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16,202 | Improving historical spelling normalization with bi-directional LSTMs
and multi-task learning | cs.CL | Natural-language processing of historical documents is complicated by the
abundance of variant spellings and lack of annotated data. A common approach is
to normalize the spelling of historical words to modern forms. We explore the
suitability of a deep neural network architecture for this task, particularly a
deep bi-... | computer science |
16,203 | Sequence Segmentation Using Joint RNN and Structured Prediction Models | cs.CL | We describe and analyze a simple and effective algorithm for sequence
segmentation applied to speech processing tasks. We propose a neural
architecture that is composed of two modules trained jointly: a recurrent
neural network (RNN) module and a structured prediction model. The RNN outputs
are considered as feature fu... | computer science |
16,204 | Statistical Machine Translation for Indian Languages: Mission Hindi 2 | cs.CL | This paper presents Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to NLP Tools Contest on Statistical Machine Translation in
Indian Languages (ILSMT) 2015 (collocated with ICON 2015). The aim of the
contest was to collectively explore the effectiveness of Statistical Machine
Translation (SMT)... | computer science |
16,205 | Content Selection in Data-to-Text Systems: A Survey | cs.CL | Data-to-text systems are powerful in generating reports from data
automatically and thus they simplify the presentation of complex data. Rather
than presenting data using visualisation techniques, data-to-text systems use
natural (human) language, which is the most common way for human-human
communication. In addition,... | computer science |
16,206 | Broad Context Language Modeling as Reading Comprehension | cs.CL | Progress in text understanding has been driven by large datasets that test
particular capabilities, like recent datasets for reading comprehension
(Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al.,
2016), a word prediction task requiring broader context than the immediate
sentence. We view LA... | computer science |
16,207 | Distraction-Based Neural Networks for Document Summarization | cs.CL | Distributed representation learned with neural networks has recently shown to
be effective in modeling natural languages at fine granularities such as words,
phrases, and even sentences. Whether and how such an approach can be extended
to help model larger spans of text, e.g., documents, is intriguing, and further
inve... | computer science |
16,208 | Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept
Embeddings | cs.CL | Biomedical word sense disambiguation (WSD) is an important intermediate task
in many natural language processing applications such as named entity
recognition, syntactic parsing, and relation extraction. In this paper, we
employ knowledge-based approaches that also exploit recent advances in neural
word/concept embeddi... | computer science |
16,209 | CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional
Method for the Identification of Semantic Relations | cs.CL | We present a submission to the CogALex 2016 shared task on the corpus-based
identification of semantic relations, using LexNET (Shwartz and Dagan, 2016),
an integrated path-based and distributional method for semantic relation
classification. The reported results in the shared task bring this submission
to the third pl... | computer science |
16,210 | A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural
Networks | cs.CL | Sarcasm detection is a key task for many natural language processing tasks.
In sentiment analysis, for example, sarcasm can flip the polarity of an
"apparently positive" sentence and, hence, negatively affect polarity detection
performance. To date, most approaches to sarcasm detection have treated the
task primarily a... | computer science |
16,211 | Ex Machina: Personal Attacks Seen at Scale | cs.CL | The damage personal attacks cause to online discourse motivates many
platforms to try to curb the phenomenon. However, understanding the prevalence
and impact of personal attacks in online platforms at scale remains
surprisingly difficult. The contribution of this paper is to develop and
illustrate a method that combin... | computer science |
16,212 | Representation Learning Models for Entity Search | cs.CL | We focus on the problem of learning distributed representations for entity
search queries, named entities, and their short descriptions. With our
representation learning models, the entity search query, named entity and
description can be represented as low-dimensional vectors. Our goal is to
develop a simple but effec... | computer science |
16,213 | Word Embeddings for the Construction Domain | cs.CL | We introduce word vectors for the construction domain. Our vectors were
obtained by running word2vec on an 11M-word corpus that we created from scratch
by leveraging freely-accessible online sources of construction-related text. We
first explore the embedding space and show that our vectors capture meaningful
construct... | computer science |
16,214 | Sequence-to-sequence neural network models for transliteration | cs.CL | Transliteration is a key component of machine translation systems and
software internationalization. This paper demonstrates that neural
sequence-to-sequence models obtain state of the art or close to state of the
