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16,902 | Improving Neural Parsing by Disentangling Model Combination and
Reranking Effects | cs.CL | Recent work has proposed several generative neural models for constituency
parsing that achieve state-of-the-art results. Since direct search in these
generative models is difficult, they have primarily been used to rescore
candidate outputs from base parsers in which decoding is more straightforward.
We first present ... | computer science |
16,903 | Refining Raw Sentence Representations for Textual Entailment Recognition
via Attention | cs.CL | In this paper we present the model used by the team Rivercorners for the 2017
RepEval shared task. First, our model separately encodes a pair of sentences
into variable-length representations by using a bidirectional LSTM. Later, it
creates fixed-length raw representations by means of simple aggregation
functions, whic... | computer science |
16,904 | Dataset for a Neural Natural Language Interface for Databases (NNLIDB) | cs.CL | Progress in natural language interfaces to databases (NLIDB) has been slow
mainly due to linguistic issues (such as language ambiguity) and domain
portability. Moreover, the lack of a large corpus to be used as a standard
benchmark has made data-driven approaches difficult to develop and compare. In
this paper, we revi... | computer science |
16,905 | A non-projective greedy dependency parser with bidirectional LSTMs | cs.CL | The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of
the Covington (2001) algorithm for non-projective dependency parsing. The
bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to
train a greedy parser with a dynamic oracle to mitigate error propagation. The
model participate... | computer science |
16,906 | Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data
Analysis | cs.CL | This paper presents the "Leipzig Corpus Miner", a technical infrastructure
for supporting qualitative and quantitative content analysis. The
infrastructure aims at the integration of 'close reading' procedures on
individual documents with procedures of 'distant reading', e.g. lexical
characteristics of large document c... | computer science |
16,907 | Modeling the dynamics of domain specific terminology in diachronic
corpora | cs.CL | In terminology work, natural language processing, and digital humanities,
several studies address the analysis of variations in context and meaning of
terms in order to detect semantic change and the evolution of terms. We
distinguish three different approaches to describe contextual variations:
methods based on the an... | computer science |
16,908 | A simple but tough-to-beat baseline for the Fake News Challenge stance
detection task | cs.CL | Identifying public misinformation is a complicated and challenging task.
Stance detection, i.e. determining the relative perspective a news source takes
towards a specific claim, is an important part of evaluating the veracity of
the assertion. Automating the process of stance detection would arguably
benefit human fac... | computer science |
16,909 | Geospatial Semantics | cs.CL | Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts i... | computer science |
16,910 | The Case for Being Average: A Mediocrity Approach to Style Masking and
Author Obfuscation | cs.CL | Users posting online expect to remain anonymous unless they have logged in,
which is often needed for them to be able to discuss freely on various topics.
Preserving the anonymity of a text's writer can be also important in some other
contexts, e.g., in the case of witness protection or anonymity programs.
However, eac... | computer science |
16,911 | N-GrAM: New Groningen Author-profiling Model | cs.CL | We describe our participation in the PAN 2017 shared task on Author
Profiling, identifying authors' gender and language variety for English,
Spanish, Arabic and Portuguese. We describe both the final, submitted system,
and a series of negative results. Our aim was to create a single model for both
gender and language, ... | computer science |
16,912 | A Critique of a Critique of Word Similarity Datasets: Sanity Check or
Unnecessary Confusion? | cs.CL | Critical evaluation of word similarity datasets is very important for
computational lexical semantics. This short report concerns the sanity check
proposed in Batchkarov et al. (2016) to evaluate several popular datasets such
as MC, RG and MEN -- the first two reportedly failed. I argue that this test is
unstable, offe... | computer science |
16,913 | Negative Sampling Improves Hypernymy Extraction Based on Projection
Learning | cs.CL | We present a new approach to extraction of hypernyms based on projection
learning and word embeddings. In contrast to classification-based approaches,
projection-based methods require no candidate hyponym-hypernym pairs. While it
is natural to use both positive and negative training examples in supervised
relation extr... | computer science |
16,914 | Predicting Causes of Reformulation in Intelligent Assistants | cs.CL | Intelligent assistants (IAs) such as Siri and Cortana conversationally
interact with users and execute a wide range of actions (e.g., searching the
Web, setting alarms, and chatting). IAs can support these actions through the
combination of various components such as automatic speech recognition, natural
language under... | computer science |
16,915 | Is writing style predictive of scientific fraud? | cs.CL | The problem of detecting scientific fraud using machine learning was recently
