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15,902 | Full-Time Supervision based Bidirectional RNN for Factoid Question
Answering | cs.CL | Recently, bidirectional recurrent neural network (BRNN) has been widely used
for question answering (QA) tasks with promising performance. However, most
existing BRNN models extract the information of questions and answers by
directly using a pooling operation to generate the representation for loss or
similarity calcu... | computer science |
15,903 | A Nonparametric Bayesian Approach for Spoken Term detection by Example
Query | cs.CL | State of the art speech recognition systems use data-intensive
context-dependent phonemes as acoustic units. However, these approaches do not
translate well to low resourced languages where large amounts of training data
is not available. For such languages, automatic discovery of acoustic units is
critical. In this pa... | computer science |
15,904 | The Role of CNL and AMR in Scalable Abstractive Summarization for
Multilingual Media Monitoring | cs.CL | In the era of Big Data and Deep Learning, there is a common view that machine
learning approaches are the only way to cope with the robust and scalable
information extraction and summarization. It has been recently proposed that
the CNL approach could be scaled up, building on the concept of embedded CNL
and, thus, all... | computer science |
15,905 | A Data-Driven Approach for Semantic Role Labeling from Induced Grammar
Structures in Language | cs.CL | Semantic roles play an important role in extracting knowledge from text.
Current unsupervised approaches utilize features from grammar structures, to
induce semantic roles. The dependence on these grammars, however, makes it
difficult to adapt to noisy and new languages. In this paper we develop a
data-driven approach ... | computer science |
15,906 | Incremental Parsing with Minimal Features Using Bi-Directional LSTM | cs.CL | Recently, neural network approaches for parsing have largely automated the
combination of individual features, but still rely on (often a larger number
of) atomic features created from human linguistic intuition, and potentially
omitting important global context. To further reduce feature engineering to the
bare minimu... | computer science |
15,907 | Neural Morphological Tagging from Characters for Morphologically Rich
Languages | cs.CL | This paper investigates neural character-based morphological tagging for
languages with complex morphology and large tag sets. We systematically explore
a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain
character-based word vectors combined with bidirectional LSTMs to model
across-word con... | computer science |
15,908 | Correlation-based Intrinsic Evaluation of Word Vector Representations | cs.CL | We introduce QVEC-CCA--an intrinsic evaluation metric for word vector
representations based on correlations of learned vectors with features
extracted from linguistic resources. We show that QVEC-CCA scores are an
effective proxy for a range of extrinsic semantic and syntactic tasks. We also
show that the proposed eval... | computer science |
15,909 | The word entropy of natural languages | cs.CL | The average uncertainty associated with words is an information-theoretic
concept at the heart of quantitative and computational linguistics. The entropy
has been established as a measure of this average uncertainty - also called
average information content. We here use parallel texts of 21 languages to
establish the n... | computer science |
15,910 | Semantic Parsing to Probabilistic Programs for Situated Question
Answering | cs.CL | Situated question answering is the problem of answering questions about an
environment such as an image or diagram. This problem requires jointly
interpreting a question and an environment using background knowledge to select
the correct answer. We present Parsing to Probabilistic Programs (P3), a novel
situated questi... | computer science |
15,911 | CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation
Tasks | cs.CL | Neural sequence to sequence learning recently became a very promising
paradigm in machine translation, achieving competitive results with statistical
phrase-based systems. In this system description paper, we attempt to utilize
several recently published methods used for neural sequential learning in order
to build sys... | computer science |
15,912 | A Sentence Compression Based Framework to Query-Focused Multi-Document
Summarization | cs.CL | We consider the problem of using sentence compression techniques to
facilitate query-focused multi-document summarization. We present a
sentence-compression-based framework for the task, and design a series of
learning-based compression models built on parse trees. An innovative beam
search decoder is proposed to effic... | computer science |
15,913 | Evaluation method of word embedding by roots and affixes | cs.CL | Word embedding has been shown to be remarkably effective in a lot of Natural
Language Processing tasks. However, existing models still have a couple of
limitations in interpreting the dimensions of word vector. In this paper, we
provide a new approach---roots and affixes model(RAAM)---to interpret it from
the intrinsic... | computer science |
15,914 | Issues in evaluating semantic spaces using word analogies | cs.CL | The offset method for solving word analogies has become a standard evaluation
tool for vector-space semantic models: it is considered desirable for a space
to represent semantic relations as consistent vector offsets. We show that the
method's reliance on cosine similarity conflates offset consistency with
largely irre... | computer science |
15,915 | The emotional arcs of stories are dominated by six basic shapes | cs.CL | Advances in computing power, natural language processing, and digitization of
text now make it possible to study a culture's evolution through its texts
using a "big data" lens. Our ability to communicate relies in part upon a
shared emotional experience, with stories often following distinct emotional
trajectories and... | computer science |
15,916 | Sequential Convolutional Neural Networks for Slot Filling in Spoken
Language Understanding | cs.CL | We investigate the usage of convolutional neural networks (CNNs) for the slot
filling task in spoken language understanding. We propose a novel CNN
architecture for sequence labeling which takes into account the previous
context words with preserved order information and pays special attention to
the current word with ... | computer science |
15,917 | Unsupervised Topic Modeling Approaches to Decision Summarization in
Spoken Meetings | cs.CL | We present a token-level decision summarization framework that utilizes the
latent topic structures of utterances to identify "summary-worthy" words.
