (BoW) and linear classification techniques, fastText [1] attains same or
only slightly lower accuracy than deep learning algorithms [2-9] that are
orders of mag... | computer science |
16,433 | Reproducing and learning new algebraic operations on word embeddings
using genetic programming | cs.CL | Word-vector representations associate a high dimensional real-vector to every
word from a corpus. Recently, neural-network based methods have been proposed
for learning this representation from large corpora. This type of
word-to-vector embedding is able to keep, in the learned vector space, some of
the syntactic and s... | computer science |
16,434 | A Stylometric Inquiry into Hyperpartisan and Fake News | cs.CL | This paper reports on a writing style analysis of hyperpartisan (i.e.,
extremely one-sided) news in connection to fake news. It presents a large
corpus of 1,627 articles that were manually fact-checked by professional
journalists from BuzzFeed. The articles originated from 9 well-known political
publishers, 3 each from... | computer science |
16,435 | Harmonic Grammar, Optimality Theory, and Syntax Learnability: An
Empirical Exploration of Czech Word Order | cs.CL | This work presents a systematic theoretical and empirical comparison of the
major algorithms that have been proposed for learning Harmonic and Optimality
Theory grammars (HG and OT, respectively). By comparing learning algorithms, we
are also able to compare the closely related OT and HG frameworks themselves.
Experime... | computer science |
16,436 | Post-edit Analysis of Collective Biography Generation | cs.CL | Text generation is increasingly common but often requires manual post-editing
where high precision is critical to end users. However, manual editing is
expensive so we want to ensure this effort is focused on high-value tasks. And
we want to maintain stylistic consistency, a particular challenge in crowd
settings. We p... | computer science |
16,437 | Latent Variable Dialogue Models and their Diversity | cs.CL | We present a dialogue generation model that directly captures the variability
in possible responses to a given input, which reduces the `boring output' issue
of deterministic dialogue models. Experiments show that our model generates
more diverse outputs than baseline models, and also generates more consistently
accept... | computer science |
16,438 | Parent Oriented Teacher Selection Causes Language Diversity | cs.CL | An evolutionary model for emergence of diversity in language is developed. We
investigated the effects of two real life observations, namely, people prefer
people that they communicate with well, and people interact with people that
are physically close to each other. Clearly these groups are relatively small
compared ... | computer science |
16,439 | Enabling Multi-Source Neural Machine Translation By Concatenating Source
Sentences In Multiple Languages | cs.CL | In this paper, we propose a novel and elegant solution to "Multi-Source
Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way
multilingual corpus without modifying the Neural Machine Translation (NMT)
architecture or training procedure. We simply concatenate the source sentences
to form a singl... | computer science |
16,440 | Learning to generate one-sentence biographies from Wikidata | cs.CL | We investigate the generation of one-sentence Wikipedia biographies from
facts derived from Wikidata slot-value pairs. We train a recurrent neural
network sequence-to-sequence model with attention to select facts and generate
textual summaries. Our model incorporates a novel secondary objective that
helps ensure it gen... | computer science |
16,441 | Reinforcement Learning Based Argument Component Detection | cs.CL | Argument component detection (ACD) is an important sub-task in argumentation
mining. ACD aims at detecting and classifying different argument components in
natural language texts. Historical annotations (HAs) are important features the
human annotators consider when they manually perform the ACD task. However, HAs
are ... | computer science |
16,442 | Hybrid Dialog State Tracker with ASR Features | cs.CL | This paper presents a hybrid dialog state tracker enhanced by trainable
Spoken Language Understanding (SLU) for slot-filling dialog systems. Our
architecture is inspired by previously proposed neural-network-based
belief-tracking systems. In addition, we extended some parts of our modular
architecture with differentiab... | computer science |
16,443 | Multitask Learning with CTC and Segmental CRF for Speech Recognition | cs.CL | Segmental conditional random fields (SCRFs) and connectionist temporal
classification (CTC) are two sequence labeling methods used for end-to-end
training of speech recognition models. Both models define a transcription
probability by marginalizing decisions about latent segmentation alternatives
to derive a sequence p... | computer science |
16,444 | Neural Multi-Step Reasoning for Question Answering on Semi-Structured
