Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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15,702 | Corpus analysis without prior linguistic knowledge - unsupervised mining
of phrases and subphrase structure | cs.CL | When looking at the structure of natural language, "phrases" and "words" are
central notions. We consider the problem of identifying such "meaningful
subparts" of language of any length and underlying composition principles in a
completely corpus-based and language-independent way without using any kind of
prior lingui... | computer science |
15,703 | The Interaction of Memory and Attention in Novel Word Generalization: A
Computational Investigation | cs.CL | People exhibit a tendency to generalize a novel noun to the basic-level in a
hierarchical taxonomy -- a cognitively salient category such as "dog" -- with
the degree of generalization depending on the number and type of exemplars.
Recently, a change in the presentation timing of exemplars has also been shown
to have an... | computer science |
15,704 | Abstractive Text Summarization Using Sequence-to-Sequence RNNs and
Beyond | cs.CL | In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basi... | computer science |
15,705 | On Training Bi-directional Neural Network Language Model with Noise
Contrastive Estimation | cs.CL | We propose to train bi-directional neural network language model(NNLM) with
noise contrastive estimation(NCE). Experiments are conducted on a rescore task
on the PTB data set. It is shown that NCE-trained bi-directional NNLM
outperformed the one trained by conventional maximum likelihood training. But
still(regretfully... | computer science |
15,706 | Learning to SMILE(S) | cs.CL | This paper shows how one can directly apply natural language processing (NLP)
methods to classification problems in cheminformatics. Connection between these
seemingly separate fields is shown by considering standard textual
representation of compound, SMILES. The problem of activity prediction against
a target protein... | computer science |
15,707 | Contextual LSTM (CLSTM) models for Large scale NLP tasks | cs.CL | Documents exhibit sequential structure at multiple levels of abstraction
(e.g., sentences, paragraphs, sections). These abstractions constitute a
natural hierarchy for representing the context in which to infer the meaning of
words and larger fragments of text. In this paper, we present CLSTM (Contextual
LSTM), an exte... | computer science |
15,708 | Semi-supervised Clustering for Short Text via Deep Representation
Learning | cs.CL | In this work, we propose a semi-supervised method for short text clustering,
where we represent texts as distributed vectors with neural networks, and use a
small amount of labeled data to specify our intention for clustering. We design
a novel objective to combine the representation learning process and the
k-means cl... | computer science |
15,709 | Sentence Similarity Learning by Lexical Decomposition and Composition | cs.CL | Most conventional sentence similarity methods only focus on similar parts of
two input sentences, and simply ignore the dissimilar parts, which usually give
us some clues and semantic meanings about the sentences. In this work, we
propose a model to take into account both the similarities and dissimilarities
by decompo... | computer science |
15,710 | Petrarch 2 : Petrarcher | cs.CL | PETRARCH 2 is the fourth generation of a series of Event-Data coders stemming
from research by Phillip Schrodt. Each iteration has brought new functionality
and usability, and this is no exception.Petrarch 2 takes much of the power of
the original Petrarch's dictionaries and redirects it into a faster and smarter
core ... | computer science |
15,711 | Ultradense Word Embeddings by Orthogonal Transformation | cs.CL | Embeddings are generic representations that are useful for many NLP tasks. In
this paper, we introduce DENSIFIER, a method that learns an orthogonal
transformation of the embedding space that focuses the information relevant for
a task in an ultradense subspace of a dimensionality that is smaller by a
factor of 100 tha... | computer science |
15,712 | Toward Mention Detection Robustness with Recurrent Neural Networks | cs.CL | One of the key challenges in natural language processing (NLP) is to yield
good performance across application domains and languages. In this work, we
investigate the robustness of the mention detection systems, one of the
fundamental tasks in information extraction, via recurrent neural networks
(RNNs). The advantage ... | computer science |
15,713 | Automated Word Prediction in Bangla Language Using Stochastic Language
Models | cs.CL | Word completion and word prediction are two important phenomena in typing
that benefit users who type using keyboard or other similar devices. They can