Concretely, a series of unsupervised topic models is explored and experimental
results show that fine-grained topic models, which discover topics at the
utterance-level r... | computer science |
15,918 | Focused Meeting Summarization via Unsupervised Relation Extraction | cs.CL | We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarizatio... | computer science |
15,919 | Corpus-level Fine-grained Entity Typing Using Contextual Information | cs.CL | This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We prop... | computer science |
15,920 | Intrinsic Subspace Evaluation of Word Embedding Representations | cs.CL | We introduce a new methodology for intrinsic evaluation of word
representations. Specifically, we identify four fundamental criteria based on
the characteristics of natural language that pose difficulties to NLP systems;
and develop tests that directly show whether or not representations contain the
subspaces necessary... | computer science |
15,921 | Word sense disambiguation: a complex network approach | cs.CL | In recent years, concepts and methods of complex networks have been employed
to tackle the word sense disambiguation (WSD) task by representing words as
nodes, which are connected if they are semantically similar. Despite the
increasingly number of studies carried out with such models, most of them use
networks just to... | computer science |
15,922 | Summarizing Decisions in Spoken Meetings | cs.CL | This paper addresses the problem of summarizing decisions in spoken meetings:
our goal is to produce a concise {\it decision abstract} for each meeting
decision. We explore and compare token-level and dialogue act-level automatic
summarization methods using both unsupervised and supervised learning
frameworks. In the s... | computer science |
15,923 | Leveraging Semantic Web Search and Browse Sessions for Multi-Turn Spoken
Dialog Systems | cs.CL | Training statistical dialog models in spoken dialog systems (SDS) requires
large amounts of annotated data. The lack of scalable methods for data mining
and annotation poses a significant hurdle for state-of-the-art statistical
dialog managers. This paper presents an approach that directly leverage
billions of web sear... | computer science |
15,924 | Learning for Biomedical Information Extraction: Methodological Review of
Recent Advances | cs.CL | Biomedical information extraction (BioIE) is important to many applications,
including clinical decision support, integrative biology, and
pharmacovigilance, and therefore it has been an active research. Unlike
existing reviews covering a holistic view on BioIE, this review focuses on
mainly recent advances in learning... | computer science |
15,925 | Functional Distributional Semantics | cs.CL | Vector space models have become popular in distributional semantics, despite
the challenges they face in capturing various semantic phenomena. We propose a
novel probabilistic framework which draws on both formal semantics and recent
advances in machine learning. In particular, we separate predicates from the
entities ... | computer science |
15,926 | This before That: Causal Precedence in the Biomedical Domain | cs.CL | Causal precedence between biochemical interactions is crucial in the
biomedical domain, because it transforms collections of individual
interactions, e.g., bindings and phosphorylations, into the causal mechanisms
needed to inform meaningful search and inference. Here, we analyze causal
precedence in the biomedical dom... | computer science |
15,927 | Evaluating Informal-Domain Word Representations With UrbanDictionary | cs.CL | Existing corpora for intrinsic evaluation are not targeted towards tasks in
informal domains such as Twitter or news comment forums. We want to test
whether a representation of informal words fulfills the promise of eliding
explicit text normalization as a preprocessing step. One possible evaluation
metric for such dom... | computer science |
15,928 | Topic Aware Neural Response Generation | cs.CL | We consider incorporating topic information into the sequence-to-sequence
framework to generate informative and interesting responses for chatbots. To
this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The
model utilizes topics to simulate prior knowledge of human that guides them to
form infor... | computer science |
15,929 | Predicting the Relative Difficulty of Single Sentences With and Without
Surrounding Context | cs.CL | The problem of accurately predicting relative reading difficulty across a set
of sentences arises in a number of important natural language applications,
such as finding and curating effective usage examples for intelligent language
tutoring systems. Yet while significant research has explored document- and
passage-lev... | computer science |
15,930 | Network-Efficient Distributed Word2vec Training System for Large
