Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
15,602 | Edge-Linear First-Order Dependency Parsing with Undirected Minimum
Spanning Tree Inference | cs.CL | The run time complexity of state-of-the-art inference algorithms in
graph-based dependency parsing is super-linear in the number of input words
(n). Recently, pruning algorithms for these models have shown to cut a large
portion of the graph edges, with minimal damage to the resulting parse trees.
Solving the inference... | computer science |
15,603 | Parser for Abstract Meaning Representation using Learning to Search | cs.CL | We develop a novel technique to parse English sentences into Abstract Meaning
Representation (AMR) using SEARN, a Learning to Search approach, by modeling
the concept and the relation learning in a unified framework. We evaluate our
parser on multiple datasets from varied domains and show an absolute
improvement of 2% ... | computer science |
15,604 | Standards for language resources in ISO -- Looking back at 13 fruitful
years | cs.CL | This paper provides an overview of the various projects carried out within
ISO committee TC 37/SC 4 dealing with the management of language (digital)
resources. On the basis of the technical experience gained in the committee and
the wider standardization landscape the paper identifies some possible trends
for the futu... | computer science |
15,605 | Fast k-best Sentence Compression | cs.CL | A popular approach to sentence compression is to formulate the task as a
constrained optimization problem and solve it with integer linear programming
(ILP) tools. Unfortunately, dependence on ILP may make the compressor
prohibitively slow, and thus approximation techniques have been proposed which
are often complex an... | computer science |
15,606 | Emoticons vs. Emojis on Twitter: A Causal Inference Approach | cs.CL | Online writing lacks the non-verbal cues present in face-to-face
communication, which provide additional contextual information about the
utterance, such as the speaker's intention or affective state. To fill this
void, a number of orthographic features, such as emoticons, expressive
lengthening, and non-standard punct... | computer science |
15,607 | SentiWords: Deriving a High Precision and High Coverage Lexicon for
Sentiment Analysis | cs.CL | Deriving prior polarity lexica for sentiment analysis - where positive or
negative scores are associated with words out of context - is a challenging
task. Usually, a trade-off between precision and coverage is hard to find, and
it depends on the methodology used to build the lexicon. Manually annotated
lexica provide ... | computer science |
15,608 | A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network
with Word Embedding | cs.CL | Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for modeling and predicting sequential data,
e.g. speech utterances or handwritten documents. In this study, we propose to
use BLSTM-RNN for a unified tagging solution that can be applied to various
tagging tas... | computer science |
15,609 | Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix
Language Model | cs.CL | We describe Sparse Non-negative Matrix (SNM) language model estimation using
multinomial loss on held-out data.
Being able to train on held-out data is important in practical situations
where the training data is usually mismatched from the held-out/test data. It
is also less constrained than the previous training al... | computer science |
15,610 | An Empirical Study on Sentiment Classification of Chinese Review using
Word Embedding | cs.CL | In this article, how word embeddings can be used as features in Chinese
sentiment classification is presented. Firstly, a Chinese opinion corpus is
built with a million comments from hotel review websites. Then the word
embeddings which represent each comment are used as input in different machine
learning methods for ... | computer science |
15,611 | Comparing Writing Styles using Word Embedding and Dynamic Time Warping | cs.CL | The development of plot or story in novels is reflected in the content and
the words used. The flow of sentiments, which is one aspect of writing style,
can be quantified by analyzing the flow of words. This study explores literary
works as signals in word embedding space and tries to compare writing styles of
popular ... | computer science |
15,612 | "Pale as death" or "pâle comme la mort" : Frozen similes used as
literary clichés | cs.CL | The present study is focused on the automatic identification and description
of frozen similes in British and French novels written between the 19 th
century and the beginning of the 20 th century. Two main patterns of frozen
similes were considered: adjectival ground + simile marker + nominal vehicle
(e.g. happy as a ... | computer science |
15,613 | Population size predicts lexical diversity, but so does the mean sea
level - why it is important to correctly account for the structure of
temporal data | cs.CL | In order to demonstrate why it is important to correctly account for the
(serial dependent) structure of temporal data, we document an apparently
spectacular relationship between population size and lexical diversity: for
five out of seven investigated languages, there is a strong relationship
between population size a... | computer science |
15,614 | Introducing SKYSET - a Quintuple Approach for Improving Instructions | cs.CL | A new approach called SKYSET (Synthetic Knowledge Yield Social Entities
Translation) is proposed to validate completeness and to reduce ambiguity from
written instructional documentation. SKYSET utilizes a quintuple set of
standardized categories, which differs from traditional approaches that
typically use triples. Th... | computer science |
15,615 | The Goldilocks Principle: Reading Children's Books with Explicit Memory
Representations | cs.CL | We introduce a new test of how well language models capture meaning in
children's books. Unlike standard language modelling benchmarks, it
distinguishes the task of predicting syntactic function words from that of
predicting lower-frequency words, which carry greater semantic content. We
compare a range of state-of-the... | computer science |
15,616 | A Chinese POS Decision Method Using Korean Translation Information | cs.CL | In this paper we propose a method that imitates a translation expert using
