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17,602 | Novel Ranking-Based Lexical Similarity Measure for Word Embedding | cs.CL | Distributional semantics models derive word space from linguistic items in
context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. In
this paper we provide a guideline for post process improvements to the baseline
vectors. We ... | computer science |
17,603 | Are words easier to learn from infant- than adult-directed speech? A
quantitative corpus-based investigation | cs.CL | We investigate whether infant-directed speech (IDS) could facilitate word
form learning when compared to adult-directed speech (ADS). To study this, we
examine the distribution of word forms at two levels, acoustic and
phonological, using a large database of spontaneous speech in Japanese. At the
acoustic level we show... | computer science |
17,604 | A Framework for Enriching Lexical Semantic Resources with Distributional
Semantics | cs.CL | We present an approach to combining distributional semantic representations
induced from text corpora with manually constructed lexical-semantic networks.
While both kinds of semantic resources are available with high lexical
coverage, our aligned resource combines the domain specificity and availability
of contextual ... | computer science |
17,605 | Dual Long Short-Term Memory Networks for Sub-Character Representation
Learning | cs.CL | Characters have commonly been regarded as the minimal processing unit in
Natural Language Processing (NLP). But many non-latin languages have
hieroglyphic writing systems, involving a big alphabet with thousands or
millions of characters. Each character is composed of even smaller parts, which
are often ignored by the ... | computer science |
17,606 | Building a Sentiment Corpus of Tweets in Brazilian Portuguese | cs.CL | The large amount of data available in social media, forums and websites
motivates researches in several areas of Natural Language Processing, such as
sentiment analysis. The popularity of the area due to its subjective and
semantic characteristics motivates research on novel methods and approaches for
classification. H... | computer science |
17,607 | Semi-automatic definite description annotation: a first report | cs.CL | Studies in Referring Expression Generation (REG) often make use of corpora of
definite descriptions produced by human subjects in controlled experiments.
Experiments of this kind, which are essential for the study of reference
phenomena and many others, may however include a considerable amount of noise.
Human subjects... | computer science |
17,608 | Generative Adversarial Nets for Multiple Text Corpora | cs.CL | Generative adversarial nets (GANs) have been successfully applied to the
artificial generation of image data. In terms of text data, much has been done
on the artificial generation of natural language from a single corpus. We
consider multiple text corpora as the input data, for which there can be two
applications of G... | computer science |
17,609 | Actionable Email Intent Modeling with Reparametrized RNNs | cs.CL | Emails in the workplace are often intentional calls to action for its
recipients. We propose to annotate these emails for what action its recipient
will take. We argue that our approach of action-based annotation is more
scalable and theory-agnostic than traditional speech-act-based email intent
annotation, while still... | computer science |
17,610 | Mapping to Declarative Knowledge for Word Problem Solving | cs.CL | Math word problems form a natural abstraction to a range of quantitative
reasoning problems, such as understanding financial news, sports results, and
casualties of war. Solving such problems requires the understanding of several
mathematical concepts such as dimensional analysis, subset relationships, etc.
In this pap... | computer science |
17,611 | Advances in Pre-Training Distributed Word Representations | cs.CL | Many Natural Language Processing applications nowadays rely on pre-trained
word representations estimated from large text corpora such as news
collections, Wikipedia and Web Crawl. In this paper, we show how to train
high-quality word vector representations by using a combination of known tricks
that are however rarely... | computer science |
17,612 | A Gap-Based Framework for Chinese Word Segmentation via Very Deep
Convolutional Networks | cs.CL | Most previous approaches to Chinese word segmentation can be roughly
classified into character-based and word-based methods. The former regards this
task as a sequence-labeling problem, while the latter directly segments
character sequence into words. However, if we consider segmenting a given
sentence, the most intuit... | computer science |
17,613 | Improving Text Normalization by Optimizing Nearest Neighbor Matching | cs.CL | Text normalization is an essential task in the processing and analysis of
social media that is dominated with informal writing. It aims to map informal
words to their intended standard forms. Previously proposed text normalization
approaches typically require manual selection of parameters for improved
performance. In ... | computer science |
17,614 | A Syntactic Approach to Domain-Specific Automatic Question Generation | cs.CL | Factoid questions are questions that require short fact-based answers.