art results on existing datasets. In an effort to make machine transliteration
accessible, we open source ... | computer science |
16,215 | Represent, Aggregate, and Constrain: A Novel Architecture for Machine
Reading from Noisy Sources | cs.CL | In order to extract event information from text, a machine reading model must
learn to accurately read and interpret the ways in which that information is
expressed. But it must also, as the human reader must, aggregate numerous
individual value hypotheses into a single coherent global analysis, applying
global constra... | computer science |
16,216 | Experiments with POS Tagging Code-mixed Indian Social Media Text | cs.CL | This paper presents Centre for Development of Advanced Computing Mumbai's
(CDACM) submission to the NLP Tools Contest on Part-Of-Speech (POS) Tagging For
Code-mixed Indian Social Media Text (POSCMISMT) 2015 (collocated with ICON
2015). We submitted results for Hindi (hi), Bengali (bn), and Telugu (te)
languages mixed w... | computer science |
16,217 | Named Entity Recognition for Novel Types by Transfer Learning | cs.CL | In named entity recognition, we often don't have a large in-domain training
corpus or a knowledge base with adequate coverage to train a model directly. In
this paper, we propose a method where, given training data in a related domain
with similar (but not identical) named entity (NE) types and a small amount of
in-dom... | computer science |
16,218 | Knowledge Questions from Knowledge Graphs | cs.CL | We address the novel problem of automatically generating quiz-style knowledge
questions from a knowledge graph such as DBpedia. Questions of this kind have
ample applications, for instance, to educate users about or to evaluate their
knowledge in a specific domain. To solve the problem, we propose an end-to-end
approac... | computer science |
16,219 | Generating Sentiment Lexicons for German Twitter | cs.CL | Despite a substantial progress made in developing new sentiment lexicon
generation (SLG) methods for English, the task of transferring these approaches
to other languages and domains in a sound way still remains open. In this
paper, we contribute to the solution of this problem by systematically
comparing semi-automati... | computer science |
16,220 | End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension | cs.CL | This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading
comprehension (RC) model that is able to extract and rank a set of answer
candidates from a given document to answer questions. DCR is able to predict
answers of variable lengths, whereas previous neural RC models primarily
focused on predicti... | computer science |
16,221 | RNN Approaches to Text Normalization: A Challenge | cs.CL | This paper presents a challenge to the community: given a large corpus of
written text aligned to its normalized spoken form, train an RNN to learn the
correct normalization function. We present a data set of general text where the
normalizations were generated using an existing text normalization component of
a text-t... | computer science |
16,222 | Improving Twitter Sentiment Classification via Multi-Level
Sentiment-Enriched Word Embeddings | cs.CL | Most of existing work learn sentiment-specific word representation for
improving Twitter sentiment classification, which encoded both n-gram and
distant supervised tweet sentiment information in learning process. They assume
all words within a tweet have the same sentiment polarity as the whole tweet,
which ignores the... | computer science |
16,223 | Dual Learning for Machine Translation | cs.CL | While neural machine translation (NMT) is making good progress in the past
two years, tens of millions of bilingual sentence pairs are needed for its
training. However, human labeling is very costly. To tackle this training data
bottleneck, we develop a dual-learning mechanism, which can enable an NMT
system to automat... | computer science |
16,224 | Recurrent Neural Network Language Model Adaptation Derived Document
Vector | cs.CL | In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among t... | computer science |
16,225 | Faster decoding for subword level Phrase-based SMT between related
languages | cs.CL | A common and effective way to train translation systems between related
languages is to consider sub-word level basic units. However, this increases
the length of the sentences resulting in increased decoding time. The increase
in length is also impacted by the specific choice of data format for
representing the senten... | computer science |
16,226 | Towards Sub-Word Level Compositions for Sentiment Analysis of
Hindi-English Code Mixed Text | cs.CL | Sentiment analysis (SA) using code-mixed data from social media has several
applications in opinion mining ranging from customer satisfaction to social
campaign analysis in multilingual societies. Advances in this area are impeded
by the lack of a suitable annotated dataset. We introduce a Hindi-English
(Hi-En) code-mi... | computer science |
16,227 | Detecting Context Dependent Messages in a Conversational Environment | cs.CL | While automatic response generation for building chatbot systems has drawn a
lot of attention recently, there is limited understanding on when we need to
consider the linguistic context of an input text in the generation process. The
task is challenging, as messages in a conversational environment are short and