have profound impact on the typing of disable people. Our work is based on word
prediction on Bangla sentence by using stochastic, i.e. N-gram language model
such as un... | computer science |
15,714 | QuotationFinder - Searching for Quotations and Allusions in Greek and
Latin Texts and Establishing the Degree to Which a Quotation or Allusion
Matches Its Source | cs.CL | The software programs generally used with the TLG (Thesaurus Linguae Graecae)
and the CLCLT (CETEDOC Library of Christian Latin Texts) CD-ROMs are not well
suited for finding quotations and allusions. QuotationFinder uses more
sophisticated criteria as it ranks search results based on how closely they
match the source ... | computer science |
15,715 | Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus | cs.CL | We propose a method for efficiently finding all parallel passages in a large
corpus, even if the passages are not quite identical due to rephrasing and
orthographic variation. The key ideas are the representation of each word in
the corpus by its two most infrequent letters, finding matched pairs of strings
of four or ... | computer science |
15,716 | Gibberish Semantics: How Good is Russian Twitter in Word Semantic
Similarity Task? | cs.CL | The most studied and most successful language models were developed and
evaluated mainly for English and other close European languages, such as
French, German, etc. It is important to study applicability of these models to
other languages. The use of vector space models for Russian was recently
studied for multiple co... | computer science |
15,717 | Bioinformatics and Classical Literary Study | cs.CL | This paper describes the Quantitative Criticism Lab, a collaborative
initiative between classicists, quantitative biologists, and computer
scientists to apply ideas and methods drawn from the sciences to the study of
literature. A core goal of the project is the use of computational biology,
natural language processing... | computer science |
15,718 | Easy-First Dependency Parsing with Hierarchical Tree LSTMs | cs.CL | We suggest a compositional vector representation of parse trees that relies
on a recursive combination of recurrent-neural network encoders. To demonstrate
its effectiveness, we use the representation as the backbone of a greedy,
bottom-up dependency parser, achieving state-of-the-art accuracies for English
and Chinese... | computer science |
15,719 | Improving Named Entity Recognition for Chinese Social Media with Word
Segmentation Representation Learning | cs.CL | Named entity recognition, and other information extraction tasks, frequently
use linguistic features such as part of speech tags or chunkings. For languages
where word boundaries are not readily identified in text, word segmentation is
a key first step to generating features for an NER system. While using word
boundary... | computer science |
15,720 | Question Answering on Freebase via Relation Extraction and Textual
Evidence | cs.CL | Existing knowledge-based question answering systems often rely on small
annotated training data. While shallow methods like relation extraction are
robust to data scarcity, they are less expressive than the deep meaning
representation methods like semantic parsing, thereby failing at answering
questions involving multi... | computer science |
15,721 | MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for
Sentence Classification | cs.CL | We introduce a novel, simple convolution neural network (CNN) architecture -
multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of
word embeddings for sentence classification. MGNC-CNN extracts features from
input embedding sets independently and then joins these at the penultimate
layer in th... | computer science |
15,722 | Right Ideals of a Ring and Sublanguages of Science | cs.CL | Among Zellig Harris's numerous contributions to linguistics his theory of the
sublanguages of science probably ranks among the most underrated. However, not
only has this theory led to some exhaustive and meaningful applications in the
study of the grammar of immunology language and its changes over time, but it
also i... | computer science |
15,723 | Multi-domain Neural Network Language Generation for Spoken Dialogue
Systems | cs.CL | Moving from limited-domain natural language generation (NLG) to open domain
is difficult because the number of semantic input combinations grows
exponentially with the number of domains. Therefore, it is important to
leverage existing resources and exploit similarities between domains to
facilitate domain adaptation. I... | computer science |
15,724 | Joint Learning Templates and Slots for Event Schema Induction | cs.CL | Automatic event schema induction (AESI) means to extract meta-event from raw
text, in other words, to find out what types (templates) of event may exist in
the raw text and what roles (slots) may exist in each event type. In this