Automatic generation (AQG) of factoid questions from a given text can
contribute to educational activities, interactive question answering systems,
search engines, and other applications. The goal of our research is to generate
factoid source-questi... | computer science |
17,615 | Disentangled Representations for Manipulation of Sentiment in Text | cs.CL | The ability to change arbitrary aspects of a text while leaving the core
message intact could have a strong impact in fields like marketing and politics
by enabling e.g. automatic optimization of message impact and personalized
language adapted to the receiver's profile. In this paper we take a first step
towards such ... | computer science |
17,616 | Scalable Multi-Domain Dialogue State Tracking | cs.CL | Dialogue state tracking (DST) is a key component of task-oriented dialogue
systems. DST estimates the user's goal at each user turn given the interaction
until then. State of the art approaches for state tracking rely on deep
learning methods, and represent dialogue state as a distribution over all
possible slot values... | computer science |
17,617 | Personal Names in Modern Turkey | cs.CL | We analyzed the most common 5000 male and 5000 female Turkish names based on
their etymological, morphological, and semantic attributes. The name statistics
are based on all Turkish citizens who were alive in 2014 and they cover 90% of
all population. To the best of our knowledge, this study is the most
comprehensive d... | computer science |
17,618 | The CAPIO 2017 Conversational Speech Recognition System | cs.CL | In this paper we show how we have achieved the state-of-the-art performance
on the industry-standard NIST 2000 Hub5 English evaluation set. We explore
densely connected LSTMs, inspired by the densely connected convolutional
networks recently introduced for image classification tasks. We also propose an
acoustic model a... | computer science |
17,619 | Bidirectional Attention for SQL Generation | cs.CL | Generating structural query language (SQL) queries from natural language is a
long-standing open problem. Answering a natural language question about a
database table requires modeling complex interactions between the columns of
the table and the question. It has been attracting considerable interest
recently and drive... | computer science |
17,620 | A New Approach for Measuring Sentiment Orientation based on
Multi-Dimensional Vector Space | cs.CL | This study implements a vector space model approach to measure the sentiment
orientations of words. Two representative vectors for positive/negative
polarity are constructed using high-dimensional vec-tor space in both an
unsupervised and a semi-supervised manner. A sentiment ori-entation value per
word is determined b... | computer science |
17,621 | PronouncUR: An Urdu Pronunciation Lexicon Generator | cs.CL | State-of-the-art speech recognition systems rely heavily on three basic
components: an acoustic model, a pronunciation lexicon and a language model. To
build these components, a researcher needs linguistic as well as technical
expertise, which is a barrier in low-resource domains. Techniques to construct
these three co... | computer science |
17,622 | Sanskrit Sandhi Splitting using $\pmb{seq2(seq)^2}$ | cs.CL | In Sanskrit, small words (morphemes) are combined through a
morphophonological process called Sandhi to form compound words. Sandhi
splitting is the process of splitting a given compound word into its
constituent morphemes. Although rules governing the splitting of words exist,
it is highly challenging to identify the ... | computer science |
17,623 | Learning Multimodal Word Representation via Dynamic Fusion Methods | cs.CL | Multimodal models have been proven to outperform text-based models on
learning semantic word representations. Almost all previous multimodal models
typically treat the representations from different modalities equally. However,
it is obvious that information from different modalities contributes
differently to the mean... | computer science |
17,624 | An Attentive Sequence Model for Adverse Drug Event Extraction from
Biomedical Text | cs.CL | Adverse reaction caused by drugs is a potentially dangerous problem which may
lead to mortality and morbidity in patients. Adverse Drug Event (ADE)
extraction is a significant problem in biomedical research. We model ADE
extraction as a Question-Answering problem and take inspiration from Machine
Reading Comprehension ... | computer science |
17,625 | Identifying emergency stages in Facebook posts of police departments
with convolutional and recurrent neural networks and support vector machines | cs.CL | Classification of social media posts in emergency response is an important
practical problem: accurate classification can help automate processing of such
messages and help other responders and the public react to emergencies in a
timely fashion. This research focused on classifying Facebook messages of US
police depar... | computer science |
17,626 | Social Media Analysis based on Semanticity of Streaming and Batch Data | cs.CL | Languages shared by people differ in different regions based on their