informa... | computer science |
16,228 | Ordinal Common-sense Inference | cs.CL | Humans have the capacity to draw common-sense inferences from natural
language: various things that are likely but not certain to hold based on
established discourse, and are rarely stated explicitly. We propose an
evaluation of automated common-sense inference based on an extension of
recognizing textual entailment: p... | computer science |
16,229 | Fuzzy paraphrases in learning word representations with a lexicon | cs.CL | A synonym of a polysemous word is usually only the paraphrase of one sense
among many. When lexicons are used to improve vector-space word
representations, such paraphrases are unreliable and bring noise to the
vector-space. The prior works use a coefficient to adjust the overall learning
of the lexicons. They regard t... | computer science |
16,230 | A FOFE-based Local Detection Approach for Named Entity Recognition and
Mention Detection | cs.CL | In this paper, we study a novel approach for named entity recognition (NER)
and mention detection in natural language processing. Instead of treating NER
as a sequence labelling problem, we propose a new local detection approach,
which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method
to fully e... | computer science |
16,231 | An empirical study for Vietnamese dependency parsing | cs.CL | This paper presents an empirical comparison of different dependency parsers
for Vietnamese, which has some unusual characteristics such as copula drop and
verb serialization. Experimental results show that the neural network-based
parsers perform significantly better than the traditional parsers. We report
the highest ... | computer science |
16,232 | A Hybrid Approach to Word Sense Disambiguation Combining Supervised and
Unsupervised Learning | cs.CL | In this paper, we are going to find meaning of words based on distinct
situations. Word Sense Disambiguation is used to find meaning of words based on
live contexts using supervised and unsupervised approaches. Unsupervised
approaches use online dictionary for learning, and supervised approaches use
manual learning set... | computer science |
16,233 | CogALex-V Shared Task: ROOT18 | cs.CL | In this paper, we describe ROOT 18, a classifier using the scores of several
unsupervised distributional measures as features to discriminate between
semantically related and unrelated words, and then to classify the related
pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy,
part-whole mero... | computer science |
16,234 | Binary Paragraph Vectors | cs.CL | Recently Le & Mikolov described two log-linear models, called Paragraph
Vector, that can be used to learn state-of-the-art distributed representations
of documents. Inspired by this work, we present Binary Paragraph Vector models:
simple neural networks that learn short binary codes for fast information
retrieval. We s... | computer science |
16,235 | Answering Complicated Question Intents Expressed in Decomposed Question
Sequences | cs.CL | Recent work in semantic parsing for question answering has focused on long
and complicated questions, many of which would seem unnatural if asked in a
normal conversation between two humans. In an effort to explore a
conversational QA setting, we present a more realistic task: answering
sequences of simple but inter-re... | computer science |
16,236 | Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies | cs.CL | The success of long short-term memory (LSTM) neural networks in language
processing is typically attributed to their ability to capture long-distance
statistical regularities. Linguistic regularities are often sensitive to
syntactic structure; can such dependencies be captured by LSTMs, which do not
have explicit struc... | computer science |
16,237 | Learning Recurrent Span Representations for Extractive Question
Answering | cs.CL | The reading comprehension task, that asks questions about a given evidence
document, is a central problem in natural language understanding. Recent
formulations of this task have typically focused on answer selection from a set
of candidates pre-defined manually or through the use of an external NLP
pipeline. However, ... | computer science |
16,238 | Morphological Inflection Generation with Hard Monotonic Attention | cs.CL | We present a neural model for morphological inflection generation which
employs a hard attention mechanism, inspired by the nearly-monotonic alignment
commonly found between the characters in a word and the characters in its
inflection. We evaluate the model on three previously studied morphological
inflection generati... | computer science |
16,239 | Bidirectional Attention Flow for Machine Comprehension | cs.CL | Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it... | computer science |
16,240 | Reference-Aware Language Models | cs.CL | We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by... | computer science |
16,241 | Hierarchical Question Answering for Long Documents | cs.CL | We present a framework for question answering that can efficiently scale to
longer documents while maintaining or even improving performance of
state-of-the-art models. While most successful approaches for reading
comprehension rely on recurrent neural networks (RNNs), running them over long
documents is prohibitively ... | computer science |