introduced, with initial, positive results from a model taking into account
various general indicators. The results seem to suggest that writing style is
predictive of scientific fraud. We revisit these initial experiments, and show
that the ... | computer science |
16,916 | Do Convolutional Networks need to be Deep for Text Classification ? | cs.CL | We study in this work the importance of depth in convolutional models for
text classification, either when character or word inputs are considered. We
show on 5 standard text classification and sentiment analysis tasks that deep
models indeed give better performances than shallow networks when the text
input is represe... | computer science |
16,917 | Developing a concept-level knowledge base for sentiment analysis in
Singlish | cs.CL | In this paper, we present Singlish sentiment lexicon, a concept-level
knowledge base for sentiment analysis that associates multiword expressions to
a set of emotion labels and a polarity value. Unlike many other sentiment
analysis resources, this lexicon is not built by manually labeling pieces of
knowledge coming fro... | computer science |
16,918 | Evaluating Semantic Parsing against a Simple Web-based Question
Answering Model | cs.CL | Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based qu... | computer science |
16,919 | LIUM-CVC Submissions for WMT17 Multimodal Translation Task | cs.CL | This paper describes the monomodal and multimodal Neural Machine Translation
systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal
Translation. We mainly explored two multimodal architectures where either
global visual features or convolutional feature maps are integrated in order to
benefit from visual... | computer science |
16,920 | LIUM Machine Translation Systems for WMT17 News Translation Task | cs.CL | This paper describes LIUM submissions to WMT17 News Translation Task for
English-German, English-Turkish, English-Czech and English-Latvian language
pairs. We train BPE-based attentive Neural Machine Translation systems with and
without factored outputs using the open source nmtpy framework. Competitive
scores were obt... | computer science |
16,921 | Cross-genre Document Retrieval: Matching between Conversational and
Formal Writings | cs.CL | This paper challenges a cross-genre document retrieval task, where the
queries are in formal writing and the target documents are in conversational
writing. In this task, a query, is a sentence extracted from either a summary
or a plot of an episode in a TV show, and the target document consists of
transcripts from the... | computer science |
16,922 | Rotations and Interpretability of Word Embeddings: the Case of the
Russian Language | cs.CL | Consider a continuous word embedding model. Usually, the cosines between word
vectors are used as a measure of similarity of words. These cosines do not
change under orthogonal transformations of the embedding space. We demonstrate
that, using some canonical orthogonal transformations from SVD, it is possible
both to i... | computer science |
16,923 | Open-Set Language Identification | cs.CL | We present the first open-set language identification experiments using
one-class classification. We first highlight the shortcomings of traditional
feature extraction methods and propose a hashing-based feature vectorization
approach as a solution. Using a dataset of 10 languages from different writing
systems, we tra... | computer science |
16,924 | Do Neural Nets Learn Statistical Laws behind Natural Language? | cs.CL | The performance of deep learning in natural language processing has been
spectacular, but the reasons for this success remain unclear because of the
inherent complexity of deep learning. This paper provides empirical evidence of
its effectiveness and of a limitation of neural networks for language
engineering. Precisel... | computer science |
16,925 | Automated Detection of Non-Relevant Posts on the Russian Imageboard
"2ch": Importance of the Choice of Word Representations | cs.CL | This study considers the problem of automated detection of non-relevant posts
on Web forums and discusses the approach of resolving this problem by
approximation it with the task of detection of semantic relatedness between the
given post and the opening post of the forum discussion thread. The
approximated task could ... | computer science |
16,926 | End-to-End Information Extraction without Token-Level Supervision | cs.CL | Most state-of-the-art information extraction approaches rely on token-level
labels to find the areas of interest in text. Unfortunately, these labels are
time-consuming and costly to create, and consequently, not available for many
real-life IE tasks. To make matters worse, token-level labels are usually not
the desire... | computer science |
16,927 | In-Order Transition-based Constituent Parsing | cs.CL | Both bottom-up and top-down strategies have been used for neural
transition-based constituent parsing. The parsing strategies differ in terms of
the order in which they recognize productions in the derivation tree, where
bottom-up strategies and top-down strategies take post-order and pre-order
traversal over trees, re... | computer science |
16,928 | Towards Bidirectional Hierarchical Representations for Attention-Based
Neural Machine Translation | cs.CL | This paper proposes a hierarchical attentional neural translation model which
focuses on enhancing source-side hierarchical representations by covering both
local and global semantic information using a bidirectional tree-based encoder.