accents, pronunciation and word usages. In this era sharing of language takes
place mainly through social media and blogs. Every second swing of such a micro
posts exist which induces the need of processing those micro posts, in-order to
extract know... | computer science |
17,627 | VnCoreNLP: A Vietnamese Natural Language Processing Toolkit | cs.CL | We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP
annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language
processing (NLP) tasks including word segmentation, part-of-speech (POS)
tagging, named entity recognition (NER) and dependency parsing, and obtains
state-of-the-art ... | computer science |
17,628 | A Multi-task Learning Approach for Improving Product Title Compression
with User Search Log Data | cs.CL | It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Tradi... | computer science |
17,629 | Towards Understanding and Answering Multi-Sentence Recommendation
Questions on Tourism | cs.CL | We introduce the first system towards the novel task of answering complex
multisentence recommendation questions in the tourism domain. Our solution uses
a pipeline of two modules: question understanding and answering. For question
understanding, we define an SQL-like query language that captures the semantic
intent of... | computer science |
17,630 | Using reinforcement learning to learn how to play text-based games | cs.CL | The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, sinc... | computer science |
17,631 | Explorations in an English Poetry Corpus: A Neurocognitive Poetics
Perspective | cs.CL | This paper describes a corpus of about 3000 English literary texts with about
250 million words extracted from the Gutenberg project that span a range of
genres from both fiction and non-fiction written by more than 130 authors
(e.g., Darwin, Dickens, Shakespeare). Quantitative Narrative Analysis (QNA) is
used to explo... | computer science |
17,632 | Analysis of Wikipedia-based Corpora for Question Answering | cs.CL | This paper gives comprehensive analyses of corpora based on Wikipedia for
several tasks in question answering. Four recent corpora are collected,WikiQA,
SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual
similarities, question types, and answer categories. These corpora are then
analyzed extrinsic... | computer science |
17,633 | MIZAN: A Large Persian-English Parallel Corpus | cs.CL | One of the most major and essential tasks in natural language processing is
machine translation that is now highly dependent upon multilingual parallel
corpora. Through this paper, we introduce the biggest Persian-English parallel
corpus with more than one million sentence pairs collected from masterpieces of
literatur... | computer science |
17,634 | Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment
Identification | cs.CL | Multilingual speakers often switch between languages to express themselves on
social communication platforms. Sometimes, the original script of the language
is preserved, while using a common script for all the languages is quite
popular as well due to convenience. On such occasions, multiple languages are
being mixed ... | computer science |
17,635 | Denotation Extraction for Interactive Learning in Dialogue Systems | cs.CL | This paper presents a novel task using real user data obtained in
human-machine conversation. The task concerns with denotation extraction from
answer hints collected interactively in a dialogue. The task is motivated by
the need for large amounts of training data for question answering dialogue
system development, whe... | computer science |
17,636 | Translating Pro-Drop Languages with Reconstruction Models | cs.CL | Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. To date, very little attention has been paid to the
dropped pronoun (DP) problem within neural machine translation (NMT). In this
work, we propose ... | computer science |
17,637 | MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection | cs.CL | We introduce MilkQA, a question answering dataset from the dairy domain
dedicated to the study of consumer questions. The dataset contains 2,657 pairs
of questions and answers, written in the Portuguese language and originally
collected by the Brazilian Agricultural Research Corporation (Embrapa). All
questions were mo... | computer science |
17,638 | Discrete symbolic optimization and Boltzmann sampling by continuous
neural dynamics: Gradient Symbolic Computation | cs.CL | Gradient Symbolic Computation is proposed as a means of solving discrete
global optimization problems using a neurally plausible continuous stochastic
dynamical system. Gradient symbolic dynamics involves two free parameters that
must be adjusted as a function of time to obtain the global maximizer at the
end of the co... | computer science |
17,639 | Group Communication Analysis: A Computational Linguistics Approach for
Detecting Sociocognitive Roles in Multi-Party Interactions | cs.CL | Roles are one of the most important concepts in understanding human
sociocognitive behavior. During group interactions, members take on different
roles within the discussion. Roles have distinct patterns of behavioral