16,242 | Latent Attention For If-Then Program Synthesis | cs.CL | Automatic translation from natural language descriptions into programs is a
longstanding challenging problem. In this work, we consider a simple yet
important sub-problem: translation from textual descriptions to If-Then
programs. We devise a novel neural network architecture for this task which we
train end-to-end. Sp... | computer science |
16,243 | Neural Machine Translation with Reconstruction | cs.CL | Although end-to-end Neural Machine Translation (NMT) has achieved remarkable
progress in the past two years, it suffers from a major drawback: translations
generated by NMT systems often lack of adequacy. It has been widely observed
that NMT tends to repeatedly translate some source words while mistakenly
ignoring othe... | computer science |
16,244 | AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text
Classification | cs.CL | Recently deeplearning models have been shown to be capable of making
remarkable performance in sentences and documents classification tasks. In this
work, we propose a novel framework called AC-BLSTM for modeling sentences and
documents, which combines the asymmetric convolution neural network (ACNN) with
the Bidirecti... | computer science |
16,245 | Keyphrase Annotation with Graph Co-Ranking | cs.CL | Keyphrase annotation is the task of identifying textual units that represent
the main content of a document. Keyphrase annotation is either carried out by
extracting the most important phrases from a document, keyphrase extraction, or
by assigning entries from a controlled domain-specific vocabulary, keyphrase
assignme... | computer science |
16,246 | Presenting a New Dataset for the Timeline Generation Problem | cs.CL | The timeline generation task summarises an entity's biography by selecting
stories representing key events from a large pool of relevant documents. This
paper addresses the lack of a standard dataset and evaluative methodology for
the problem. We present and make publicly available a new dataset of 18,793
news articles... | computer science |
16,247 | :telephone::person::sailboat::whale::okhand:; or "Call me Ishmael" - How
do you translate emoji? | cs.CL | We report on an exploratory analysis of Emoji Dick, a project that leverages
crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive
use of emoji removes textual context, and leads to a varying translation
quality. In this paper, we use statistical word alignment and part-of-speech
tagging to explo... | computer science |
16,248 | Building a comprehensive syntactic and semantic corpus of Chinese
clinical texts | cs.CL | Objective: To build a comprehensive corpus covering syntactic and semantic
annotations of Chinese clinical texts with corresponding annotation guidelines
and methods as well as to develop tools trained on the annotated corpus, which
supplies baselines for research on Chinese texts in the clinical domain.
Materials an... | computer science |
16,249 | A Convolutional Encoder Model for Neural Machine Translation | cs.CL | The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for ... | computer science |
16,250 | Cruciform: Solving Crosswords with Natural Language Processing | cs.CL | Crossword puzzles are popular word games that require not only a large
vocabulary, but also a broad knowledge of topics. Answering each clue is a
natural language task on its own as many clues contain nuances, puns, or
counter-intuitive word definitions. Additionally, it can be extremely difficult
to ascertain definiti... | computer science |
16,251 | Dependency Sensitive Convolutional Neural Networks for Modeling
Sentences and Documents | cs.CL | The goal of sentence and document modeling is to accurately represent the
meaning of sentences and documents for various Natural Language Processing
tasks. In this work, we present Dependency Sensitive Convolutional Neural
Networks (DSCNN) as a general-purpose classification system for both sentences
and documents. DSC... | computer science |
16,252 | A Surrogate-based Generic Classifier for Chinese TV Series Reviews | cs.CL | With the emerging of various online video platforms like Youtube, Youku and
LeTV, online TV series' reviews become more and more important both for viewers
and producers. Customers rely heavily on these reviews before selecting TV
series, while producers use them to improve the quality. As a result,
automatically class... | computer science |
16,253 | Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based
Approaches | cs.CL | Acoustic word embeddings --- fixed-dimensional vector representations of
variable-length spoken word segments --- have begun to be considered for tasks
such as speech recognition and query-by-example search. Such embeddings can be
learned discriminatively so that they are similar for speech segments
corresponding to th... | computer science |
16,254 | Contradiction Detection for Rumorous Claims | cs.CL | The utilization of social media material in journalistic workflows is
increasing, demanding automated methods for the identification of mis- and
disinformation. Since textual contradiction across social media posts can be a
signal of rumorousness, we seek to model how claims in Twitter posts are being
textually contrad... | computer science |
16,255 | Veracity Computing from Lexical Cues and Perceived Certainty Trends | cs.CL | We present a data-driven method for determining the veracity of a set of
rumorous claims on social media data. Tweets from different sources pertaining