Tables | cs.CL | Advances in natural language processing tasks have gained momentum in recent
years due to the increasingly popular neural network methods. In this paper, we
explore deep learning techniques for answering multi-step reasoning questions
that operate on semi-structured tables. Challenges here arise from the level of
logic... | computer science |
16,445 | On the Complexity of CCG Parsing | cs.CL | We study the parsing complexity of Combinatory Categorial Grammar (CCG) in
the formalism of Vijay-Shanker and Weir (1994). As our main result, we prove
that any parsing algorithm for this formalism will necessarily take exponential
time when the size of the grammar, and not only the length of the input
sentence, is inc... | computer science |
16,446 | Calculating Probabilities Simplifies Word Learning | cs.CL | Children can use the statistical regularities of their environment to learn
word meanings, a mechanism known as cross-situational learning. We take a
computational approach to investigate how the information present during each
observation in a cross-situational framework can affect the overall acquisition
of word mean... | computer science |
16,447 | Context-Aware Prediction of Derivational Word-forms | cs.CL | Derivational morphology is a fundamental and complex characteristic of
language. In this paper we propose the new task of predicting the derivational
form of a given base-form lemma that is appropriate for a given context. We
present an encoder--decoder style neural network to produce a derived form
character-by-charac... | computer science |
16,448 | One Representation per Word - Does it make Sense for Composition? | cs.CL | In this paper, we investigate whether an a priori disambiguation of word
senses is strictly necessary or whether the meaning of a word in context can be
disambiguated through composition alone. We evaluate the performance of
off-the-shelf single-vector and multi-sense vector models on a benchmark phrase
similarity task... | computer science |
16,449 | Data Distillation for Controlling Specificity in Dialogue Generation | cs.CL | People speak at different levels of specificity in different situations.
Depending on their knowledge, interlocutors, mood, etc.} A conversational agent
should have this ability and know when to be specific and when to be general.
We propose an approach that gives a neural network--based conversational agent
this abili... | computer science |
16,450 | Fine-Grained Entity Type Classification by Jointly Learning
Representations and Label Embeddings | cs.CL | Fine-grained entity type classification (FETC) is the task of classifying an
entity mention to a broad set of types. Distant supervision paradigm is
extensively used to generate training data for this task. However, generated
training data assigns same set of labels to every mention of an entity without
considering its... | computer science |
16,451 | Improving a Strong Neural Parser with Conjunction-Specific Features | cs.CL | While dependency parsers reach very high overall accuracy, some dependency
relations are much harder than others. In particular, dependency parsers
perform poorly in coordination construction (i.e., correctly attaching the
"conj" relation). We extend a state-of-the-art dependency parser with
conjunction-specific featur... | computer science |
16,452 | Improving Chinese SRL with Heterogeneous Annotations | cs.CL | Previous studies on Chinese semantic role labeling (SRL) have concentrated on
single semantically annotated corpus. But the training data of single corpus is
often limited. Meanwhile, there usually exists other semantically annotated
corpora for Chinese SRL scattered across different annotation frameworks. Data
sparsit... | computer science |
16,453 | Tackling Error Propagation through Reinforcement Learning: A Case of
Greedy Dependency Parsing | cs.CL | Error propagation is a common problem in NLP. Reinforcement learning explores
erroneous states during training and can therefore be more robust when mistakes
are made early in a process. In this paper, we apply reinforcement learning to
greedy dependency parsing which is known to suffer from error propagation.
Reinforc... | computer science |
16,454 | EVE: Explainable Vector Based Embedding Technique Using Wikipedia | cs.CL | We present an unsupervised explainable word embedding technique, called EVE,
which is built upon the structure of Wikipedia. The proposed model defines the
dimensions of a semantic vector representing a word using human-readable
labels, thereby it readily interpretable. Specifically, each vector is
constructed using th... | computer science |
16,455 | Unsupervised Learning of Morphological Forests | cs.CL | This paper focuses on unsupervised modeling of morphological families,
collectively comprising a forest over the language vocabulary. This formulation
enables us to capture edgewise properties reflecting single-step morphological
derivations, along with global distributional properties of the entire forest.