engagement (i.e., active or passive, leading or following), contribution
characteristics (i.e., provid... | computer science |
17,640 | Unsupervised Part-of-Speech Induction | cs.CL | Part-of-Speech (POS) tagging is an old and fundamental task in natural
language processing. While supervised POS taggers have shown promising
accuracy, it is not always feasible to use supervised methods due to lack of
labeled data. In this project, we attempt to unsurprisingly induce POS tags by
iteratively looking fo... | computer science |
17,641 | SEE: Syntax-aware Entity Embedding for Neural Relation Extraction | cs.CL | Distant supervised relation extraction is an efficient approach to scale
relation extraction to very large corpora, and has been widely used to find
novel relational facts from plain text. Recent studies on neural relation
extraction have shown great progress on this task via modeling the sentences in
low-dimensional s... | computer science |
17,642 | Improved English to Russian Translation by Neural Suffix Prediction | cs.CL | Neural machine translation (NMT) suffers a performance deficiency when a
limited vocabulary fails to cover the source or target side adequately, which
happens frequently when dealing with morphologically rich languages. To address
this problem, previous work focused on adjusting translation granularity or
expanding the... | computer science |
17,643 | Did William Shakespeare and Thomas Kyd Write Edward III? | cs.CL | William Shakespeare is believed to be a significant author in the anonymous
play, The Reign of King Edward III, published in 1596. However, recently,
Thomas Kyd, has been suggested as the primary author. Using a neurolinguistics
approach to authorship identification we use a four-feature technique, RPAS, to
convert the... | computer science |
17,644 | EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings | cs.CL | Keyphrase extraction is the task of automatically selecting a small set of
phrases that best describe a given free text document. Keyphrases can be used
for indexing, searching, aggregating and summarizing text documents, serving
many automatic as well as human-facing use cases. Existing supervised systems
for keyphras... | computer science |
17,645 | An Interpretable Reasoning Network for Multi-Relation Question Answering | cs.CL | Multi-relation Question Answering is a challenging task, due to the
requirement of elaborated analysis on questions and reasoning over multiple
fact triples in knowledge base. In this paper, we present a novel model called
Interpretable Reasoning Network that employs an interpretable, hop-by-hop
reasoning process for q... | computer science |
17,646 | What Level of Quality can Neural Machine Translation Attain on Literary
Text? | cs.CL | Given the rise of a new approach to MT, Neural MT (NMT), and its promising
performance on different text types, we assess the translation quality it can
attain on what is perceived to be the greatest challenge for MT: literary text.
Specifically, we target novels, arguably the most popular type of literary
text. We bui... | computer science |
17,647 | Variational Recurrent Neural Machine Translation | cs.CL | Partially inspired by successful applications of variational recurrent neural
networks, we propose a novel variational recurrent neural machine translation
(VRNMT) model in this paper. Different from the variational NMT, VRNMT
introduces a series of latent random variables to model the translation
procedure of a senten... | computer science |
17,648 | Asynchronous Bidirectional Decoding for Neural Machine Translation | cs.CL | The dominant neural machine translation (NMT) models apply unified
attentional encoder-decoder neural networks for translation. Traditionally, the
NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a
left-toright manner, leaving the target-side contexts generated from right to
left unexploite... | computer science |
17,649 | Adversarial Learning for Chinese NER from Crowd Annotations | cs.CL | To quickly obtain new labeled data, we can choose crowdsourcing as an
alternative way at lower cost in a short time. But as an exchange, crowd
annotations from non-experts may be of lower quality than those from experts.
In this paper, we propose an approach to performing crowd annotation learning
for Chinese Named Ent... | computer science |
17,650 | OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language
Understanding | cs.CL | In practice, most spoken language understanding systems process user input in
a pipelined manner; first domain is predicted, then intent and semantic slots
are inferred according to the semantic frames of the predicted domain. The
pipeline approach, however, has some disadvantages: error propagation and lack
of informa... | computer science |
17,651 | Contextual and Position-Aware Factorization Machines for Sentiment
Classification | cs.CL | While existing machine learning models have achieved great success for
sentiment classification, they typically do not explicitly capture
sentiment-oriented word interaction, which can lead to poor results for
fine-grained analysis at the snippet level (a phrase or sentence).