to a rumor are processed on three levels: first, factuality values are assigned
to each tweet based on four textual cue categories relevant for our journalism
use case;... | computer science |
16,256 | Old Content and Modern Tools - Searching Named Entities in a Finnish
OCRed Historical Newspaper Collection 1771-1910 | cs.CL | Named Entity Recognition (NER), search, classification and tagging of names
and name like frequent informational elements in texts, has become a standard
information extraction procedure for textual data. NER has been applied to many
types of texts and different types of entities: newspapers, fiction, historical
record... | computer science |
16,257 | A Comparison of Word Embeddings for English and Cross-Lingual Chinese
Word Sense Disambiguation | cs.CL | Word embeddings are now ubiquitous forms of word representation in natural
language processing. There have been applications of word embeddings for
monolingual word sense disambiguation (WSD) in English, but few comparisons
have been done. This paper attempts to bridge that gap by examining popular
embeddings for the t... | computer science |
16,258 | Distant supervision for emotion detection using Facebook reactions | cs.CL | We exploit the Facebook reaction feature in a distant supervised fashion to
train a support vector machine classifier for emotion detection, using several
feature combinations and combining different Facebook pages. We test our models
on existing benchmarks for emotion detection and show that employing only
information... | computer science |
16,259 | When silver glitters more than gold: Bootstrapping an Italian
part-of-speech tagger for Twitter | cs.CL | We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter
data, in the context of the Evalita 2016 PoSTWITA shared task. We show that
training the tagger on native Twitter data enriched with little amounts of
specifically selected gold data and additional silver-labelled data scraped
from Facebook, y... | computer science |
16,260 | Tracing metaphors in time through self-distance in vector spaces | cs.CL | From a diachronic corpus of Italian, we build consecutive vector spaces in
time and use them to compare a term's cosine similarity to itself in different
time spans. We assume that a drop in similarity might be related to the
emergence of a metaphorical sense at a given time. Similarity-based
observations are matched t... | computer science |
16,261 | Efficient Summarization with Read-Again and Copy Mechanism | cs.CL | Encoder-decoder models have been widely used to solve sequence to sequence
prediction tasks. However current approaches suffer from two shortcomings.
First, the encoders compute a representation of each word taking into account
only the history of the words it has read so far, yielding suboptimal
representations. Secon... | computer science |
16,262 | Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory
Detection: A Natural Language Processing Approach | cs.CL | The aim of syntactic tracking is to classify spatio-temporal patterns of a
target's motion using natural language processing models. In this paper, we
generalize earlier work by considering a constrained stochastic context free
grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained
grammar facilit... | computer science |
16,263 | Improving Reliability of Word Similarity Evaluation by Redesigning
Annotation Task and Performance Measure | cs.CL | We suggest a new method for creating and using gold-standard datasets for
word similarity evaluation. Our goal is to improve the reliability of the
evaluation, and we do this by redesigning the annotation task to achieve higher
inter-rater agreement, and by defining a performance measure which takes the
reliability of ... | computer science |
16,264 | Training IBM Watson using Automatically Generated Question-Answer Pairs | cs.CL | IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to... | computer science |
16,265 | Linguistically Regularized LSTMs for Sentiment Classification | cs.CL | Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification. Though a variety
of neural network models have been proposed very recently, however, previous
models either depend on expensive phrase-level annotation, whose performance
drops sub... | computer science |
16,266 | Multi-Language Identification Using Convolutional Recurrent Neural
Network | cs.CL | Language Identification, being an important aspect of Automatic Speaker
Recognition has had many changes and new approaches to ameliorate performance
over the last decade. We compare the performance of using audio spectrum in the
log scale and using Polyphonic sound sequences from raw audio samples to train
the neural ... | computer science |
16,267 | Semi-automatic Simultaneous Interpreting Quality Evaluation | cs.CL | Increasing interpreting needs a more objective and automatic measurement. We
hold a basic idea that 'translating means translating meaning' in that we can
assessment interpretation quality by comparing the meaning of the interpreting
output with the source input. That is, a translation unit of a 'chunk' named
Frame whi... | computer science |
16,268 | Cross-lingual Dataless Classification for Languages with Small Wikipedia
Presence | cs.CL | This paper presents an approach to classify documents in any language into an
English topical label space, without any text categorization training data. The