Vocabularies | cs.CL | Word2vec is a popular family of algorithms for unsupervised training of dense
vector representations of words on large text corpuses. The resulting vectors
have been shown to capture semantic relationships among their corresponding
words, and have shown promise in reducing a number of natural language
processing (NLP) ... | computer science |
15,931 | SelQA: A New Benchmark for Selection-based Question Answering | cs.CL | This paper presents a new selection-based question answering dataset, SelQA.
The dataset consists of questions generated through crowdsourcing and sentence
length answers that are drawn from the ten most prevalent topics in the English
Wikipedia. We introduce a corpus annotation scheme that enhances the generation
of l... | computer science |
15,932 | Recurrent Neural Networks for Dialogue State Tracking | cs.CL | This paper discusses models for dialogue state tracking using recurrent
neural networks (RNN). We present experiments on the standard dialogue state
tracking (DST) dataset, DSTC2. On the one hand, RNN models became the state of
the art models in DST, on the other hand, most state-of-the-art models are only
turn-based a... | computer science |
15,933 | Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy | cs.CL | Speech recognition, especially name recognition, is widely used in phone
services such as company directory dialers, stock quote providers or location
finders. It is usually challenging due to pronunciation variations. This paper
proposes an efficient and robust data-driven technique which automatically
learns acceptab... | computer science |
15,934 | Relation extraction from clinical texts using domain invariant
convolutional neural network | cs.CL | In recent years extracting relevant information from biomedical and clinical
texts such as research articles, discharge summaries, or electronic health
records have been a subject of many research efforts and shared challenges.
Relation extraction is the process of detecting and classifying the semantic
relation among ... | computer science |
15,935 | Recurrent neural network models for disease name recognition using
domain invariant features | cs.CL | Hand-crafted features based on linguistic and domain-knowledge play crucial
role in determining the performance of disease name recognition systems. Such
methods are further limited by the scope of these features or in other words,
their ability to cover the contexts or word dependencies within a sentence. In
this work... | computer science |
15,936 | Neural Network-based Word Alignment through Score Aggregation | cs.CL | We present a simple neural network for word alignment that builds source and
target word window representations to compute alignment scores for sentence
pairs. To enable unsupervised training, we use an aggregation operation that
summarizes the alignment scores for a given target word. A soft-margin
objective increases... | computer science |
15,937 | Exploring Prediction Uncertainty in Machine Translation Quality
Estimation | cs.CL | Machine Translation Quality Estimation is a notoriously difficult task, which
lessens its usefulness in real-world translation environments. Such scenarios
can be improved if quality predictions are accompanied by a measure of
uncertainty. However, models in this task are traditionally evaluated only in
terms of point ... | computer science |
15,938 | SnapToGrid: From Statistical to Interpretable Models for Biomedical
Information Extraction | cs.CL | We propose an approach for biomedical information extraction that marries the
advantages of machine learning models, e.g., learning directly from data, with
the benefits of rule-based approaches, e.g., interpretability. Our approach
starts by training a feature-based statistical model, then converts this model
to a rul... | computer science |
15,939 | Representation of texts as complex networks: a mesoscopic approach | cs.CL | Statistical techniques that analyze texts, referred to as text analytics,
have departed from the use of simple word count statistics towards a new
paradigm. Text mining now hinges on a more sophisticated set of methods,
including the representations in terms of complex networks. While
well-established word-adjacency (c... | computer science |
15,940 | HUME: Human UCCA-Based Evaluation of Machine Translation | cs.CL | Human evaluation of machine translation normally uses sentence-level measures
such as relative ranking or adequacy scales. However, these provide no insight
into possible errors, and do not scale well with sentence length. We argue for
a semantics-based evaluation, which captures what meaning components are
retained in... | computer science |
15,941 | A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue
Systems | cs.CL | User simulation is essential for generating enough data to train a
statistical spoken dialogue system. Previous models for user simulation suffer