Factorization Machine provides a possible a... | computer science |
17,652 | Investigating the Working of Text Classifiers | cs.CL | Text classification is one of the most widely studied task in natural
language processing. Recently, larger and larger multilayer neural network
models are employed for the task motivated by the principle of
compositionality. Almost all of the methods reported use discriminative
approaches for the task. Discriminative ... | computer science |
17,653 | Size vs. Structure in Training Corpora for Word Embedding Models:
Araneum Russicum Maximum and Russian National Corpus | cs.CL | In this paper, we present a distributional word embedding model trained on
one of the largest available Russian corpora: Araneum Russicum Maximum (over 10
billion words crawled from the web). We compare this model to the model trained
on the Russian National Corpus (RNC). The two corpora are much different in
their siz... | computer science |
17,654 | Evaluating neural network explanation methods using hybrid documents and
morphological prediction | cs.CL | We propose two novel paradigms for evaluating neural network explanations in
NLP. The first paradigm works on hybrid documents, the second exploits
morphosyntactic agreements. Neither paradigm requires manual annotations;
instead, a relevance ground truth is generated automatically. In our
experiments, successful expla... | computer science |
17,655 | A Resource-Light Method for Cross-Lingual Semantic Textual Similarity | cs.CL | Recognizing semantically similar sentences or paragraphs across languages is
beneficial for many tasks, ranging from cross-lingual information retrieval and
plagiarism detection to machine translation. Recently proposed methods for
predicting cross-lingual semantic similarity of short texts, however, make use
of tools ... | computer science |
17,656 | A Practitioners' Guide to Transfer Learning for Text Classification
using Convolutional Neural Networks | cs.CL | Transfer Learning (TL) plays a crucial role when a given dataset has
insufficient labeled examples to train an accurate model. In such scenarios,
the knowledge accumulated within a model pre-trained on a source dataset can be
transferred to a target dataset, resulting in the improvement of the target
model. Though TL i... | computer science |
17,657 | Efficient Text Classification Using Tree-structured Multi-linear
Principal Component Analysis | cs.CL | A novel text data dimension reduction technique, called the tree-structured
multi-linear principal component anal- ysis (TMPCA), is proposed in this work.
Being different from traditional text dimension reduction methods that deal
with the word-level representation, the TMPCA technique reduces the dimension
of input se... | computer science |
17,658 | Building an Ellipsis-aware Chinese Dependency Treebank for Web Text | cs.CL | Web 2.0 has brought with it numerous user-produced data revealing one's
thoughts, experiences, and knowledge, which are a great source for many tasks,
such as information extraction, and knowledge base construction. However, the
colloquial nature of the texts poses new challenges for current natural
language processing... | computer science |
17,659 | Attentive Recurrent Tensor Model for Community Question Answering | cs.CL | A major challenge to the problem of community question answering is the
lexical and semantic gap between the sentence representations. Some solutions
to minimize this gap includes the introduction of extra parameters to deep
models or augmenting the external handcrafted features. In this paper, we
propose a novel atten... | computer science |
17,660 | A Universal Semantic Space | cs.CL | Multilingual embeddings build on the success of monolingual embeddings and
have applications in crosslingual transfer, in machine translation and in the
digital humanities. We present the first multilingual embedding space for
thousands of languages, a much larger number of languages than in prior work. | computer science |
17,661 | Neural Multi-task Learning in Automated Assessment | cs.CL | Grammatical error detection and automated essay scoring are two tasks in the
area of automated assessment. Traditionally these tasks have been treated
independently with different machine learning models and features used for each
task. In this paper, we develop a multi-task neural network model that jointly
optimises ... | computer science |
17,662 | BiographyNet: Extracting Relations Between People and Events | cs.CL | This paper describes BiographyNet, a digital humanities project (2012-2016)
that brings together researchers from history, computational linguistics and
computer science. The project uses data from the Biography Portal of the
Netherlands (BPN), which contains approximately 125,000 biographies from a
variety of Dutch bi... | computer science |
17,663 | Unsupervised Open Relation Extraction | cs.CL | We explore methods to extract relations between named entities from free text
in an unsupervised setting. In addition to standard feature extraction, we
develop a novel method to re-weight word embeddings. We alleviate the problem
of features sparsity using an individual feature reduction. Our approach
exhibits a signi... | computer science |
17,664 | Siamese Neural Networks with Random Forest for detecting duplicate
question pairs | cs.CL | Determining whether two given questions are semantically similar is a fairly
challenging task given the different structures and forms that the questions
can take. In this paper, we use Gated Recurrent Units(GRU) in combination with
other highly used machine learning algorithms like Random Forest, Adaboost and
SVM for ... | computer science |
17,665 | Assertion-based QA with Question-Aware Open Information Extraction | cs.CL | We present assertion based question answering (ABQA), an open domain question
answering task that takes a question and a passage as inputs, and outputs a
semi-structured assertion consisting of a subject, a predicate and a list of
arguments. An assertion conveys more evidences than a short answer span in
reading compre... | computer science |
17,666 | What did you Mention? A Large Scale Mention Detection Benchmark for
Spoken and Written Text | cs.CL | We describe a large, high-quality benchmark for the evaluation of Mention
Detection tools. The benchmark contains annotations of both named entities as
well as other types of entities, annotated on different types of text, ranging
from clean text taken from Wikipedia, to noisy spoken data. The benchmark was
built throu... | computer science |
17,667 | Query Focused Abstractive Summarization: Incorporating Query Relevance,
Multi-Document Coverage, and Summary Length Constraints into seq2seq Models | cs.CL | Query Focused Summarization (QFS) has been addressed mostly using extractive
methods. Such methods, however, produce text which suffers from low coherence.