the Korean translation information and analyse the performance. Korean is good
at tagging than Chinese, so we can use this property in Chinese POS tagging. | computer science |
15,617 | Investigating the stylistic relevance of adjective and verb simile
markers | cs.CL | Similes play an important role in literary texts not only as rhetorical
devices and as figures of speech but also because of their evocative power,
their aptness for description and the relative ease with which they can be
combined with other figures of speech (Israel et al. 2004). Detecting all types
of simile constru... | computer science |
15,618 | USFD: Twitter NER with Drift Compensation and Linked Data | cs.CL | This paper describes a pilot NER system for Twitter, comprising the USFD
system entry to the W-NUT 2015 NER shared task. The goal is to correctly label
entities in a tweet dataset, using an inventory of ten types. We employ
structured learning, drawing on gazetteers taken from Linked Data, and on
unsupervised clusterin... | computer science |
15,619 | Larger-Context Language Modelling | cs.CL | In this work, we propose a novel method to incorporate corpus-level discourse
information into language modelling. We call this larger-context language
model. We introduce a late fusion approach to a recurrent language model based
on long short-term memory units (LSTM), which helps the LSTM unit keep
intra-sentence dep... | computer science |
15,620 | A Multilingual FrameNet-based Grammar and Lexicon for Controlled Natural
Language | cs.CL | Berkeley FrameNet is a lexico-semantic resource for English based on the
theory of frame semantics. It has been exploited in a range of natural language
processing applications and has inspired the development of framenets for many
languages. We present a methodological approach to the extraction and
generation of a co... | computer science |
15,621 | Character-based Neural Machine Translation | cs.CL | We introduce a neural machine translation model that views the input and
output sentences as sequences of characters rather than words. Since word-level
information provides a crucial source of bias, our input model composes
representations of character sequences into representations of words (as
determined by whitespa... | computer science |
15,622 | Learning to Represent Words in Context with Multilingual Supervision | cs.CL | We present a neural network architecture based on bidirectional LSTMs to
compute representations of words in the sentential contexts. These
context-sensitive word representations are suitable for, e.g., distinguishing
different word senses and other context-modulated variations in meaning. To
learn the parameters of ou... | computer science |
15,623 | A System for Extracting Sentiment from Large-Scale Arabic Social Data | cs.CL | Social media data in Arabic language is becoming more and more abundant. It
is a consensus that valuable information lies in social media data. Mining this
data and making the process easier are gaining momentum in the industries. This
paper describes an enterprise system we developed for extracting sentiment from
larg... | computer science |
15,624 | Latent Dirichlet Allocation Based Organisation of Broadcast Media
Archives for Deep Neural Network Adaptation | cs.CL | This paper presents a new method for the discovery of latent domains in
diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs)
for Automatic Speech Recognition. Our work focuses on transcription of
multi-genre broadcast media, which is often only categorised broadly in terms
of high level genres ... | computer science |
15,625 | Learning to retrieve out-of-vocabulary words in speech recognition | cs.CL | Many Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech
recognition systems used to process diachronic audio data. To help recovery of
the PNs missed by the system, relevant OOV PNs can be retrieved out of the many
OOVs by exploiting semantic context of the spoken content. In this paper, we
propose two neu... | computer science |
15,626 | Gaussian Mixture Embeddings for Multiple Word Prototypes | cs.CL | Recently, word representation has been increasingly focused on for its
excellent properties in representing the word semantics. Previous works mainly
suffer from the problem of polysemy phenomenon. To address this problem, most
of previous models represent words as multiple distributed vectors. However, it
cannot refle... | computer science |
15,627 | Good, Better, Best: Choosing Word Embedding Context | cs.CL | We propose two methods of learning vector representations of words and
phrases that each combine sentence context with structural features extracted
from dependency trees. Using several variations of neural network classifier,
we show that these combined methods lead to improved performance when used as
input features ... | computer science |
15,628 | Reasoning in Vector Space: An Exploratory Study of Question Answering | cs.CL | Question answering tasks have shown remarkable progress with distributed
vector representation. In this paper, we investigate the recently proposed
Facebook bAbI tasks which consist of twenty different categories of questions
that require complex reasoning. Because the previous work on bAbI are all
end-to-end models, e... | computer science |
15,629 | Polysemy in Controlled Natural Language Texts | cs.CL | Computational semantics and logic-based controlled natural languages (CNL) do
not address systematically the word sense disambiguation problem of content
words, i.e., they tend to interpret only some functional words that are crucial
for construction of discourse representation structures. We show that
micro-ontologies... | computer science |
15,630 | Improving Neural Machine Translation Models with Monolingual Data | cs.CL | Neural Machine Translation (NMT) has obtained state-of-the art performance
for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for
phrase-based statistical machine translation, and we investigate the use of
monolingual data fo... | computer science |
15,631 | Spoken Language Translation for Polish | cs.CL | Spoken language translation (SLT) is becoming more important in the
increasingly globalized world, both from a social and economic point of view.