To maximize the predictive likelihood of target words, a weighted variant of an
att... | computer science |
16,929 | To Normalize, or Not to Normalize: The Impact of Normalization on
Part-of-Speech Tagging | cs.CL | Does normalization help Part-of-Speech (POS) tagging accuracy on noisy,
non-canonical data? To the best of our knowledge, little is known on the actual
impact of normalization in a real-world scenario, where gold error detection is
not available. We investigate the effect of automatic normalization on POS
tagging of tw... | computer science |
16,930 | LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task | cs.CL | This paper presents the LIG-CRIStAL submission to the shared Automatic Post-
Editing task of WMT 2017. We propose two neural post-editing models: a
monosource model with a task-specific attention mechanism, which performs
particularly well in a low-resource scenario; and a chained architecture which
makes use of the so... | computer science |
16,931 | Neural Reranking for Named Entity Recognition | cs.CL | We propose a neural reranking system for named entity recognition (NER). The
basic idea is to leverage recurrent neural network models to learn
sentence-level patterns that involve named entity mentions. In particular,
given an output sentence produced by a baseline NER model, we replace all
entity mentions, such as \t... | computer science |
16,932 | Exploring text datasets by visualizing relevant words | cs.CL | When working with a new dataset, it is important to first explore and
familiarize oneself with it, before applying any advanced machine learning
algorithms. However, to the best of our knowledge, no tools exist that quickly
and reliably give insight into the contents of a selection of documents with
respect to what dis... | computer science |
16,933 | A Simple Language Model based on PMI Matrix Approximations | cs.CL | In this study, we introduce a new approach for learning language models by
training them to estimate word-context pointwise mutual information (PMI), and
then deriving the desired conditional probabilities from PMI at test time.
Specifically, we show that with minor modifications to word2vec's algorithm, we
get princip... | computer science |
16,934 | MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity
Linking Approach | cs.CL | Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We addr... | computer science |
16,935 | Unsupervised Iterative Deep Learning of Speech Features and Acoustic
Tokens with Applications to Spoken Term Detection | cs.CL | In this paper we aim to automatically discover high quality frame-level
speech features and acoustic tokens directly from unlabeled speech data. A
Multi-granular Acoustic Tokenizer (MAT) was proposed for automatic discovery of
multiple sets of acoustic tokens from the given corpus. Each acoustic token set
is specified ... | computer science |
16,936 | Improved Neural Machine Translation with a Syntax-Aware Encoder and
Decoder | cs.CL | Most neural machine translation (NMT) models are based on the sequential
encoder-decoder framework, which makes no use of syntactic information. In this
paper, we improve this model by explicitly incorporating source-side syntactic
trees. More specifically, we propose (1) a bidirectional tree encoder which
learns both ... | computer science |
16,937 | Top-Rank Enhanced Listwise Optimization for Statistical Machine
Translation | cs.CL | Pairwise ranking methods are the basis of many widely used discriminative
training approaches for structure prediction problems in natural language
processing(NLP). Decomposing the problem of ranking hypotheses into pairwise
comparisons enables simple and efficient solutions. However, neglecting the
global ordering of ... | computer science |
16,938 | Detecting Intentional Lexical Ambiguity in English Puns | cs.CL | The article describes a model of automatic analysis of puns, where a word is
intentionally used in two meanings at the same time (the target word). We
employ Roget's Thesaurus to discover two groups of words which, in a pun, form
around two abstract bits of meaning (semes). They become a semantic vector,
based on which... | computer science |
16,939 | PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in
Automatic Pun Recognition and Interpretation | cs.CL | The article describes a model of automatic interpretation of English puns,
based on Roget's Thesaurus, and its implementation, PunFields. In a pun, the
algorithm discovers two groups of words that belong to two main semantic
fields. The fields become a semantic vector based on which an SVM classifier
learns to recogniz... | computer science |
16,940 | Story Generation from Sequence of Independent Short Descriptions | cs.CL | Existing Natural Language Generation (NLG) systems are weak AI systems and
exhibit limited capabilities when language generation tasks demand higher
levels of creativity, originality and brevity. Effective solutions or, at least