We investigate how abstractive methods can be applied to QFS, to overcome such
limitations. Recent developments in neural-attention based sequence-to-sequence
model... | computer science |
17,668 | SentiPers: A Sentiment Analysis Corpus for Persian | cs.CL | Sentiment Analysis (SA) is a major field of study in natural language
processing, computational linguistics and information retrieval. Interest in SA
has been constantly growing in both academia and industry over the recent
years. Moreover, there is an increasing need for generating appropriate
resources and datasets i... | computer science |
17,669 | HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments | cs.CL | The science of happiness is an area of positive psychology concerned with
understanding what behaviors make people happy in a sustainable fashion.
Recently, there has been interest in developing technologies that help
incorporate the findings of the science of happiness into users' daily lives by
steering them towards ... | computer science |
17,670 | Evaluating Layers of Representation in Neural Machine Translation on
Part-of-Speech and Semantic Tagging Tasks | cs.CL | While neural machine translation (NMT) models provide improved translation
quality in an elegant, end-to-end framework, it is less clear what they learn
about language. Recent work has started evaluating the quality of vector
representations learned by NMT models on morphological and syntactic tasks. In
this paper, we ... | computer science |
17,671 | Vietnamese Open Information Extraction | cs.CL | Open information extraction (OIE) is the process to extract relations and
their arguments automatically from textual documents without the need to
restrict the search to predefined relations. In recent years, several OIE
systems for the English language have been created but there is not any system
for the Vietnamese l... | computer science |
17,672 | A Question-Focused Multi-Factor Attention Network for Question Answering | cs.CL | Neural network models recently proposed for question answering (QA) primarily
focus on capturing the passage-question relation. However, they have minimal
capability to link relevant facts distributed across multiple sentences which
is crucial in achieving deeper understanding, such as performing multi-sentence
reasoni... | computer science |
17,673 | Continuous Space Reordering Models for Phrase-based MT | cs.CL | Bilingual sequence models improve phrase-based translation and reordering by
overcoming phrasal independence assumption and handling long range reordering.
However, due to data sparsity, these models often fall back to very small
context sizes. This problem has been previously addressed by learning sequences
over gener... | computer science |
17,674 | A Multilayer Convolutional Encoder-Decoder Neural Network for
Grammatical Error Correction | cs.CL | We improve automatic correction of grammatical, orthographic, and collocation
errors in text using a multilayer convolutional encoder-decoder neural network.
The network is initialized with embeddings that make use of character N-gram
information to better suit this task. When evaluated on common benchmark test
data se... | computer science |
17,675 | A Formal Definition of Importance for Summarization | cs.CL | Research on summarization has mainly been driven by empirical approaches,
crafting systems to perform well on standard datasets with the notion of
information Importance remaining latent. We argue that establishing formal
theories of Importance will advance our understanding of the task and further
improve summarizatio... | computer science |
17,676 | Exploration on Generating Traditional Chinese Medicine Prescription from
Symptoms with an End-to-End method | cs.CL | Traditional Chinese Medicine (TCM) is an influential form of medical
treatment in China and surrounding areas. In this paper, we propose a TCM
prescription generation task that aims to automatically generate a herbal
medicine prescription based on textual symptom descriptions.