It is one of the major challenges for automatic speech recognition (ASR) and
machine translation (MT), driving intense research activities in these areas.
While past research... | computer science |
15,632 | OntoSeg: a Novel Approach to Text Segmentation using Ontological
Similarity | cs.CL | Text segmentation (TS) aims at dividing long text into coherent segments
which reflect the subtopic structure of the text. It is beneficial to many
natural language processing tasks, such as Information Retrieval (IR) and
document summarisation. Current approaches to text segmentation are similar in
that they all use w... | computer science |
15,633 | Category Enhanced Word Embedding | cs.CL | Distributed word representations have been demonstrated to be effective in
capturing semantic and syntactic regularities. Unsupervised representation
learning from large unlabeled corpora can learn similar representations for
those words that present similar co-occurrence statistics. Besides local
occurrence statistics... | computer science |
15,634 | A C-LSTM Neural Network for Text Classification | cs.CL | Neural network models have been demonstrated to be capable of achieving
remarkable performance in sentence and document modeling. Convolutional neural
network (CNN) and recurrent neural network (RNN) are two mainstream
architectures for such modeling tasks, which adopt totally different ways of
understanding natural la... | computer science |
15,635 | Multilingual Language Processing From Bytes | cs.CL | We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads
text as bytes and outputs span annotations of the form [start, length, label]
where start positions, lengths, and labels are separate entries in our
vocabulary. Because we operate directly on unicode bytes rather than
language-specific words or... | computer science |
15,636 | Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT | cs.CL | Pivot language is employed as a way to solve the data sparseness problem in
machine translation, especially when the data for a particular language pair
does not exist. The combination of source-to-pivot and pivot-to-target
translation models can induce a new translation model through the pivot
language. However, the e... | computer science |
15,637 | Benchmarking sentiment analysis methods for large-scale texts: A case
for using continuum-scored words and word shift graphs | cs.CL | The emergence and global adoption of social media has rendered possible the
real-time estimation of population-scale sentiment, bearing profound
implications for our understanding of human behavior. Given the growing
assortment of sentiment measuring instruments, comparisons between them are
evidently required. Here, w... | computer science |
15,638 | Annotating Character Relationships in Literary Texts | cs.CL | We present a dataset of manually annotated relationships between characters
in literary texts, in order to support the training and evaluation of automatic
methods for relation type prediction in this domain (Makazhanov et al., 2014;
Kokkinakis, 2013) and the broader computational analysis of literary character
(Elson ... | computer science |
15,639 | Effective LSTMs for Target-Dependent Sentiment Classification | cs.CL | Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connect... | computer science |
15,640 | Neural Generative Question Answering | cs.CL | This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equip... | computer science |
15,641 | What Makes it Difficult to Understand a Scientific Literature? | cs.CL | In the artificial intelligence area, one of the ultimate goals is to make
computers understand human language and offer assistance. In order to achieve
this ideal, researchers of computer science have put forward a lot of models
and algorithms attempting at enabling the machine to analyze and process human
natural lang... | computer science |
15,642 | Want Answers? A Reddit Inspired Study on How to Pose Questions | cs.CL | Questions form an integral part of our everyday communication, both offline
and online. Getting responses to our questions from others is fundamental to
satisfying our information need and in extending our knowledge boundaries. A
question may be represented using various factors such as social, syntactic,
semantic, etc... | computer science |
15,643 | Minimum Risk Training for Neural Machine Translation | cs.CL | We propose minimum risk training for end-to-end neural machine translation.
Unlike conventional maximum likelihood estimation, minimum risk training is
capable of optimizing model parameters directly with respect to arbitrary
evaluation metrics, which are not necessarily differentiable. Experiments show
that our approa... | computer science |
15,644 | Deep Speech 2: End-to-End Speech Recognition in English and Mandarin | cs.CL | We show that an end-to-end deep learning approach can be used to recognize
either English or Mandarin Chinese speech--two vastly different languages.