These globa... | computer science |
16,456 | Feature Generation for Robust Semantic Role Labeling | cs.CL | Hand-engineered feature sets are a well understood method for creating robust
NLP models, but they require a lot of expertise and effort to create. In this
work we describe how to automatically generate rich feature sets from simple
units called featlets, requiring less engineering. Using information gain to
guide the ... | computer science |
16,457 | LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and
Vector Representations | cs.CL | Topic models have been widely used in discovering latent topics which are
shared across documents in text mining. Vector representations, word embeddings
and topic embeddings, map words and topics into a low-dimensional and dense
real-value vector space, which have obtained high performance in NLP tasks.
However, most ... | computer science |
16,458 | Utilizing Lexical Similarity between Related, Low-resource Languages for
Pivot-based SMT | cs.CL | We investigate pivot-based translation between related languages in a low
resource, phrase-based SMT setting. We show that a subword-level pivot-based
SMT model using a related pivot language is substantially better than word and
morpheme-level pivot models. It is also highly competitive with the best direct
translatio... | computer science |
16,459 | Are Emojis Predictable? | cs.CL | Emojis are ideograms which are naturally combined with plain text to visually
complement or condense the meaning of a message. Despite being widely used in
social media, their underlying semantics have received little attention from a
Natural Language Processing standpoint. In this paper, we investigate the
relation be... | computer science |
16,460 | Inherent Biases of Recurrent Neural Networks for Phonological
Assimilation and Dissimilation | cs.CL | A recurrent neural network model of phonological pattern learning is
proposed. The model is a relatively simple neural network with one recurrent
layer, and displays biases in learning that mimic observed biases in human
learning. Single-feature patterns are learned faster than two-feature patterns,
and vowel or conson... | computer science |
16,461 | Dirichlet-vMF Mixture Model | cs.CL | This document is about the multi-document Von-Mises-Fisher mixture model with
a Dirichlet prior, referred to as VMFMix. VMFMix is analogous to Latent
Dirichlet Allocation (LDA) in that they can capture the co-occurrence patterns
acorss multiple documents. The difference is that in VMFMix, the topic-word
distribution is... | computer science |
16,462 | Use Generalized Representations, But Do Not Forget Surface Features | cs.CL | Only a year ago, all state-of-the-art coreference resolvers were using an
extensive amount of surface features. Recently, there was a paradigm shift
towards using word embeddings and deep neural networks, where the use of
surface features is very limited. In this paper, we show that a simple SVM
model with surface feat... | computer science |
16,463 | Residual Convolutional CTC Networks for Automatic Speech Recognition | cs.CL | Deep learning approaches have been widely used in Automatic Speech
Recognition (ASR) and they have achieved a significant accuracy improvement.