from several drawbacks, such as the inability to take dialogue history into
account, the need of rigid structure to ensure coherent user behaviour, heavy
dependence on a spec... | computer science |
15,942 | TensiStrength: Stress and relaxation magnitude detection for social
media texts | cs.CL | Computer systems need to be able to react to stress in order to perform
optimally on some tasks. This article describes TensiStrength, a system to
detect the strength of stress and relaxation expressed in social media text
messages. TensiStrength uses a lexical approach and a set of rules to detect
direct and indirect ... | computer science |
15,943 | Sharing Network Parameters for Crosslingual Named Entity Recognition | cs.CL | Most state of the art approaches for Named Entity Recognition rely on hand
crafted features and annotated corpora. Recently Neural network based models
have been proposed which do not require handcrafted features but still require
annotated corpora. However, such annotated corpora may not be available for
many language... | computer science |
15,944 | Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource | cs.CL | Word embeddings have recently seen a strong increase in interest as a result
of strong performance gains on a variety of tasks. However, most of this
research also underlined the importance of benchmark datasets, and the
difficulty of constructing these for a variety of language-specific tasks.
Still, many of the datas... | computer science |
15,945 | Moving Toward High Precision Dynamical Modelling in Hidden Markov Models | cs.CL | Hidden Markov Model (HMM) is often regarded as the dynamical model of choice
in many fields and applications. It is also at the heart of most
state-of-the-art speech recognition systems since the 70's. However, from
Gaussian mixture models HMMs (GMM-HMM) to deep neural network HMMs (DNN-HMM),
the underlying Markovian c... | computer science |
15,946 | Text comparison using word vector representations and dimensionality
reduction | cs.CL | This paper describes a technique to compare large text sources using word
vector representations (word2vec) and dimensionality reduction (t-SNE) and how
it can be implemented using Python. The technique provides a bird's-eye view of
text sources, e.g. text summaries and their source material, and enables users
to explo... | computer science |
15,947 | Context-Dependent Word Representation for Neural Machine Translation | cs.CL | We first observe a potential weakness of continuous vector representations of
symbols in neural machine translation. That is, the continuous vector
representation, or a word embedding vector, of a symbol encodes multiple
dimensions of similarity, equivalent to encoding more than one meaning of the
word. This has the co... | computer science |
15,948 | Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent
Unit for Summarization | cs.CL | In this work, we introduce temporal hierarchies to the sequence to sequence
(seq2seq) model to tackle the problem of abstractive summarization of
scientific articles. The proposed Multiple Timescale model of the Gated
Recurrent Unit (MTGRU) is implemented in the encoder-decoder setting to better
deal with the presence ... | computer science |
15,949 | Learning when to trust distant supervision: An application to
low-resource POS tagging using cross-lingual projection | cs.CL | Cross lingual projection of linguistic annotation suffers from many sources
of bias and noise, leading to unreliable annotations that cannot be used
directly. In this paper, we introduce a novel approach to sequence tagging that
learns to correct the errors from cross-lingual projection using an explicit
debiasing laye... | computer science |
15,950 | Target-Side Context for Discriminative Models in Statistical Machine
Translation | cs.CL | Discriminative translation models utilizing source context have been shown to
help statistical machine translation performance. We propose a novel extension
of this work using target context information. Surprisingly, we show that this
model can be efficiently integrated directly in the decoding process. Our
approach s... | computer science |
15,951 | Chains of Reasoning over Entities, Relations, and Text using Recurrent
Neural Networks | cs.CL | Our goal is to combine the rich multistep inference of symbolic logical
reasoning with the generalization capabilities of neural networks. We are
particularly interested in complex reasoning about entities and relations in
text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs
to compose the dis... | computer science |
15,952 | Global Neural CCG Parsing with Optimality Guarantees | cs.CL | We introduce the first global recursive neural parsing model with optimality
guarantees during decoding. To support global features, we give up dynamic
programs and instead search directly in the space of all possible subtrees.