paper, we propose a joint entity-driven model to learn templates and slots
simultaneously ... | computer science |
15,725 | Neural Architectures for Named Entity Recognition | cs.CL | State-of-the-art named entity recognition systems rely heavily on
hand-crafted features and domain-specific knowledge in order to learn
effectively from the small, supervised training corpora that are available. In
this paper, we introduce two new neural architectures---one based on
bidirectional LSTMs and conditional ... | computer science |
15,726 | A Bayesian Model of Multilingual Unsupervised Semantic Role Induction | cs.CL | We propose a Bayesian model of unsupervised semantic role induction in
multiple languages, and use it to explore the usefulness of parallel corpora
for this task. Our joint Bayesian model consists of individual models for each
language plus additional latent variables that capture alignments between roles
across langua... | computer science |
15,727 | Parallel Texts in the Hebrew Bible, New Methods and Visualizations | cs.CL | In this article we develop an algorithm to detect parallel texts in the
Masoretic Text of the Hebrew Bible. The results are presented online and
chapters in the Hebrew Bible containing parallel passages can be inspected
synoptically. Differences between parallel passages are highlighted. In a
similar way the MT of Isai... | computer science |
15,728 | Text Understanding with the Attention Sum Reader Network | cs.CL | Several large cloze-style context-question-answer datasets have been
introduced recently: the CNN and Daily Mail news data and the Children's Book
Test. Thanks to the size of these datasets, the associated text comprehension
task is well suited for deep-learning techniques that currently seem to
outperform all alternat... | computer science |
15,729 | Getting More Out Of Syntax with PropS | cs.CL | Semantic NLP applications often rely on dependency trees to recognize major
elements of the proposition structure of sentences. Yet, while much semantic
structure is indeed expressed by syntax, many phenomena are not easily read out
of dependency trees, often leading to further ad-hoc heuristic post-processing
or to in... | computer science |
15,730 | Extracting Arabic Relations from the Web | cs.CL | The goal of this research is to extract a large list or table from named
entities and relations in a specific domain. A small set of a handful of
instance relations is required as input from the user. The system exploits
summaries from Google search engine as a source text. These instances are used
to extract patterns.... | computer science |
15,731 | Observing Trends in Automated Multilingual Media Analysis | cs.CL | Any large organisation, be it public or private, monitors the media for
information to keep abreast of developments in their field of interest, and
usually also to become aware of positive or negative opinions expressed towards
them. At least for the written media, computer programs have become very
efficient at helpin... | computer science |
15,732 | Unsupervised word segmentation and lexicon discovery using acoustic word
embeddings | cs.CL | In settings where only unlabelled speech data is available, speech technology
needs to be developed without transcriptions, pronunciation dictionaries, or
language modelling text. A similar problem is faced when modelling infant
language acquisition. In these cases, categorical linguistic structure needs to
be discover... | computer science |
15,733 | Lexical bundles in computational linguistics academic literature | cs.CL | In this study we analyzed a corpus of 8 million words academic literature
from Computational lingustics' academic literature. the lexical bundles from
this corpus are categorized based on structures and functions. | computer science |
15,734 | Sieve-based Coreference Resolution in the Biomedical Domain | cs.CL | We describe challenges and advantages unique to coreference resolution in the
biomedical domain, and a sieve-based architecture that leverages domain
knowledge for both entity and event coreference resolution. Domain-general
coreference resolution algorithms perform poorly on biomedical documents,
because the cues they... | computer science |
15,735 | Training with Exploration Improves a Greedy Stack-LSTM Parser | cs.CL | We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to
support a training-with-exploration procedure using dynamic oracles(Goldberg
and Nivre, 2013) instead of cross-entropy minimization. This form of training,
which accounts for model predictions at training time rather than assuming an
error-free a... | computer science |
15,736 | Neural Discourse Relation Recognition with Semantic Memory | cs.CL | Humans comprehend the meanings and relations of discourses heavily relying on
their semantic memory that encodes general knowledge about concepts and facts.