evaluations of modern NLG paradigms for such creative tasks have been elusive,
unfortunatel... | computer science |
16,941 | On the State of the Art of Evaluation in Neural Language Models | cs.CL | Ongoing innovations in recurrent neural network architectures have provided a
steady influx of apparently state-of-the-art results on language modelling
benchmarks. However, these have been evaluated using differing code bases and
limited computational resources, which represent uncontrolled sources of
experimental var... | computer science |
16,942 | Spherical Paragraph Model | cs.CL | Representing texts as fixed-length vectors is central to many language
processing tasks. Most traditional methods build text representations based on
the simple Bag-of-Words (BoW) representation, which loses the rich semantic
relations between words. Recent advances in natural language processing have
shown that semant... | computer science |
16,943 | A Short Survey of Biomedical Relation Extraction Techniques | cs.CL | Biomedical information is growing rapidly in the recent years and retrieving
useful data through information extraction system is getting more attention. In
the current research, we focus on different aspects of relation extraction
techniques in biomedical domain and briefly describe the state-of-the-art for
relation e... | computer science |
16,944 | Encoding Word Confusion Networks with Recurrent Neural Networks for
Dialog State Tracking | cs.CL | This paper presents our novel method to encode word confusion networks, which
can represent a rich hypothesis space of automatic speech recognition systems,
via recurrent neural networks. We demonstrate the utility of our approach for
the task of dialog state tracking in spoken dialog systems that relies on
automatic s... | computer science |
16,945 | Deep Active Learning for Named Entity Recognition | cs.CL | Deep learning has yielded state-of-the-art performance on many natural
language processing tasks including named entity recognition (NER). However,
this typically requires large amounts of labeled data. In this work, we
demonstrate that the amount of labeled training data can be drastically reduced
when deep learning i... | computer science |
16,946 | Measuring Thematic Fit with Distributional Feature Overlap | cs.CL | In this paper, we introduce a new distributional method for modeling
predicate-argument thematic fit judgments. We use a syntax-based DSM to build a
prototypical representation of verb-specific roles: for every verb, we extract
the most salient second order contexts for each of its roles (i.e. the most
salient dimensio... | computer science |
16,947 | Argotario: Computational Argumentation Meets Serious Games | cs.CL | An important skill in critical thinking and argumentation is the ability to
spot and recognize fallacies. Fallacious arguments, omnipresent in
argumentative discourse, can be deceptive, manipulative, or simply leading to
`wrong moves' in a discussion. Despite their importance, argumentation scholars
and NLP researchers... | computer science |
16,948 | Modeling Target-Side Inflection in Neural Machine Translation | cs.CL | NMT systems have problems with large vocabulary sizes. Byte-pair encoding
(BPE) is a popular approach to solving this problem, but while BPE allows the
system to generate any target-side word, it does not enable effective
generalization over the rich vocabulary in morphologically rich languages with
strong inflectional... | computer science |
16,949 | Discovering topics in text datasets by visualizing relevant words | cs.CL | When dealing with large collections of documents, it is imperative to quickly
get an overview of the texts' contents. In this paper we show how this can be
achieved by using a clustering algorithm to identify topics in the dataset and
then selecting and visualizing relevant words, which distinguish a group of
documents... | computer science |
16,950 | Improving Language Modeling using Densely Connected Recurrent Neural
Networks | cs.CL | In this paper, we introduce the novel concept of densely connected layers
into recurrent neural networks. We evaluate our proposed architecture on the
Penn Treebank language modeling task. We show that we can obtain similar
perplexity scores with six times fewer parameters compared to a standard
stacked 2-layer LSTM mo... | computer science |
16,951 | Expect the unexpected: Harnessing Sentence Completion for Sarcasm
Detection | cs.CL | The trigram `I love being' is expected to be followed by positive words such
as `happy'. In a sarcastic sentence, however, the word `ignored' may be
observed. The expected and the observed words are, thus, incongruous. We model
sarcasm detection as the task of detecting incongruity between an observed and
an expected w... | computer science |
16,952 | Sentence-level quality estimation by predicting HTER as a
multi-component metric | cs.CL | This submission investigates alternative machine learning models for