approach, Cross-Lingual Dataless Document Classification (CLDDC) relies on
mapping the English labels or short category description into a Wikipedia-based
semanti... | computer science |
16,269 | Joint Representation Learning of Text and Knowledge for Knowledge Graph
Completion | cs.CL | Joint representation learning of text and knowledge within a unified semantic
space enables us to perform knowledge graph completion more accurately. In this
work, we propose a novel framework to embed words, entities and relations into
the same continuous vector space. In this model, both entity and relation
embedding... | computer science |
16,270 | SummaRuNNer: A Recurrent Neural Network based Sequence Model for
Extractive Summarization of Documents | cs.CL | We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model
for extractive summarization of documents and show that it achieves performance
better than or comparable to state-of-the-art. Our model has the additional
advantage of being very interpretable, since it allows visualization of its
prediction... | computer science |
16,271 | A New Recurrent Neural CRF for Learning Non-linear Edge Features | cs.CL | Conditional Random Field (CRF) and recurrent neural models have achieved
success in structured prediction. More recently, there is a marriage of CRF and
recurrent neural models, so that we can gain from both non-linear dense
features and globally normalized CRF objective. These recurrent neural CRF
models mainly focus ... | computer science |
16,272 | F-Score Driven Max Margin Neural Network for Named Entity Recognition in
Chinese Social Media | cs.CL | We focus on named entity recognition (NER) for Chinese social media. With
massive unlabeled text and quite limited labelled corpus, we propose a
semi-supervised learning model based on B-LSTM neural network. To take
advantage of traditional methods in NER such as CRF, we combine transition
probability with deep learnin... | computer science |
16,273 | Classify or Select: Neural Architectures for Extractive Document
Summarization | cs.CL | We present two novel and contrasting Recurrent Neural Network (RNN) based
architectures for extractive summarization of documents. The Classifier based
architecture sequentially accepts or rejects each sentence in the original
document order for its membership in the final summary. The Selector
architecture, on the oth... | computer science |
16,274 | `Who would have thought of that!': A Hierarchical Topic Model for
Extraction of Sarcasm-prevalent Topics and Sarcasm Detection | cs.CL | Topic Models have been reported to be beneficial for aspect-based sentiment
analysis. This paper reports a simple topic model for sarcasm detection, a
first, to the best of our knowledge. Designed on the basis of the intuition
that sarcastic tweets are likely to have a mixture of words of both sentiments
as against twe... | computer science |
16,275 | Character-level Convolutional Network for Text Classification Applied to
Chinese Corpus | cs.CL | This article provides an interesting exploration of character-level
convolutional neural network solving Chinese corpus text classification
problem. We constructed a large-scale Chinese language dataset, and the result
shows that character-level convolutional neural network works better on Chinese
corpus than its corre... | computer science |
16,276 | Ranking medical jargon in electronic health record notes by adapted
distant supervision | cs.CL | Objective: Allowing patients to access their own electronic health record
(EHR) notes through online patient portals has the potential to improve
patient-centered care. However, medical jargon, which abounds in EHR notes, has
been shown to be a barrier for patient EHR comprehension. Existing knowledge
bases that link m... | computer science |
16,277 | Multi-view Recurrent Neural Acoustic Word Embeddings | cs.CL | Recent work has begun exploring neural acoustic word
embeddings---fixed-dimensional vector representations of arbitrary-length
speech segments corresponding to words. Such embeddings are applicable to
speech retrieval and recognition tasks, where reasoning about whole words may
make it possible to avoid ambiguous sub-w... | computer science |
16,278 | Knowledge Enhanced Hybrid Neural Network for Text Matching | cs.CL | Long text brings a big challenge to semantic matching due to their
complicated semantic and syntactic structures. To tackle the challenge, we
consider using prior knowledge to help identify useful information and filter
out noise to matching in long text. To this end, we propose a knowledge
enhanced hybrid neural netwo... | computer science |
16,279 | A Neural Architecture Mimicking Humans End-to-End for Natural Language
Inference | cs.CL | In this work we use the recent advances in representation learning to propose
a neural architecture for the problem of natural language inference. Our
approach is aligned to mimic how a human does the natural language inference
process given two statements. The model uses variants of Long Short Term Memory
(LSTM), atte... | computer science |
16,280 | Toward Multilingual Neural Machine Translation with Universal Encoder
and Decoder | cs.CL | In this paper, we present our first attempts in building a multilingual
Neural Machine Translation framework under a unified approach. We are then able
to employ attention-based NMT for many-to-many multilingual translation tasks.