Sequence-to-sequence (seq2seq) model has b... | computer science |
17,677 | Improving Word Vector with Prior Knowledge in Semantic Dictionary | cs.CL | Using low dimensional vector space to represent words has been very effective
in many NLP tasks. However, it doesn't work well when faced with the problem of
rare and unseen words. In this paper, we propose to leverage the knowledge in
semantic dictionary in combination with some morphological information to build
an e... | computer science |
17,678 | A Sheaf Model of Contradictions and Disagreements. Preliminary Report
and Discussion | cs.CL | We introduce a new formal model -- based on the mathematical construct of
sheaves -- for representing contradictory information in textual sources. This
model has the advantage of letting us (a) identify the causes of the
inconsistency; (b) measure how strong it is; (c) and do something about it,
e.g. suggest ways to r... | computer science |
17,679 | Combining Convolution and Recursive Neural Networks for Sentiment
Analysis | cs.CL | This paper addresses the problem of sentence-level sentiment analysis. In
recent years, Convolution and Recursive Neural Networks have been proven to be
effective network architecture for sentence-level sentiment analysis.
Nevertheless, each of them has their own potential drawbacks. For alleviating
their weaknesses, w... | computer science |
17,680 | A Survey of Word Embeddings Evaluation Methods | cs.CL | Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive ove... | computer science |
17,681 | Helping Crisis Responders Find the Informative Needle in the Tweet
Haystack | cs.CL | Crisis responders are increasingly using social media, data and other digital
sources of information to build a situational understanding of a crisis
situation in order to design an effective response. However with the increased
availability of such data, the challenge of identifying relevant information
from it also i... | computer science |
17,682 | A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts | cs.CL | Motivated by a project to create a system for people who are deaf or
hard-of-hearing that would use automatic speech recognition (ASR) to produce
real-time text captions of spoken English during in-person meetings with
hearing individuals, we have augmented a transcript of the Switchboard
conversational dialogue corpus... | computer science |
17,683 | A State-of-the-Art of Semantic Change Computation | cs.CL | This paper reviews the state-of-the-art of semantic change computation, one
emerging research field in computational linguistics, proposing a framework
that summarizes the literature by identifying and expounding five essential
components in the field: diachronic corpus, diachronic word sense
characterization, change m... | computer science |
17,684 | An Attention-Based Word-Level Interaction Model: Relation Detection for
Knowledge Base Question Answering | cs.CL | Relation detection plays a crucial role in Knowledge Base Question Answering
(KBQA) because of the high variance of relation expression in the question.
Traditional deep learning methods follow an encoding-comparing paradigm, where
the question and the candidate relation are represented as vectors to compare
their sema... | computer science |
17,685 | Pilot study for the COST Action "Reassembling the Republic of Letters":
language-driven network analysis of letters from the Hartlib's Papers | cs.CL | The present report summarizes an exploratory study which we carried out in
the context of the COST Action IS1310 "Reassembling the Republic of Letters,
1500-1800", and which is relevant to the activities of Working Group 3 "Texts
and Topics" and Working Group 2 "People and Networks". In this study we
investigated the u... | computer science |
17,686 | PEYMA: A Tagged Corpus for Persian Named Entities | cs.CL | The goal in the NER task is to classify proper nouns of a text into classes
such as person, location, and organization. This is an important preprocessing
step in many NLP tasks such as question-answering and summarization. Although
many research studies have been conducted in this area in English and the
state-of-the-... | computer science |
17,687 | Generating Wikipedia by Summarizing Long Sequences | cs.CL | We show that generating English Wikipedia articles can be approached as a
multi- document summarization of source documents. We use extractive
summarization to coarsely identify salient information and a neural abstractive
model to generate the article. For the abstractive model, we introduce a
decoder-only architectur... | computer science |
17,688 | Paraphrase-Supervised Models of Compositionality | cs.CL | Compositional vector space models of meaning promise new solutions to
stubborn language understanding problems. This paper makes two contributions
toward this end: (i) it uses automatically-extracted paraphrase examples as a
source of supervision for training compositional models, replacing previous
work which relied o... | computer science |
17,689 | Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention
for Sequence Modeling | cs.CL | Many natural language processing tasks solely rely on sparse dependencies
between a few tokens in a sentence. Soft attention mechanisms show promising
performance in modeling local/global dependencies by soft probabilities between
every two tokens, but they are not effective and efficient when applied to long
sentences... | computer science |
17,690 | Complex Sequential Question Answering: Towards Learning to Converse Over
Linked Question Answer Pairs with a Knowledge Graph | cs.CL | While conversing with chatbots, humans typically tend to ask many questions,
a significant portion of which can be answered by referring to large-scale
knowledge graphs (KG). While Question Answering (QA) and dialog systems have
been studied independently, there is a need to study them closely to evaluate
such real-wor... | computer science |
17,691 | Adapting predominant and novel sense discovery algorithms for
identifying corpus-specific sense differences | cs.CL | Word senses are not static and may have temporal, spatial or corpus-specific
scopes. Identifying such scopes might benefit the existing WSD systems largely.