predicting the HTER score on the sentence level. Instead of directly predicting
the HTER score, we suggest a model that jointly predicts the amount of the 4
distinct post-editing operations, which are then used to calculate the HTER
score. This also g... | computer science |
16,953 | Fast and Accurate OOV Decoder on High-Level Features | cs.CL | This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-depende... | computer science |
16,954 | A Sub-Character Architecture for Korean Language Processing | cs.CL | We introduce a novel sub-character architecture that exploits a unique
compositional structure of the Korean language. Our method decomposes each
character into a small set of primitive phonetic units called jamo letters from
which character- and word-level representations are induced. The jamo letters
divulge syntacti... | computer science |
16,955 | Improving Discourse Relation Projection to Build Discourse Annotated
Corpora | cs.CL | The naive approach to annotation projection is not effective to project
discourse annotations from one language to another because implicit discourse
relations are often changed to explicit ones and vice-versa in the translation.
In this paper, we propose a novel approach based on the intersection between
statistical w... | computer science |
16,956 | Large-Scale Goodness Polarity Lexicons for Community Question Answering | cs.CL | We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher th... | computer science |
16,957 | Revisiting Selectional Preferences for Coreference Resolution | cs.CL | Selectional preferences have long been claimed to be essential for
coreference resolution. However, they are mainly modeled only implicitly by
current coreference resolvers. We propose a dependency-based embedding model of
selectional preferences which allows fine-grained compatibility judgments with
high coverage. We ... | computer science |
16,958 | Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling
Tasks | cs.CL | Selecting optimal parameters for a neural network architecture can often make
the difference between mediocre and state-of-the-art performance. However,
little is published which parameters and design choices should be evaluated or
selected making the correct hyperparameter optimization often a "black art that
requires... | computer science |
16,959 | Shallow reading with Deep Learning: Predicting popularity of online
content using only its title | cs.CL | With the ever decreasing attention span of contemporary Internet users, the
title of online content (such as a news article or video) can be a major factor
in determining its popularity. To take advantage of this phenomenon, we propose
a new method based on a bidirectional Long Short-Term Memory (LSTM) neural
network d... | computer science |
16,960 | Why We Need New Evaluation Metrics for NLG | cs.CL | The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
this paper, we motivate the need for novel, system- and data-independent
automatic evaluation methods: We investigate a wide range of metrics, including
state-of-the-art word-based and novel grammar-based ones, and demonstrate that
they only ... | computer science |
16,961 | Unsupervised, Knowledge-Free, and Interpretable Word Sense
Disambiguation | cs.CL | Interpretability of a predictive model is a powerful feature that gains the
trust of users in the correctness of the predictions. In word sense
disambiguation (WSD), knowledge-based systems tend to be much more
interpretable than knowledge-free counterparts as they rely on the wealth of
manually-encoded elements repres... | computer science |
16,962 | SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New
Models and Search Strategies | cs.CL | This paper introduces SGNMT, our experimental platform for machine
translation research. SGNMT provides a generic interface to neural and symbolic
scoring modules (predictors) with left-to-right semantic such as translation
models like NMT, language models, translation lattices, $n$-best lists or other
kinds of scores ... | computer science |
16,963 | Cross-Lingual Induction and Transfer of Verb Classes Based on Word
Vector Space Specialisation | cs.CL | Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is... | computer science |
16,964 | Reconstruction of Word Embeddings from Sub-Word Parameters | cs.CL | Pre-trained word embeddings improve the performance of a neural model at the
cost of increasing the model size. We propose to benefit from this resource
without paying the cost by operating strictly at the sub-lexical level. Our
approach is quite simple: before task-specific training, we first optimize
sub-word paramet... | computer science |
16,965 | Mimicking Word Embeddings using Subword RNNs | cs.CL | Word embeddings improve generalization over lexical features by placing each
word in a lower-dimensional space, using distributional information obtained
from unlabeled data. However, the effectiveness of word embeddings for
downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which