Inspired by this, we propose a neural recognizer for implicit discourse
relation analysis, which builds upon a semantic memory that stores knowledge in
a distribut... | computer science |
15,737 | Variational Neural Discourse Relation Recognizer | cs.CL | Implicit discourse relation recognition is a crucial component for automatic
discourselevel analysis and nature language understanding. Previous studies
exploit discriminative models that are built on either powerful manual features
or deep discourse representations. In this paper, instead, we explore
generative models... | computer science |
15,738 | Interactive Tools and Tasks for the Hebrew Bible | cs.CL | This contribution to a special issue on "Computer-aided processing of
intertextuality" in ancient texts will illustrate how using digital tools to
interact with the Hebrew Bible offers new promising perspectives for
visualizing the texts and for performing tasks in education and research. This
contribution explores how... | computer science |
15,739 | Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature
Representations | cs.CL | We present a simple and effective scheme for dependency parsing which is
based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with
a BiLSTM vector representing the token in its sentential context, and feature
vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is
trained joint... | computer science |
15,740 | Multichannel Variable-Size Convolution for Sentence Classification | cs.CL | We propose MVCNN, a convolution neural network (CNN) architecture for
sentence classification. It (i) combines diverse versions of pretrained word
embeddings and (ii) extracts features of multigranular phrases with
variable-size convolution filters. We also show that pretraining MVCNN is
critical for good performance. ... | computer science |
15,741 | Topic Modeling Using Distributed Word Embeddings | cs.CL | We propose a new algorithm for topic modeling, Vec2Topic, that identifies the
main topics in a corpus using semantic information captured via
high-dimensional distributed word embeddings. Our technique is unsupervised and
generates a list of topics ranked with respect to importance. We find that it
works better than ex... | computer science |
15,742 | Evaluating the word-expert approach for Named-Entity Disambiguation | cs.CL | Named Entity Disambiguation (NED) is the task of linking a named-entity
mention to an instance in a knowledge-base, typically Wikipedia. This task is
closely related to word-sense disambiguation (WSD), where the supervised
word-expert approach has prevailed. In this work we present the results of the
word-expert approa... | computer science |
15,743 | Recurrent Dropout without Memory Loss | cs.CL | This paper presents a novel approach to recurrent neural network (RNN)
regularization. Differently from the widely adopted dropout method, which is
applied to \textit{forward} connections of feed-forward architectures or RNNs,
we propose to drop neurons directly in \textit{recurrent} connections in a way
that does not ... | computer science |
15,744 | Comparing Convolutional Neural Networks to Traditional Models for Slot
Filling | cs.CL | We address relation classification in the context of slot filling, the task
of finding and evaluating fillers like "Steve Jobs" for the slot X in "X
founded Apple". We propose a convolutional neural network which splits the
input sentence into three parts according to the relation arguments and compare
it to state-of-t... | computer science |
15,745 | Predicate Gradual Logic and Linguistics | cs.CL | There are several major proposals for treating donkey anaphora such as
discourse representation theory and the likes, or E-Type theories and the
likes. Every one of them works well for a set of specific examples that they
use to demonstrate validity of their approaches. As I show in this paper,
however, they are not ve... | computer science |
15,746 | A Readability Analysis of Campaign Speeches from the 2016 US
Presidential Campaign | cs.CL | Readability is defined as the reading level of the speech from grade 1 to
grade 12. It results from the use of the REAP readability analysis (vocabulary
- Collins-Thompson and Callan, 2004; syntax - Heilman et al ,2006, 2007), which
use the lexical contents and grammatical structure of the sentences in a
document to pr... | computer science |
15,747 | Readability-based Sentence Ranking for Evaluating Text Simplification | cs.CL | We propose a new method for evaluating the readability of simplified
sentences through pair-wise ranking. The validity of the method is established
through in-corpus and cross-corpus evaluation experiments. The approach
correctly identifies the ranking of simplified and unsimplified sentences in
terms of their reading ... | computer science |
15,748 | A Fast Unified Model for Parsing and Sentence Understanding | cs.CL | Tree-structured neural networks exploit valuable syntactic parse information
as they interpret the meanings of sentences. However, they suffer from two key
technical problems that make them slow and unwieldy for large-scale NLP tasks:
they usually operate on parsed sentences and they do not directly support
batched com... | computer science |
15,749 | Adaptive Joint Learning of Compositional and Non-Compositional Phrase
Embeddings | cs.CL | We present a novel method for jointly learning compositional and
non-compositional phrase embeddings by adaptively weighting both types of
embeddings using a compositionality scoring function. The scoring function is