Although this space is exponentially large in the sentence length, we show it
is possible to... | computer science |
15,953 | Extracting Formal Models from Normative Texts | cs.CL | Normative texts are documents based on the deontic notions of obligation,
permission, and prohibition. Our goal is to model such texts using the C-O
Diagram formalism, making them amenable to formal analysis, in particular
verifying that a text satisfies properties concerning causality of actions and
timing constraints... | computer science |
15,954 | Bag of Tricks for Efficient Text Classification | cs.CL | This paper explores a simple and efficient baseline for text classification.
Our experiments show that our fast text classifier fastText is often on par
with deep learning classifiers in terms of accuracy, and many orders of
magnitude faster for training and evaluation. We can train fastText on more
than one billion wo... | computer science |
15,955 | Neural Name Translation Improves Neural Machine Translation | cs.CL | In order to control computational complexity, neural machine translation
(NMT) systems convert all rare words outside the vocabulary into a single unk
symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered
unks to learn the correspondence between source and target rare words. However,
testing w... | computer science |
15,956 | Charagram: Embedding Words and Sentences via Character n-grams | cs.CL | We present Charagram embeddings, a simple approach for learning
character-based compositional models to embed textual sequences. A word or
sentence is represented using a character n-gram count vector, followed by a
single nonlinear transformation to yield a low-dimensional embedding. We use
three tasks for evaluation:... | computer science |
15,957 | Syntactic Phylogenetic Trees | cs.CL | In this paper we identify several serious problems that arise in the use of
syntactic data from the SSWL database for the purpose of computational
phylogenetic reconstruction. We show that the most naive approach fails to
produce reliable linguistic phylogenetic trees. We identify some of the sources
of the observed pr... | computer science |
15,958 | The Benefits of Word Embeddings Features for Active Learning in Clinical
Information Extraction | cs.CL | This study investigates the use of unsupervised word embeddings and sequence
features for sample representation in an active learning framework built to
extract clinical concepts from clinical free text. The objective is to further
reduce the manual annotation effort while achieving higher effectiveness
compared to a s... | computer science |
15,959 | Open-Vocabulary Semantic Parsing with both Distributional Statistics and
Formal Knowledge | cs.CL | Traditional semantic parsers map language onto compositional, executable
queries in a fixed schema. This mapping allows them to effectively leverage the
information contained in large, formal knowledge bases (KBs, e.g., Freebase) to
answer questions, but it is also fundamentally limiting---these semantic
parsers can on... | computer science |
15,960 | An Empirical Evaluation of various Deep Learning Architectures for
Bi-Sequence Classification Tasks | cs.CL | Several tasks in argumentation mining and debating, question-answering, and
natural language inference involve classifying a sequence in the context of
another sequence (referred as bi-sequence classification). For several single
sequence classification tasks, the current state-of-the-art approaches are
based on recurr... | computer science |
15,961 | Dependency Language Models for Transition-based Dependency Parsing | cs.CL | In this paper, we present an approach to improve the accuracy of a strong
transition-based dependency parser by exploiting dependency language models
that are extracted from a large parsed corpus. We integrated a small number of
features based on the dependency language models into the parser. To
demonstrate the effect... | computer science |
15,962 | Language classification from bilingual word embedding graphs | cs.CL | We study the role of the second language in bilingual word embeddings in
monolingual semantic evaluation tasks. We find strongly and weakly positive
correlations between down-stream task performance and second language
similarity to the target language. Additionally, we show how bilingual word
embeddings can be employe... | computer science |
15,963 | Joint Event Detection and Entity Resolution: a Virtuous Cycle | cs.CL | Clustering web documents has numerous applications, such as aggregating news
articles into meaningful events, detecting trends and hot topics on the Web,
preserving diversity in search results, etc. At the same time, the importance
of named entities and, in particular, the ability to recognize them and to
solve the ass... | computer science |
15,964 | An Empirical Evaluation of doc2vec with Practical Insights into Document
Embedding Generation | cs.CL | Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec
(Mikolov et al., 2013a) to learn document-level embeddings. Despite promising