embeddings do not e... | computer science |
16,966 | Split and Rephrase | cs.CL | We propose a new sentence simplification task (Split-and-Rephrase) where the
aim is to split a complex sentence into a meaning preserving sequence of
shorter sentences. Like sentence simplification, splitting-and-rephrasing has
the potential of benefiting both natural language processing and societal
applications. Beca... | computer science |
16,967 | Emotion Detection from Text | cs.CL | Emotions are perceptions of changes in the human body such as heart rate,
breathing rate, perspiration, and hormone levels. These conscious experiences
are complex and studied extensively in different fields including computer
science. Lack of facial expressions and voice modulations make detecting
emotions from text a... | computer science |
16,968 | End-to-end Neural Coreference Resolution | cs.CL | We introduce the first end-to-end coreference resolution model and show that
it significantly outperforms all previous work without using a syntactic parser
or hand-engineered mention detector. The key idea is to directly consider all
spans in a document as potential mentions and learn distributions over possible
antec... | computer science |
16,969 | A Pilot Study of Domain Adaptation Effect for Neural Abstractive
Summarization | cs.CL | We study the problem of domain adaptation for neural abstractive
summarization. We make initial efforts in investigating what information can be
transferred to a new domain. Experimental results on news stories and opinion
articles indicate that neural summarization model benefits from pre-training
based on extractive ... | computer science |
16,970 | Identifying civilians killed by police with distantly supervised
entity-event extraction | cs.CL | We propose a new, socially-impactful task for natural language processing:
from a news corpus, extract names of persons who have been killed by police. We
present a newly collected police fatality corpus, which we release publicly,
and present a model to solve this problem that uses EM-based distant
supervision with lo... | computer science |
16,971 | Predicting the Gender of Indonesian Names | cs.CL | We investigated a way to predict the gender of a name using character-level
Long-Short Term Memory (char-LSTM). We compared our method with some
conventional machine learning methods, namely Naive Bayes, logistic regression,
and XGBoost with n-grams as the features. We evaluated the models on a dataset
consisting of th... | computer science |
16,972 | Native Language Identification on Text and Speech | cs.CL | This paper presents an ensemble system combining the output of multiple SVM
classifiers to native language identification (NLI). The system was submitted
to the NLI Shared Task 2017 fusion track which featured students essays and
spoken responses in form of audio transcriptions and iVectors by non-native
English speake... | computer science |
16,973 | "i have a feeling trump will win..................": Forecasting Winners
and Losers from User Predictions on Twitter | cs.CL | Social media users often make explicit predictions about upcoming events.
Such statements vary in the degree of certainty the author expresses toward the
outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win"
or "No way Leonardo wins!". Can popular beliefs on social media predict who
will win? ... | computer science |
16,974 | Tensor Fusion Network for Multimodal Sentiment Analysis | cs.CL | Multimodal sentiment analysis is an increasingly popular research area, which
extends the conventional language-based definition of sentiment analysis to a
multimodal setup where other relevant modalities accompany language. In this
paper, we pose the problem of multimodal sentiment analysis as modeling
intra-modality ... | computer science |
16,975 | Composing Distributed Representations of Relational Patterns | cs.CL | Learning distributed representations for relation instances is a central
technique in downstream NLP applications. In order to address semantic modeling
of relational patterns, this paper constructs a new dataset that provides
multiple similarity ratings for every pair of relational patterns on the
existing dataset. In... | computer science |
16,976 | Hierarchical Embeddings for Hypernymy Detection and Directionality | cs.CL | We present a novel neural model HyperVec to learn hierarchical embeddings for
hypernymy detection and directionality. While previous embeddings have shown
limitations on prototypical hypernyms, HyperVec represents an unsupervised
measure where embeddings are learned in a specific order and capture the
hypernym$-$hypony... | computer science |
16,977 | Fine Grained Citation Span for References in Wikipedia | cs.CL | \emph{Verifiability} is one of the core editing principles in Wikipedia,
editors being encouraged to provide citations for the added content. For a
Wikipedia article, determining the \emph{citation span} of a citation, i.e.