used to quantify the level of compositionality of each phrase, and the
parameters of the function are j... | computer science |
15,750 | Tree-to-Sequence Attentional Neural Machine Translation | cs.CL | Most of the existing Neural Machine Translation (NMT) models focus on the
conversion of sequential data and do not directly use syntactic information. We
propose a novel end-to-end syntactic NMT model, extending a
sequence-to-sequence model with the source-side phrase structure. Our model has
an attention mechanism tha... | computer science |
15,751 | Improving Hypernymy Detection with an Integrated Path-based and
Distributional Method | cs.CL | Detecting hypernymy relations is a key task in NLP, which is addressed in the
literature using two complementary approaches. Distributional methods, whose
supervised variants are the current best performers, and path-based methods,
which received less research attention. We suggest an improved path-based
algorithm, in ... | computer science |
15,752 | A Persona-Based Neural Conversation Model | cs.CL | We present persona-based models for handling the issue of speaker consistency
in neural response generation. A speaker model encodes personas in distributed
embeddings that capture individual characteristics such as background
information and speaking style. A dyadic speaker-addressee model captures
properties of inter... | computer science |
15,753 | Static and Dynamic Feature Selection in Morphosyntactic Analyzers | cs.CL | We study the use of greedy feature selection methods for morphosyntactic
tagging under a number of different conditions. We compare a static ordering of
features to a dynamic ordering based on mutual information statistics, and we
apply the techniques to standalone taggers as well as joint systems for tagging
and parsi... | computer science |
15,754 | Stack-propagation: Improved Representation Learning for Syntax | cs.CL | Traditional syntax models typically leverage part-of-speech (POS) information
by constructing features from hand-tuned templates. We demonstrate that a
better approach is to utilize POS tags as a regularizer of learned
representations. We propose a simple method for learning a stacked pipeline of
models which we call "... | computer science |
15,755 | Semi-supervised Word Sense Disambiguation with Neural Models | cs.CL | Determining the intended sense of words in text - word sense disambiguation
(WSD) - is a long standing problem in natural language processing. Recently,
researchers have shown promising results using word vectors extracted from a
neural network language model as features in WSD algorithms. However, a simple
average or ... | computer science |
15,756 | Neural Summarization by Extracting Sentences and Words | cs.CL | Traditional approaches to extractive summarization rely heavily on
human-engineered features. In this work we propose a data-driven approach based
on neural networks and continuous sentence features. We develop a general
framework for single-document summarization composed of a hierarchical document
encoder and an atte... | computer science |
15,757 | Evaluating semantic models with word-sentence relatedness | cs.CL | Semantic textual similarity (STS) systems are designed to encode and evaluate
the semantic similarity between words, phrases, sentences, and documents. One
method for assessing the quality or authenticity of semantic information
encoded in these systems is by comparison with human judgments. A data set for
evaluating s... | computer science |
15,758 | Semantic Regularities in Document Representations | cs.CL | Recent work exhibited that distributed word representations are good at
capturing linguistic regularities in language. This allows vector-oriented
reasoning based on simple linear algebra between words. Since many different
methods have been proposed for learning document representations, it is natural
to ask whether t... | computer science |
15,759 | Contrastive Analysis with Predictive Power: Typology Driven Estimation
of Grammatical Error Distributions in ESL | cs.CL | This work examines the impact of cross-linguistic transfer on grammatical
errors in English as Second Language (ESL) texts. Using a computational
framework that formalizes the theory of Contrastive Analysis (CA), we
demonstrate that language specific error distributions in ESL writing can be
predicted from the typologi... | computer science |
15,760 | Part-of-Speech Relevance Weights for Learning Word Embeddings | cs.CL | This paper proposes a model to learn word embeddings with weighted contexts
based on part-of-speech (POS) relevance weights. POS is a fundamental element
in natural language. However, state-of-the-art word embedding models fail to
consider it. This paper proposes to use position-dependent POS relevance
weighting matric... | computer science |
15,761 | Neural Text Generation from Structured Data with Application to the
Biography Domain | cs.CL | This paper introduces a neural model for concept-to-text generation that
scales to large, rich domains. We experiment with a new dataset of biographies
from Wikipedia that is an order of magnitude larger than existing resources
with over 700k samples. The dataset is also vastly more diverse with a 400k
vocabulary, comp... | computer science |
15,762 | Improving Information Extraction by Acquiring External Evidence with
Reinforcement Learning | cs.CL | Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, ex... | computer science |
15,763 | Classifying Syntactic Regularities for Hundreds of Languages | cs.CL | This paper presents a comparison of classification methods for linguistic