Our approach does not require any special treatment on the network architecture
and it al... | computer science |
16,281 | SimDoc: Topic Sequence Alignment based Document Similarity Framework | cs.CL | Document similarity is the problem of estimating the degree to which a given
pair of documents has similar semantic content. An accurate document similarity
measure can improve several enterprise relevant tasks such as document
clustering, text mining, and question-answering. In this paper, we show that a
document's th... | computer science |
16,282 | Lost in Space: Geolocation in Event Data | cs.CL | Extracting the "correct" location information from text data, i.e.,
determining the place of event, has long been a goal for automated text
processing. To approximate human-like coding schema, we introduce a supervised
machine learning algorithm that classifies each location word to be either
correct or incorrect. We u... | computer science |
16,283 | Neural Machine Translation with Pivot Languages | cs.CL | While recent neural machine translation approaches have delivered
state-of-the-art performance for resource-rich language pairs, they suffer from
the data scarcity problem for resource-scarce language pairs. Although this
problem can be alleviated by exploiting a pivot language to bridge the source
and target languages... | computer science |
16,284 | End-to-End Neural Sentence Ordering Using Pointer Network | cs.CL | Sentence ordering is one of important tasks in NLP. Previous works mainly
focused on improving its performance by using pair-wise strategy. However, it
is nontrivial for pair-wise models to incorporate the contextual sentence
information. In addition, error prorogation could be introduced by using the
pipeline strategy... | computer science |
16,285 | Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed
Social Media Text | cs.CL | This paper describes Centre for Development of Advanced Computing's (CDACM)
submission to the shared task-'Tool Contest on POS tagging for Code-Mixed
Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with
ICON-2016. The shared task was to predict Part of Speech (POS) tag at word
level for a given ... | computer science |
16,286 | How to do lexical quality estimation of a large OCRed historical Finnish
newspaper collection with scarce resources | cs.CL | The National Library of Finland has digitized the historical newspapers
published in Finland between 1771 and 1910. This collection contains
approximately 1.95 million pages in Finnish and Swedish. Finnish part of the
collection consists of about 2.40 billion words. The National Library's Digital
Collections are offere... | computer science |
16,287 | The Life of Lazarillo de Tormes and of His Machine Learning Adversities | cs.CL | Summit work of the Spanish Golden Age and forefather of the so-called
picaresque novel, The Life of Lazarillo de Tormes and of His Fortunes and
Adversities still remains an anonymous text. Although distinguished scholars
have tried to attribute it to different authors based on a variety of criteria,
a consensus has yet... | computer science |
16,288 | A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and
Part-of-Speech Tagging | cs.CL | Recently, neural network models for natural language processing tasks have
been increasingly focused on for their ability of alleviating the burden of
manual feature engineering. However, the previous neural models cannot extract
the complicated feature compositions as the traditional methods with discrete
features. In... | computer science |
16,289 | What Do Recurrent Neural Network Grammars Learn About Syntax? | cs.CL | Recurrent neural network grammars (RNNG) are a recently proposed
probabilistic generative modeling family for natural language. They show
state-of-the-art language modeling and parsing performance. We investigate what
information they learn, from a linguistic perspective, through various
ablations to the model and the ... | computer science |
16,290 | Word and Document Embeddings based on Neural Network Approaches | cs.CL | Data representation is a fundamental task in machine learning. The
representation of data affects the performance of the whole machine learning
system. In a long history, the representation of data is done by feature
engineering, and researchers aim at designing better features for specific
tasks. Recently, the rapid d... | computer science |
16,291 | Tracking Words in Chinese Poetry of Tang and Song Dynasties with the
China Biographical Database | cs.CL | Large-scale comparisons between the poetry of Tang and Song dynasties shed