what content is covered by a citation, is important as it helps decide for
which content citatio... | computer science |
16,978 | Using Argument-based Features to Predict and Analyse Review Helpfulness | cs.CL | We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusion... | computer science |
16,979 | Rule-Based Spanish Morphological Analyzer Built From Spell Checking
Lexicon | cs.CL | Preprocessing tools for automated text analysis have become more widely
available in major languages, but non-English tools are often still limited in
their functionality. When working with Spanish-language text, researchers can
easily find tools for tokenization and stemming, but may not have the means to
extract more... | computer science |
16,980 | A Sequential Model for Classifying Temporal Relations between
Intra-Sentence Events | cs.CL | We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our appr... | computer science |
16,981 | Event Coreference Resolution by Iteratively Unfolding Inter-dependencies
among Events | cs.CL | We introduce a novel iterative approach for event coreference resolution that
gradually builds event clusters by exploiting inter-dependencies among event
mentions within the same chain as well as across event chains. Among event
mentions in the same chain, we distinguish within- and cross-document event
coreference li... | computer science |
16,982 | Analysing Errors of Open Information Extraction Systems | cs.CL | We report results on benchmarking Open Information Extraction (OIE) systems
using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Our comprehensive benchmark contains three data sets from the news domain and
one data set from Wikipedia with overall 4522 labeled sentences and 11243
binary or n-ar... | computer science |
16,983 | CAp 2017 challenge: Twitter Named Entity Recognition | cs.CL | The paper describes the CAp 2017 challenge. The challenge concerns the
problem of Named Entity Recognition (NER) for tweets written in French. We
first present the data preparation steps we followed for constructing the
dataset released in the framework of the challenge. We begin by demonstrating
why NER for tweets is ... | computer science |
16,984 | Transition-Based Generation from Abstract Meaning Representations | cs.CL | This work addresses the task of generating English sentences from Abstract
Meaning Representation (AMR) graphs. To cope with this task, we transform each
input AMR graph into a structure similar to a dependency tree and annotate it
with syntactic information by applying various predefined actions to it.
Subsequently, a... | computer science |
16,985 | Improve Lexicon-based Word Embeddings By Word Sense Disambiguation | cs.CL | There have been some works that learn a lexicon together with the corpus to
improve the word embeddings. However, they either model the lexicon separately
but update the neural networks for both the corpus and the lexicon by the same
likelihood, or minimize the distance between all of the synonym pairs in the
lexicon. ... | computer science |
16,986 | Deep Architectures for Neural Machine Translation | cs.CL | It has been shown that increasing model depth improves the quality of neural
machine translation. However, different architectural variants to increase
model depth have been proposed, and so far, there has been no thorough
comparative study.
In this work, we describe and evaluate several existing approaches to
introd... | computer science |
16,987 | Global Normalization of Convolutional Neural Networks for Joint Entity
and Relation Classification | cs.CL | We introduce globally normalized convolutional neural networks for joint
entity classification and relation extraction. In particular, we propose a way
to utilize a linear-chain conditional random field output layer for predicting
entity types and relations between entities at the same time. Our experiments
show that g... | computer science |
16,988 | AMR Parsing using Stack-LSTMs | cs.CL | We present a transition-based AMR parser that directly generates AMR parses
from plain text. We use Stack-LSTMs to represent our parser state and make
decisions greedily. In our experiments, we show that our parser achieves very
competitive scores on English using only AMR training data. Adding additional
information, ... | computer science |
16,989 | Macro Grammars and Holistic Triggering for Efficient Semantic Parsing | cs.CL | To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cach... | computer science |
16,990 | Machine Translation at Booking.com: Journey and Lessons Learned | cs.CL | We describe our recently developed neural machine translation (NMT) system
and benchmark it against our own statistical machine translation (SMT) system
as well as two other general purpose online engines (statistical and neural).