typology for the purpose of expanding an extensive, but sparse language
resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath,
2013). We experimented with a variety of regression and nearest-neighbor
methods for use in cla... | computer science |
15,764 | Prepositional Attachment Disambiguation Using Bilingual Parsing and
Alignments | cs.CL | In this paper, we attempt to solve the problem of Prepositional Phrase (PP)
attachments in English. The motivation for the work comes from NLP applications
like Machine Translation, for which, getting the correct attachment of
prepositions is very crucial. The idea is to correct the PP-attachments for a
sentence with t... | computer science |
15,765 | What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL
Datasets | cs.CL | In this paper, we claim that Vector Cosine, which is generally considered one
of the most efficient unsupervised measures for identifying word similarity in
Vector Space Models, can be outperformed by a completely unsupervised measure
that evaluates the extent of the intersection among the most associated
contexts of t... | computer science |
15,766 | Nine Features in a Random Forest to Learn Taxonomical Semantic Relations | cs.CL | ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms
and random words that is derived from the already introduced ROOT13 (Santus et
al., 2016). It relies on a Random Forest algorithm and nine unsupervised
corpus-based features. We evaluate it with a 10-fold cross validation on 9,600
pairs, equa... | computer science |
15,767 | ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms | cs.CL | In this paper, we describe ROOT13, a supervised system for the classification
of hypernyms, co-hyponyms and random words. The system relies on a Random
Forest algorithm and 13 unsupervised corpus-based features. We evaluate it with
a 10-fold cross validation on 9,600 pairs, equally distributed among the three
classes a... | computer science |
15,768 | Compilation as a Typed EDSL-to-EDSL Transformation | cs.CL | This article is about an implementation and compilation technique that is
used in RAW-Feldspar which is a complete rewrite of the Feldspar embedded
domain-specific language (EDSL) (Axelsson et al. 2010). Feldspar is high-level
functional language that generates efficient C code to run on embedded targets.
The gist of t... | computer science |
15,769 | A Readable Read: Automatic Assessment of Language Learning Materials
based on Linguistic Complexity | cs.CL | Corpora and web texts can become a rich language learning resource if we have
a means of assessing whether they are linguistically appropriate for learners
at a given proficiency level. In this paper, we aim at addressing this issue by
presenting the first approach for predicting linguistic complexity for Swedish
secon... | computer science |
15,770 | A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data | cs.CL | Understanding unstructured text is a major goal within natural language
processing. Comprehension tests pose questions based on short text passages to
evaluate such understanding. In this work, we investigate machine comprehension
on the challenging {\it MCTest} benchmark. Partly because of its limited size,
prior work... | computer science |
15,771 | Learning-Based Single-Document Summarization with Compression and
Anaphoricity Constraints | cs.CL | We present a discriminative model for single-document summarization that
integrally combines compression and anaphoricity constraints. Our model selects
textual units to include in the summary based on a rich set of sparse features
whose weights are learned on a large corpus. We allow for the deletion of
content within... | computer science |
15,772 | Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence
and Vector Cosine in VSMs | cs.CL | In this paper, we claim that vector cosine, which is generally considered
among the most efficient unsupervised measures for identifying word similarity
in Vector Space Models, can be outperformed by an unsupervised measure that
calculates the extent of the intersection among the most mutually dependent
contexts of the... | computer science |
15,773 | LSTM based Conversation Models | cs.CL | In this paper, we present a conversational model that incorporates both
context and participant role for two-party conversations. Different
architectures are explored for integrating participant role and context
information into a Long Short-term Memory (LSTM) language model. The
conversational model can function as a ... | computer science |
15,774 | Response Selection with Topic Clues for Retrieval-based Chatbots | cs.CL | We consider incorporating topic information into message-response matching to
boost responses with rich content in retrieval-based chatbots. To this end, we
propose a topic-aware convolutional neural tensor network (TACNTN). In TACNTN,
matching between a message and a response is not only conducted between a
message ve... | computer science |
15,775 | Compositional Sentence Representation from Character within Large
Context Text | cs.CL | This paper describes a Hierarchical Composition Recurrent Network (HCRN)
consisting of a 3-level hierarchy of compositional models: character, word and
sentence. This model is designed to overcome two problems of representing a
sentence on the basis of a constituent word sequence. The first is a
data-sparsity problem i... | computer science |
15,776 | Stance and Sentiment in Tweets | cs.CL | We can often detect from a person's utterances whether he/she is in favor of