results in the original paper, others have struggled to reproduce those
results. This paper presents a rigorous empirical evaluation of doc2vec over
two tasks. We co... | computer science |
15,965 | Discriminating between similar languages in Twitter using label
propagation | cs.CL | Identifying the language of social media messages is an important first step
in linguistic processing. Existing models for Twitter focus on content
analysis, which is successful for dissimilar language pairs. We propose a label
propagation approach that takes the social graph of tweet authors into account
as well as co... | computer science |
15,966 | A New Bengali Readability Score | cs.CL | In this paper we have proposed methods to analyze the readability of Bengali
language texts. We have got some exceptionally good results out of the
experiments. | computer science |
15,967 | An Adaptation of Topic Modeling to Sentences | cs.CL | Advances in topic modeling have yielded effective methods for characterizing
the latent semantics of textual data. However, applying standard topic modeling
approaches to sentence-level tasks introduces a number of challenges. In this
paper, we adapt the approach of latent-Dirichlet allocation to include an
additional ... | computer science |
15,968 | Incremental Learning for Fully Unsupervised Word Segmentation Using
Penalized Likelihood and Model Selection | cs.CL | We present a novel incremental learning approach for unsupervised word
segmentation that combines features from probabilistic modeling and model
selection. This includes super-additive penalties for addressing the cognitive
burden imposed by long word formation, and new model selection criteria based
on higher-order ge... | computer science |
15,969 | Exploring phrase-compositionality in skip-gram models | cs.CL | In this paper, we introduce a variation of the skip-gram model which jointly
learns distributed word vector representations and their way of composing to
form phrase embeddings. In particular, we propose a learning procedure that
incorporates a phrase-compositionality function which can capture how we want
to compose p... | computer science |
15,970 | A Perspective on Sentiment Analysis | cs.CL | Sentiment Analysis (SA) is indeed a fascinating area of research which has
stolen the attention of researchers as it has many facets and more importantly
it promises economic stakes in the corporate and governance sector. SA has been
stemmed out of text analytics and established itself as a separate identity and
a doma... | computer science |
15,971 | Opinion Mining in Online Reviews About Distance Education Programs | cs.CL | The popularity of distance education programs is increasing at a fast pace.
En par with this development, online communication in fora, social media and
reviewing platforms between students is increasing as well. Exploiting this
information to support fellow students or institutions requires to extract the
relevant opi... | computer science |
15,972 | La representación de la variación contextual mediante definiciones
terminológicas flexibles | cs.CL | In this doctoral thesis, we apply premises of cognitive linguistics to
terminological definitions and present a proposal called the flexible
terminological definition. This consists of a set of definitions of the same
concept made up of a general definition (in this case, one encompassing the
entire environmental domai... | computer science |
15,973 | Syntax-based Attention Model for Natural Language Inference | cs.CL | Introducing attentional mechanism in neural network is a powerful concept,
and has achieved impressive results in many natural language processing tasks.
However, most of the existing models impose attentional distribution on a flat
topology, namely the entire input representation sequence. Clearly, any
well-formed sen... | computer science |
15,974 | CFGs-2-NLU: Sequence-to-Sequence Learning for Mapping Utterances to
Semantics and Pragmatics | cs.CL | In this paper, we present a novel approach to natural language understanding
that utilizes context-free grammars (CFGs) in conjunction with
sequence-to-sequence (seq2seq) deep learning. Specifically, we take a CFG
authored to generate dialogue for our target application for NLU, a videogame,
and train a long short-term... | computer science |
15,975 | Neural Sentence Ordering | cs.CL | Sentence ordering is a general and critical task for natural language
generation applications. Previous works have focused on improving its
performance in an external, downstream task, such as multi-document
summarization. Given its importance, we propose to study it as an isolated
task. We collect a large corpus of ac... | computer science |
15,976 | Authorship attribution via network motifs identification | cs.CL | Concepts and methods of complex networks can be used to analyse texts at
their different complexity levels. Examples of natural language processing
(NLP) tasks studied via topological analysis of networks are keyword
identification, automatic extractive summarization and authorship attribution.