Especially, Convolutional Neural Networks (CNNs) have been revisited in ASR
recently. However, most CNNs used in existing work have less than 10 layers
which may not be deep en... | computer science |
16,464 | Critical Survey of the Freely Available Arabic Corpora | cs.CL | The availability of corpora is a major factor in building natural language
processing applications. However, the costs of acquiring corpora can prevent
some researchers from going further in their endeavours. The ease of access to
freely available corpora is urgent needed in the NLP research community
especially for la... | computer science |
16,465 | Detecting (Un)Important Content for Single-Document News Summarization | cs.CL | We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article b... | computer science |
16,466 | Friends and Enemies of Clinton and Trump: Using Context for Detecting
Stance in Political Tweets | cs.CL | Stance detection, the task of identifying the speaker's opinion towards a
particular target, has attracted the attention of researchers. This paper
describes a novel approach for detecting stance in Twitter. We define a set of
features in order to consider the context surrounding a target of interest with
the final aim... | computer science |
16,467 | A case study on English-Malayalam Machine Translation | cs.CL | In this paper we present our work on a case study on Statistical Machine
Translation (SMT) and Rule based machine translation (RBMT) for translation
from English to Malayalam and Malayalam to English. One of the motivations of
our study is to make a three way performance comparison, such as, a) SMT and
RBMT b) English ... | computer science |
16,468 | Identifying beneficial task relations for multi-task learning in deep
neural networks | cs.CL | Multi-task learning (MTL) in deep neural networks for NLP has recently
received increasing interest due to some compelling benefits, including its
potential to efficiently regularize models and to reduce the need for labeled
data. While it has brought significant improvements in a number of NLP tasks,
mixed results hav... | computer science |
16,469 | A Knowledge-Based Approach to Word Sense Disambiguation by
distributional selection and semantic features | cs.CL | Word sense disambiguation improves many Natural Language Processing (NLP)
applications such as Information Retrieval, Information Extraction, Machine
Translation, or Lexical Simplification. Roughly speaking, the aim is to choose
for each word in a text its best sense. One of the most popular method
estimates local sema... | computer science |
16,470 | Approches d'analyse distributionnelle pour améliorer la
désambiguïsation sémantique | cs.CL | Word sense disambiguation (WSD) improves many Natural Language Processing
(NLP) applications such as Information Retrieval, Machine Translation or
Lexical Simplification. WSD is the ability of determining a word sense among
different ones within a polysemic lexical unit taking into account the context.
The most straigh... | computer science |
16,471 | CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability | cs.CL | Item Response Theory (IRT) allows for measuring ability of Machine Learning
models as compared to a human population. However, it is difficult to create a
large dataset to train the ability of deep neural network models (DNNs). We
propose Crowd-Informed Fine-Tuning (CIFT) as a new training process, where a
pre-trained ... | computer science |
16,472 | Scaffolding Networks: Incremental Learning and Teaching Through
Questioning | cs.CL | We introduce a new paradigm of learning for reasoning, understanding, and
prediction, as well as the scaffolding network to implement this paradigm. The
scaffolding network embodies an incremental learning approach that is
formulated as a teacher-student network architecture to teach machines how to
understand text and... | computer science |
16,473 | Studying Positive Speech on Twitter | cs.CL | We present results of empirical studies on positive speech on Twitter. By
positive speech we understand speech that works for the betterment of a given
situation, in this case relations between different communities in a
conflict-prone country. We worked with four Twitter data sets. Through
semi-manual opinion mining, ... | computer science |
16,474 | A Joint Identification Approach for Argumentative Writing Revisions | cs.CL | Prior work on revision identification typically uses a pipeline method:
revision extraction is first conducted to identify the locations of revisions
and revision classification is then conducted on the identified revisions. Such
a setting propagates the errors of the revision extraction step to the revision
classifica... | computer science |
16,475 | Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes
Based on Their Importance to Patients | cs.CL | Background: Electronic health record (EHR) notes contain abundant medical
jargon that can be difficult for patients to comprehend. One way to help
patients is to reduce information overload and help them focus on medical terms
that matter most to them.
Objective: The aim of this work was to develop FIT (Finding Impor... | computer science |
16,476 | Structural Embedding of Syntactic Trees for Machine Comprehension | cs.CL | Deep neural networks for machine comprehension typically utilizes only word
or character embeddings without explicitly taking advantage of structured
linguistic information such as constituency trees and dependency trees. In this
paper, we propose structural embedding of syntactic trees (SEST), an algorithm
framework t... | computer science |
16,477 | Lock-Free Parallel Perceptron for Graph-based Dependency Parsing | cs.CL | Dependency parsing is an important NLP task. A popular approach for
dependency parsing is structured perceptron. Still, graph-based dependency
parsing has the time complexity of $O(n^3)$, and it suffers from slow training.