We present automatic and human evaluation results of the translation output
provided by e... | computer science |
16,991 | Question Dependent Recurrent Entity Network for Question Answering | cs.CL | Question Answering is a task which requires building models capable of
providing answers to questions expressed in human language. Full question
answering involves some form of reasoning ability. We introduce a neural
network architecture for this task, which is a form of $Memory\ Network$, that
recognizes entities and... | computer science |
16,992 | Synthesising Sign Language from semantics, approaching "from the target
and back" | cs.CL | We present a Sign Language modelling approach allowing to build grammars and
create linguistic input for Sign synthesis through avatars. We comment on the
type of grammar it allows to build, and observe a resemblance between the
resulting expressions and traditional semantic representations. Comparing the
ways in which... | computer science |
16,993 | Challenges in Data-to-Document Generation | cs.CL | Recent neural models have shown significant progress on the problem of
generating short descriptive texts conditioned on a small number of database
records. In this work, we suggest a slightly more difficult data-to-text
generation task, and investigate how effective current approaches are on this
task. In particular, ... | computer science |
16,994 | Learning Word Relatedness over Time | cs.CL | Search systems are often focused on providing relevant results for the "now",
assuming both corpora and user needs that focus on the present. However, many
corpora today reflect significant longitudinal collections ranging from 20
years of the Web to hundreds of years of digitized newspapers and books.
Understanding th... | computer science |
16,995 | ShotgunWSD: An unsupervised algorithm for global word sense
disambiguation inspired by DNA sequencing | cs.CL | In this paper, we present a novel unsupervised algorithm for word sense
disambiguation (WSD) at the document level. Our algorithm is inspired by a
widely-used approach in the field of genetics for whole genome sequencing,
known as the Shotgun sequencing technique. The proposed WSD algorithm is based
on three main steps... | computer science |
16,996 | From Image to Text Classification: A Novel Approach based on Clustering
Word Embeddings | cs.CL | In this paper, we propose a novel approach for text classification based on
clustering word embeddings, inspired by the bag of visual words model, which is
widely used in computer vision. After each word in a collection of documents is
represented as word vector using a pre-trained word embeddings model, a k-means
algo... | computer science |
16,997 | The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference
with Sentence Representations | cs.CL | This paper presents the results of the RepEval 2017 Shared Task, which
evaluated neural network sentence representation learning models on the
Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by
Williams et al. (2017). All of the five participating teams beat the
bidirectional LSTM (BiLSTM) ... | computer science |
16,998 | Fast calculation of entropy with Zhang's estimator | cs.CL | Entropy is a fundamental property of a repertoire. Here, we present an
efficient algorithm to estimate the entropy of types with the help of Zhang's
estimator. The algorithm takes advantage of the fact that the number of
different frequencies in a text is in general much smaller than the number of
types. We justify the... | computer science |
16,999 | Can string kernels pass the test of time in Native Language
Identification? | cs.CL | We describe a machine learning approach for the 2017 shared task on Native
Language Identification (NLI). The proposed approach combines several kernels
using multiple kernel learning. While most of our kernels are based on
character p-grams (also known as n-grams) extracted from essays or speech
transcripts, we also u... | computer science |
17,000 | SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO
Data Set | cs.CL | This paper presents an augmentation of MSCOCO dataset where speech is added
to image and text. Speech captions are generated using text-to-speech (TTS)
synthesis resulting in 616,767 spoken captions (more than 600h) paired with
images. Disfluencies and speed perturbation are added to the signal in order to
sound more n... | computer science |
17,001 | All that is English may be Hindi: Enhancing language identification
through automatic ranking of likeliness of word borrowing in social media | cs.CL | In this paper, we present a set of computational methods to identify the
likeliness of a word being borrowed, based on the signals from social media. In
terms of Spearman correlation coefficient values, our methods perform more than
two times better (nearly 0.62) in predicting the borrowing likeliness compared
to the b... | computer science |
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