Even though a myriad of ... | computer science |
15,977 | Latent Tree Language Model | cs.CL | In this paper we introduce Latent Tree Language Model (LTLM), a novel
approach to language modeling that encodes syntax and semantics of a given
sentence as a tree of word roles.
The learning phase iteratively updates the trees by moving nodes according to
Gibbs sampling. We introduce two algorithms to infer a tree f... | computer science |
15,978 | Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction | cs.CL | This paper presents a computational model of the processing of dynamic
spatial relations occurring in an embodied robotic interaction setup. A
complete system is introduced that allows autonomous robots to produce and
interpret dynamic spatial phrases (in English) given an environment of moving
objects. The model unite... | computer science |
15,979 | Grounded Lexicon Acquisition - Case Studies in Spatial Language | cs.CL | This paper discusses grounded acquisition experiments of increasing
complexity. Humanoid robots acquire English spatial lexicons from robot tutors.
We identify how various spatial language systems, such as projective, absolute
and proximal can be learned. The proposed learning mechanisms do not rely on
direct meaning t... | computer science |
15,980 | Machine Learned Resume-Job Matching Solution | cs.CL | Job search through online matching engines nowadays are very prominent and
beneficial to both job seekers and employers. But the solutions of traditional
engines without understanding the semantic meanings of different resumes have
not kept pace with the incredible changes in machine learning techniques and
computing c... | computer science |
15,981 | Synthetic Language Generation and Model Validation in BEAST2 | cs.CL | Generating synthetic languages aids in the testing and validation of future
computational linguistic models and methods. This thesis extends the BEAST2
phylogenetic framework to add linguistic sequence generation under multiple
models. The new plugin is then used to test the effects of the phenomena of
word borrowing o... | computer science |
15,982 | A Novel Bilingual Word Embedding Method for Lexical Translation Using
Bilingual Sense Clique | cs.CL | Most of the existing methods for bilingual word embedding only consider
shallow context or simple co-occurrence information. In this paper, we propose
a latent bilingual sense unit (Bilingual Sense Clique, BSC), which is derived
from a maximum complete sub-graph of pointwise mutual information based graph
over bilingua... | computer science |
15,983 | Connecting Phrase based Statistical Machine Translation Adaptation | cs.CL | Although more additional corpora are now available for Statistical Machine
Translation (SMT), only the ones which belong to the same or similar domains
with the original corpus can indeed enhance SMT performance directly. Most of
the existing adaptation methods focus on sentence selection. In comparison,
phrase is a sm... | computer science |
15,984 | Recurrent Neural Machine Translation | cs.CL | The vanilla attention-based neural machine translation has achieved promising
performance because of its capability in leveraging varying-length source
annotations. However, this model still suffers from failures in long sentence
translation, for its incapability in capturing long-term dependencies. In this
paper, we p... | computer science |
15,985 | Authorship Verification - An Approach based on Random Forest | cs.CL | Authorship attribution, being an important problem in many areas in-cluding
information retrieval, computational linguistics, law and journalism etc., has
been identified as a subject of increasingly research interest in the re-cent
years. In case of Author Identification task in PAN at CLEF 2015, the main
focus was gi... | computer science |
15,986 | Supervised Attentions for Neural Machine Translation | cs.CL | In this paper, we improve the attention or alignment accuracy of neural
machine translation by utilizing the alignments of training sentence pairs. We
simply compute the distance between the machine attentions and the "true"
alignments, and minimize this cost in the training procedure. Our experiments
on large-scale Ch... | computer science |
15,987 | Left-corner Methods for Syntactic Modeling with Universal Structural
Constraints | cs.CL | The primary goal in this thesis is to identify better syntactic constraint or
bias, that is language independent but also efficiently exploitable during
sentence processing. We focus on a particular syntactic construction called
center-embedding, which is well studied in psycholinguistics and noted to cause
particular ... | computer science |
15,988 | Crowd-sourcing NLG Data: Pictures Elicit Better Data | cs.CL | Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high qualit... | computer science |
15,989 | Blind phoneme segmentation with temporal prediction errors | cs.CL | Phonemic segmentation of speech is a critical step of speech recognition
systems. We propose a novel unsupervised algorithm based on sequence prediction
models such as Markov chains and recurrent neural network. Our approach
consists in analyzing the error profile of a model trained to predict speech
features frame-by-... | computer science |
15,990 | Structured prediction models for RNN based sequence labeling in clinical
text | cs.CL | Sequence labeling is a widely used method for named entity recognition and
information extraction from unstructured natural language data. In clinical
domain one major application of sequence labeling involves extraction of
medical entities such as medication, indication, and side-effects from
Electronic Health Record ... | computer science |
15,991 | New word analogy corpus for exploring embeddings of Czech words | cs.CL | The word embedding methods have been proven to be very useful in many tasks
of NLP (Natural Language Processing). Much has been investigated about word
embeddings of English words and phrases, but only little attention has been
dedicated to other languages.