To deal with this problem, we propose a parallel algorithm called parallel
perceptron. The parall... | computer science |
16,478 | A Comparative Study of Word Embeddings for Reading Comprehension | cs.CL | The focus of past machine learning research for Reading Comprehension tasks
has been primarily on the design of novel deep learning architectures. Here we
show that seemingly minor choices made on (1) the use of pre-trained word
embeddings, and (2) the representation of out-of-vocabulary tokens at test
time, can turn o... | computer science |
16,479 | Exponential Moving Average Model in Parallel Speech Recognition Training | cs.CL | As training data rapid growth, large-scale parallel training with multi-GPUs
cluster is widely applied in the neural network model learning currently.We
present a new approach that applies exponential moving average method in
large-scale parallel training of neural network model. It is a non-interference
strategy that ... | computer science |
16,480 | Lexical Resources for Hindi Marathi MT | cs.CL | In this paper we describe some ways to utilize various lexical resources to
improve the quality of statistical machine translation system. We have
augmented the training corpus with various lexical resources such as
IndoWordnet semantic relation set, function words, kridanta pairs and verb
phrases etc. Our research on ... | computer science |
16,481 | Word forms - not just their lengths- are optimized for efficient
communication | cs.CL | The inverse relationship between the length of a word and the frequency of
its use, first identified by G.K. Zipf in 1935, is a classic empirical law that
holds across a wide range of human languages. We demonstrate that length is one
aspect of a much more general property of words: how distinctive they are with
respec... | computer science |
16,482 | A Novel Comprehensive Approach for Estimating Concept Semantic
Similarity in WordNet | cs.CL | Computation of semantic similarity between concepts is an important
foundation for many research works. This paper focuses on IC computing methods
and IC measures, which estimate the semantic similarities between concepts by
exploiting the topological parameters of the taxonomy. Based on analyzing
representative IC com... | computer science |
16,483 | Performing Stance Detection on Twitter Data using Computational
Linguistics Techniques | cs.CL | As humans, we can often detect from a persons utterances if he or she is in
favor of or against a given target entity (topic, product, another person,
etc). But from the perspective of a computer, we need means to automatically
deduce the stance of the tweeter, given just the tweet text. In this paper, we
present our r... | computer science |
16,484 | Random vector generation of a semantic space | cs.CL | We show how random vectors and random projection can be implemented in the
usual vector space model to construct a Euclidean semantic space from a French
synonym dictionary. We evaluate theoretically the resulting noise and show the
experimental distribution of the similarities of terms in a neighborhood
according to t... | computer science |
16,485 | English Conversational Telephone Speech Recognition by Humans and
Machines | cs.CL | One of the most difficult speech recognition tasks is accurate recognition of
human to human communication. Advances in deep learning over the last few years
have produced major speech recognition improvements on the representative
Switchboard conversational corpus. Word error rates that just a few years ago
were 14% h... | computer science |
16,486 | Building a Syllable Database to Solve the Problem of Khmer Word
Segmentation | cs.CL | Word segmentation is a basic problem in natural language processing. With the
languages having the complex writing system like the Khmer language in Southern
of Vietnam, this problem really very intractable, posing the significant
challenges. Although there are some experts in Vietnam as well as international
having de... | computer science |
16,487 | Learning opacity in Stratal Maximum Entropy Grammar | cs.CL | Opaque phonological patterns are sometimes claimed to be difficult to learn;
specific hypotheses have been advanced about the relative difficulty of
particular kinds of opaque processes (Kiparsky 1971, 1973), and the kind of
data that will be helpful in learning an opaque pattern (Kiparsky 2000). In
this paper, we pres... | computer science |
16,488 | Linguistic Knowledge as Memory for Recurrent Neural Networks | cs.CL | Training recurrent neural networks to model long term dependencies is
difficult. Hence, we propose to use external linguistic knowledge as an
explicit signal to inform the model which memories it should utilize.