light on how words, collocations, and expressions were used and shared among
the poets. That some words were used only in the Tang poetry and some only in
the Song poetry could lead to interesting research in linguistics. That the
most frequent ... | computer science |
16,292 | Incorporating Pass-Phrase Dependent Background Models for Text-Dependent
Speaker Verification | cs.CL | In this paper, we propose pass-phrase dependent background models (PBMs) for
text-dependent (TD) speaker verification (SV) to integrate the pass-phrase
identification process into the conventional TD-SV system, where a PBM is
derived from a text-independent background model through adaptation using the
utterances of a ... | computer science |
16,293 | Text Classification Improved by Integrating Bidirectional LSTM with
Two-dimensional Max Pooling | cs.CL | Recurrent Neural Network (RNN) is one of the most popular architectures used
in Natural Language Processsing (NLP) tasks because its recurrent structure is
very suitable to process variable-length text. RNN can utilize distributed
representations of words by first converting the tokens comprising each text
into vectors... | computer science |
16,294 | False-Friend Detection and Entity Matching via Unsupervised
Transliteration | cs.CL | Transliterations play an important role in multilingual entity reference
resolution, because proper names increasingly travel between languages in news
and social media. Previous work associated with machine translation targets
transliteration only single between language pairs, focuses on specific classes
of entities ... | computer science |
16,295 | Bidirectional Tree-Structured LSTM with Head Lexicalization | cs.CL | Sequential LSTM has been extended to model tree structures, giving
competitive results for a number of tasks. Existing methods model constituent
trees by bottom-up combinations of constituent nodes, making direct use of
input word information only for leaf nodes. This is different from sequential
LSTMs, which contain r... | computer science |
16,296 | Learning to Distill: The Essence Vector Modeling Framework | cs.CL | In the context of natural language processing, representation learning has
emerged as a newly active research subject because of its excellent performance
in many applications. Learning representations of words is a pioneering study
in this school of research. However, paragraph (or sentence and document)
embedding lea... | computer science |
16,297 | Compositional Learning of Relation Path Embedding for Knowledge Base
Completion | cs.CL | Large-scale knowledge bases have currently reached impressive sizes; however,
these knowledge bases are still far from complete. In addition, most of the
existing methods for knowledge base completion only consider the direct links
between entities, ignoring the vital impact of the consistent semantics of
relation path... | computer science |
16,298 | ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods
in Scala | cs.CL | Automatically recognized terminology is widely used for various
domain-specific texts processing tasks, such as machine translation,
information retrieval or sentiment analysis. However, there is still no
agreement on which methods are best suited for particular settings and,
moreover, there is no reliable comparison o... | computer science |
16,299 | Emergent Predication Structure in Hidden State Vectors of Neural Readers | cs.CL | A significant number of neural architectures for reading comprehension have
recently been developed and evaluated on large cloze-style datasets. We present
experiments supporting the emergence of "predication structure" in the hidden
state vectors of these readers. More specifically, we provide evidence that the
hidden... | computer science |
16,300 | Kannada Spell Checker with Sandhi Splitter | cs.CL | Spelling errors are introduced in text either during typing, or when the user
does not know the correct phoneme or grapheme. If a language contains complex
words like sandhi where two or more morphemes join based on some rules, spell
checking becomes very tedious. In such situations, having a spell checker with
sandhi ... | computer science |
16,301 | Neural Machine Translation with Latent Semantic of Image and Text | cs.CL | Although attention-based Neural Machine Translation have achieved great
success, attention-mechanism cannot capture the entire meaning of the source
sentence because the attention mechanism generates a target word depending
heavily on the relevant parts of the source sentence. The report of earlier
studies has introduc... | computer science |
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