Our goal in this paper is to explore the behavior of state-o... | computer science |
15,992 | Semantic Representations of Word Senses and Concepts | cs.CL | Representing the semantics of linguistic items in a machine-interpretable
form has been a major goal of Natural Language Processing since its earliest
days. Among the range of different linguistic items, words have attracted the
most research attention. However, word representations have an important
limitation: they c... | computer science |
15,993 | SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity | cs.CL | Verbs play a critical role in the meaning of sentences, but these ubiquitous
words have received little attention in recent distributional semantics
research. We introduce SimVerb-3500, an evaluation resource that provides human
ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed
verb types f... | computer science |
15,994 | Knowledge Distillation for Small-footprint Highway Networks | cs.CL | Deep learning has significantly advanced state-of-the-art of speech
recognition in the past few years. However, compared to conventional Gaussian
mixture acoustic models, neural network models are usually much larger, and are
therefore not very deployable in embedded devices. Previously, we investigated
a compact highw... | computer science |
15,995 | Efficient Segmental Cascades for Speech Recognition | cs.CL | Discriminative segmental models offer a way to incorporate flexible feature
functions into speech recognition. However, their appeal has been limited by
their computational requirements, due to the large number of possible segments
to consider. Multi-pass cascades of segmental models introduce features of
increasing co... | computer science |
15,996 | Morphological Priors for Probabilistic Neural Word Embeddings | cs.CL | Word embeddings allow natural language processing systems to share
statistical information across related words. These embeddings are typically
based on distributional statistics, making it difficult for them to generalize
to rare or unseen words. We propose to improve word embeddings by incorporating
morphological inf... | computer science |
15,997 | To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering
in Statistical Machine Translation | cs.CL | Reordering poses a major challenge in machine translation (MT) between two
languages with significant differences in word order. In this paper, we present
a novel reordering approach utilizing sparse features based on dependency word
pairs. Each instance of these features captures whether two words, which are
related b... | computer science |
15,998 | Words, Concepts, and the Geometry of Analogy | cs.CL | This paper presents a geometric approach to the problem of modelling the
relationship between words and concepts, focusing in particular on analogical
phenomena in language and cognition. Grounded in recent theories regarding
geometric conceptual spaces, we begin with an analysis of existing static
distributional seman... | computer science |
15,999 | Entailment Relations on Distributions | cs.CL | In this paper we give an overview of partial orders on the space of
probability distributions that carry a notion of information content and serve
as a generalisation of the Bayesian order given in (Coecke and Martin, 2011).
We investigate what constraints are necessary in order to get a unique notion
of information co... | computer science |
16,000 | Solving General Arithmetic Word Problems | cs.CL | This paper presents a novel approach to automatically solving arithmetic word
problems. This is the first algorithmic approach that can handle arithmetic
problems with multiple steps and operations, without depending on additional
annotations or predefined templates. We develop a theory for expression trees
that can be... | computer science |
16,001 | Word Segmentation on Micro-blog Texts with External Lexicon and
Heterogeneous Data | cs.CL | This paper describes our system designed for the NLPCC 2016 shared task on
word segmentation on micro-blog texts. | computer science |
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