Specifically, external knowledge is used to augment a sequence with typed edges
between arbitrarily distant ... | computer science |
16,489 | A World of Difference: Divergent Word Interpretations among People | cs.CL | Divergent word usages reflect differences among people. In this paper, we
present a novel angle for studying word usage divergence -- word
interpretations. We propose an approach that quantifies semantic differences in
interpretations among different groups of people. The effectiveness of our
approach is validated by q... | computer science |
16,490 | Deep Learning applied to NLP | cs.CL | Convolutional Neural Network (CNNs) are typically associated with Computer
Vision. CNNs are responsible for major breakthroughs in Image Classification
and are the core of most Computer Vision systems today. More recently CNNs have
been applied to problems in Natural Language Processing and gotten some
interesting resu... | computer science |
16,491 | Detecting Sockpuppets in Deceptive Opinion Spam | cs.CL | This paper explores the problem of sockpuppet detection in deceptive opinion
spam using authorship attribution and verification approaches. Two methods are
explored. The first is a feature subsampling scheme that uses the KL-Divergence
on stylistic language models of an author to find discriminative features. The
secon... | computer science |
16,492 | Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small
Datasets | cs.CL | Sparsity is one of the major problems in natural language processing. The
problem becomes even more severe in agglutinating languages that are highly
prone to be inflected. We deal with sparsity in Turkish by adopting
morphological features for part-of-speech tagging. We learn inflectional and
derivational morpheme tag... | computer science |
16,493 | A Study of Metrics of Distance and Correlation Between Ranked Lists for
Compositionality Detection | cs.CL | Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between... | computer science |
16,494 | Comparison of SMT and RBMT; The Requirement of Hybridization for
Marathi-Hindi MT | cs.CL | We present in this paper our work on comparison between Statistical Machine
Translation (SMT) and Rule-based machine translation for translation from
Marathi to Hindi. Rule Based systems although robust take lots of time to
build. On the other hand statistical machine translation systems are easier to
create, maintain ... | computer science |
16,495 | Coping with Construals in Broad-Coverage Semantic Annotation of
Adpositions | cs.CL | We consider the semantics of prepositions, revisiting a broad-coverage
annotation scheme used for annotating all 4,250 preposition tokens in a 55,000
word corpus of English. Attempts to apply the scheme to adpositions and case
markers in other languages, as well as some problematic cases in English, have
led us to reco... | computer science |
16,496 | Effects of Limiting Memory Capacity on the Behaviour of Exemplar
Dynamics | cs.CL | Exemplar models are a popular class of models used to describe language
change. Here we study how limiting the memory capacity of an individual in
these models affects the system's behaviour. In particular we demonstrate the
effect this change has on the extinction of categories. Previous work in
exemplar dynamics has ... | computer science |
16,497 | Massive Exploration of Neural Machine Translation Architectures | cs.CL | Neural Machine Translation (NMT) has shown remarkable progress over the past
few years with production systems now being deployed to end-users. One major
drawback of current architectures is that they are expensive to train,
typically requiring days to weeks of GPU time to converge. This makes
exhaustive hyperparameter... | computer science |
16,498 | Automated Hate Speech Detection and the Problem of Offensive Language | cs.CL | A key challenge for automatic hate-speech detection on social media is the
separation of hate speech from other instances of offensive language. Lexical
detection methods tend to have low precision because they classify all messages
containing particular terms as hate speech and previous work using supervised
learning ... | computer science |
16,499 | Why we have switched from building full-fledged taxonomies to simply
detecting hypernymy relations | cs.CL | The study of taxonomies and hypernymy relations has been extensive on the
Natural Language Processing (NLP) literature. However, the evaluation of
taxonomy learning approaches has been traditionally troublesome, as it mainly
relies on ad-hoc experiments which are hardly reproducible and manually
expensive. Partly becau... | computer science |
16,500 | MetaPAD: Meta Pattern Discovery from Massive Text Corpora | cs.CL | Mining textual patterns in news, tweets, papers, and many other kinds of text
corpora has been an active theme in text mining and NLP research. Previous
studies adopt a dependency parsing-based pattern discovery approach. However,
the parsing results lose rich context around entities in the patterns, and the
process is... | computer science |
16,501 | Story Cloze Ending Selection Baselines and Data Examination | cs.CL | This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model ... | computer science |
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