or against a given target entity -- their stance towards the target. However, a
person may express the same stance towards a target by using negative or
positive language. Here for the first time we present a dataset of
tweet--target pairs ann... | computer science |
15,777 | Improving Automated Patent Claim Parsing: Dataset, System, and
Experiments | cs.CL | Off-the-shelf natural language processing software performs poorly when
parsing patent claims owing to their use of irregular language relative to the
corpora built from news articles and the web typically utilized to train this
software. Stopping short of the extensive and expensive process of accumulating
a large eno... | computer science |
15,778 | Detecting Context Dependence in Exercise Item Candidates Selected from
Corpora | cs.CL | We explore the factors influencing the dependence of single sentences on
their larger textual context in order to automatically identify candidate
sentences for language learning exercises from corpora which are presentable in
isolation. An in-depth investigation of this question has not been previously
carried out. Un... | computer science |
15,779 | Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec | cs.CL | Distributed dense word vectors have been shown to be effective at capturing
token-level semantic and syntactic regularities in language, while topic models
can form interpretable representations over documents. In this work, we
describe lda2vec, a model that learns dense word vectors jointly with
Dirichlet-distributed ... | computer science |
15,780 | Neural Recovery Machine for Chinese Dropped Pronoun | cs.CL | Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese,
Japanese etc. Previous work mainly focused on painstakingly exploring the
empirical features for DPs recovery. In this paper, we propose a neural
recovery machine (NRM) to model and recover DPs in Chinese, so that to avoid
the non-trivial feature... | computer science |
15,781 | On Improving Informativity and Grammaticality for Multi-Sentence
Compression | cs.CL | Multi Sentence Compression (MSC) is of great value to many real world
applications, such as guided microblog summarization, opinion summarization and
newswire summarization. Recently, word graph-based approaches have been
proposed and become popular in MSC. Their key assumption is that redundancy
among a set of related... | computer science |
15,782 | A corpus of preposition supersenses in English web reviews | cs.CL | We present the first corpus annotated with preposition supersenses,
unlexicalized categories for semantic functions that can be marked by English
prepositions (Schneider et al., 2015). That scheme improves upon its
predecessors to better facilitate comprehensive manual annotation. Moreover,
unlike the previous schemes,... | computer science |
15,783 | Problems With Evaluation of Word Embeddings Using Word Similarity Tasks | cs.CL | Lacking standardized extrinsic evaluation methods for vector representations
of words, the NLP community has relied heavily on word similarity tasks as a
proxy for intrinsic evaluation of word vectors. Word similarity evaluation,
which correlates the distance between vectors and human judgments of semantic
similarity i... | computer science |
15,784 | The Controlled Natural Language of Randall Munroe's Thing Explainer | cs.CL | It is rare that texts or entire books written in a Controlled Natural
Language (CNL) become very popular, but exactly this has happened with a book
that has been published last year. Randall Munroe's Thing Explainer uses only
the 1'000 most often used words of the English language together with drawn
pictures to explai... | computer science |
15,785 | GLEU Without Tuning | cs.CL | The GLEU metric was proposed for evaluating grammatical error corrections
using n-gram overlap with a set of reference sentences, as opposed to
precision/recall of specific annotated errors (Napoles et al., 2015). This
paper describes improvements made to the GLEU metric that address problems that
arise when using an i... | computer science |
15,786 | Coverage Embedding Models for Neural Machine Translation | cs.CL | In this paper, we enhance the attention-based neural machine translation
(NMT) by adding explicit coverage embedding models to alleviate issues of
repeating and dropping translations in NMT. For each source word, our model
starts with a full coverage embedding vector to track the coverage status, and
then keeps updatin... | computer science |
15,787 | Vocabulary Manipulation for Neural Machine Translation | cs.CL | In order to capture rich language phenomena, neural machine translation
models have to use a large vocabulary size, which requires high computing time
and large memory usage. In this paper, we alleviate this issue by introducing a
sentence-level or batch-level vocabulary, which is only a very small sub-set of
the full ... | computer science |
15,788 | Machine Comprehension Based on Learning to Rank | cs.CL | Machine comprehension plays an essential role in NLP and has been widely
explored with dataset like MCTest. However, this dataset is too simple and too
small for learning true reasoning abilities. \cite{hermann2015teaching}
therefore release a large scale news article dataset and propose a deep LSTM
reader system for m... | computer science |
15,789 | Real-Time Web Scale Event Summarization Using Sequential Decision Making | cs.CL | We present a system based on sequential decision making for the online
summarization of massive document streams, such as those found on the web.