In this paper, while studying corpus specific word senses, we adapt three
existing predominant and novel-sense discovery algorithms to identify these
corpus-specif... | computer science |
17,692 | Emerging Language Spaces Learned From Massively Multilingual Corpora | cs.CL | Translations capture important information about languages that can be used
as implicit supervision in learning linguistic properties and semantic
representations. In an information-centric view, translated texts may be
considered as semantic mirrors of the original text and the significant
variations that we can obser... | computer science |
17,693 | Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer
Learning | cs.CL | Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialo... | computer science |
17,694 | Submodularity-inspired Data Selection for Goal-oriented Chatbot Training
based on Sentence Embeddings | cs.CL | Goal-oriented (GO) dialogue systems rely on an initial natural language
understanding (NLU) module to determine the user's intention and parameters
thereof - also known as slots. Since the systems, also known as bots, help the
users with solving problems in relatively narrow domains, they require training
data within t... | computer science |
17,695 | Order matters: Distributional properties of speech to young children
bootstraps learning of semantic representations | cs.CL | Some researchers claim that language acquisition is critically dependent on
experiencing linguistic input in order of increasing complexity. We set out to
test this hypothesis using a simple recurrent neural network (SRN) trained to
predict word sequences in CHILDES, a 5-million-word corpus of speech directed
to childr... | computer science |
17,696 | Densely Connected Bidirectional LSTM with Applications to Sentence
Classification | cs.CL | Deep neural networks have recently been shown to achieve highly competitive
performance in many computer vision tasks due to their abilities of exploring
in a much larger hypothesis space. However, since most deep architectures like
stacked RNNs tend to suffer from the vanishing-gradient and overfitting
problems, their... | computer science |
17,697 | Left-Center-Right Separated Neural Network for Aspect-based Sentiment
Analysis with Rotatory Attention | cs.CL | Deep learning techniques have achieved success in aspect-based sentiment
analysis in recent years. However, there are two important issues that still
remain to be further studied, i.e., 1) how to efficiently represent the target
especially when the target contains multiple words; 2) how to utilize the
interaction betwe... | computer science |
17,698 | DeepType: Multilingual Entity Linking by Neural Type System Evolution | cs.CL | The wealth of structured (e.g. Wikidata) and unstructured data about the
world available today presents an incredible opportunity for tomorrow's
Artificial Intelligence. So far, integration of these two different modalities
is a difficult process, involving many decisions concerning how best to
represent the informatio... | computer science |
17,699 | Heuristic Feature Selection for Clickbait Detection | cs.CL | We study feature selection as a means to optimize the baseline clickbait
detector employed at the Clickbait Challenge 2017. The challenge's task is to
score the "clickbaitiness" of a given Twitter tweet on a scale from 0 (no
clickbait) to 1 (strong clickbait). Unlike most other approaches submitted to
the challenge, th... | computer science |
17,700 | Semantic projection: recovering human knowledge of multiple, distinct
object features from word embeddings | cs.CL | The words of a language reflect the structure of the human mind, allowing us
to transmit thoughts between individuals. However, language can represent only
a subset of our rich and detailed cognitive architecture. Here, we ask what
kinds of common knowledge (semantic memory) are captured by word meanings
(lexical seman... | computer science |
17,701 | Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN
models | cs.CL | Text mining the relations between chemicals and proteins is an increasingly
important task. The CHEMPROT track at BioCreative VI aims to promote the
development and evaluation of systems that can automatically detect the
chemical-protein relations in running text (PubMed abstracts). This manuscript
describes our submis... | computer science |
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