Given an event of interest (e.g. "Boston marathon bombing"), our system is able
to filter the stream for relevance and produce a series of short text updates
describing the e... | computer science |
15,790 | Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic
Representation Learning | cs.CL | We introduce polyglot language models, recurrent neural network models
trained to predict symbol sequences in many different languages using shared
representations of symbols and conditioning on typological information about
the language to be predicted. We apply these to the problem of modeling phone
sequences---a dom... | computer science |
15,791 | Learning the Curriculum with Bayesian Optimization for Task-Specific
Word Representation Learning | cs.CL | We use Bayesian optimization to learn curricula for word representation
learning, optimizing performance on downstream tasks that depend on the learned
representations as features. The curricula are modeled by a linear ranking
function which is the scalar product of a learned weight vector and an
engineered feature vec... | computer science |
15,792 | Joint Embeddings of Hierarchical Categories and Entities | cs.CL | Due to the lack of structured knowledge applied in learning distributed
representation of categories, existing work cannot incorporate category
hierarchies into entity information.~We propose a framework that embeds
entities and categories into a semantic space by integrating structured
knowledge and taxonomy hierarchy... | computer science |
15,793 | On the Convergent Properties of Word Embedding Methods | cs.CL | Do word embeddings converge to learn similar things over different
initializations? How repeatable are experiments with word embeddings? Are all
word embedding techniques equally reliable? In this paper we propose evaluating
methods for learning word representations by their consistency across
initializations. We propo... | computer science |
15,794 | Which Learning Algorithms Can Generalize Identity-Based Rules to Novel
Inputs? | cs.CL | We propose a novel framework for the analysis of learning algorithms that
allows us to say when such algorithms can and cannot generalize certain
patterns from training data to test data. In particular we focus on situations
where the rule that must be learned concerns two components of a stimulus being
identical. We c... | computer science |
15,795 | Semantic Spaces | cs.CL | Any natural language can be considered as a tool for producing large
databases (consisting of texts, written, or discursive). This tool for its
description in turn requires other large databases (dictionaries, grammars
etc.). Nowadays, the notion of database is associated with computer processing
and computer memory. H... | computer science |
15,796 | Universal Dependencies for Learner English | cs.CL | We introduce the Treebank of Learner English (TLE), the first publicly
available syntactic treebank for English as a Second Language (ESL). The TLE
provides manually annotated POS tags and Universal Dependency (UD) trees for
5,124 sentences from the Cambridge First Certificate in English (FCE) corpus.
The UD annotation... | computer science |
15,797 | Occurrence Statistics of Entities, Relations and Types on the Web | cs.CL | The problem of collecting reliable estimates of occurrence of entities on the
open web forms the premise for this report. The models learned for tagging
entities cannot be expected to perform well when deployed on the web. This is
owing to the severe mismatch in the distributions of such entities on the web
and in the ... | computer science |
15,798 | Rationale-Augmented Convolutional Neural Networks for Text
Classification | cs.CL | We present a new Convolutional Neural Network (CNN) model for text
classification that jointly exploits labels on documents and their component
sentences. Specifically, we consider scenarios in which annotators explicitly
mark sentences (or snippets) that support their overall document
categorization, i.e., they provid... | computer science |
15,799 | Capturing divergence in dependency trees to improve syntactic projection | cs.CL | Obtaining syntactic parses is a crucial part of many NLP pipelines. However,
most of the world's languages do not have large amounts of syntactically
annotated corpora available for building parsers. Syntactic projection
techniques attempt to address this issue by using parallel corpora consisting
of resource-poor and ... | computer science |
15,800 | Anchoring and Agreement in Syntactic Annotations | cs.CL | We present a study on two key characteristics of human syntactic annotations:
anchoring and agreement. Anchoring is a well known cognitive bias in human
decision making, where judgments are drawn towards pre-existing values. We
study the influence of anchoring on a standard approach to creation of
syntactic resources w... | computer science |
15,801 | Machine Translation Evaluation: A Survey | cs.CL | We introduce the Machine Translation (MT) evaluation survey that contains
both manual and automatic evaluation methods. The traditional human evaluation
criteria mainly include the intelligibility, fidelity, fluency, adequacy,
comprehension, and informativeness. The advanced human assessments include
task-oriented meas... | computer science |
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