Because it replaces entire pipelines of hand-engineered components with neural
networks, end-to-end learning allows us to handle a diverse variety of speech
including noi... | computer science |
15,645 | Mined Semantic Analysis: A New Concept Space Model for Semantic
Representation of Textual Data | cs.CL | Mined Semantic Analysis (MSA) is a novel concept space model which employs
unsupervised learning to generate semantic representations of text. MSA
represents textual structures (terms, phrases, documents) as a Bag of Concepts
(BoC) where concepts are derived from concept rich encyclopedic corpora.
Traditional concept s... | computer science |
15,646 | A Hidden Markov Model Based System for Entity Extraction from Social
Media English Text at FIRE 2015 | cs.CL | This paper presents the experiments carried out by us at Jadavpur University
as part of the participation in FIRE 2015 task: Entity Extraction from Social
Media Text - Indian Languages (ESM-IL). The tool that we have developed for the
task is based on Trigram Hidden Markov Model that utilizes information like
gazetteer... | computer science |
15,647 | Agreement-based Joint Training for Bidirectional Attention-based Neural
Machine Translation | cs.CL | The attentional mechanism has proven to be effective in improving end-to-end
neural machine translation. However, due to the intricate structural divergence
between natural languages, unidirectional attention-based models might only
capture partial aspects of attentional regularities. We propose agreement-based
joint t... | computer science |
15,648 | Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised
Learning | cs.CL | Morpho-syntactic lexicons provide information about the morphological and
syntactic roles of words in a language. Such lexicons are not available for all
languages and even when available, their coverage can be limited. We present a
graph-based semi-supervised learning method that uses the morphological,
syntactic and ... | computer science |
15,649 | ABCNN: Attention-Based Convolutional Neural Network for Modeling
Sentence Pairs | cs.CL | How to model a pair of sentences is a critical issue in many NLP tasks such
as answer selection (AS), paraphrase identification (PI) and textual entailment
(TE). Most prior work (i) deals with one individual task by fine-tuning a
specific system; (ii) models each sentence's representation separately, rarely
considering... | computer science |
15,650 | Towards automating the generation of derivative nouns in Sanskrit by
simulating Panini | cs.CL | About 1115 rules in Astadhyayi from A.4.1.76 to A.5.4.160 deal with
generation of derivative nouns, making it one of the largest topical sections
in Astadhyayi, called as the Taddhita section owing to the head rule A.4.1.76.
This section is a systematic arrangement of rules that enumerates various
affixes that are used... | computer science |
15,651 | A Planning based Framework for Essay Generation | cs.CL | Generating an article automatically with computer program is a challenging
task in artificial intelligence and natural language processing. In this paper,
we target at essay generation, which takes as input a topic word in mind and
generates an organized article under the theme of the topic. We follow the idea
of text ... | computer science |
15,652 | Morphological Inflection Generation Using Character Sequence to Sequence
Learning | cs.CL | Morphological inflection generation is the task of generating the inflected
form of a given lemma corresponding to a particular linguistic transformation.
We model the problem of inflection generation as a character sequence to
sequence learning problem and present a variant of the neural encoder-decoder
model for solv... | computer science |
15,653 | The 2015 Sheffield System for Transcription of Multi-Genre Broadcast
Media | cs.CL | We describe the University of Sheffield system for participation in the 2015
Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre
broadcast shows. Transcription was one of four tasks proposed in the MGB
challenge, with the aim of advancing the state of the art of automatic speech
recognition, speaker ... | computer science |
15,654 | The Improvement of Negative Sentences Translation in English-to-Korean
Machine Translation | cs.CL | This paper describes the algorithm for translating English negative sentences
into Korean in English-Korean Machine Translation (EKMT). The proposed
algorithm is based on the comparative study of English and Korean negative
sentences. The earlier translation software cannot translate English negative
sentences into acc... | computer science |
15,655 | Learning Document Embeddings by Predicting N-grams for Sentiment
Classification of Long Movie Reviews | cs.CL | Despite the loss of semantic information, bag-of-ngram based methods still
achieve state-of-the-art results for tasks such as sentiment classification of
long movie reviews. Many document embeddings methods have been proposed to
capture semantics, but they still can't outperform bag-of-ngram based methods
on this task.... | computer science |
15,656 | Sentiment/Subjectivity Analysis Survey for Languages other than English | cs.CL | Subjective and sentiment analysis have gained considerable attention
recently. Most of the resources and systems built so far are done for English.
The need for designing systems for other languages is increasing. This paper
surveys different ways used for building systems for subjective and sentiment
analysis for lang... | computer science |
15,657 | Contrastive Entropy: A new evaluation metric for unnormalized language
models | cs.CL | Perplexity (per word) is the most widely used metric for evaluating language
models. Despite this, there has been no dearth of criticism for this metric.
Most of these criticisms center around lack of correlation with extrinsic
metrics like word error rate (WER), dependence upon shared vocabulary for model
comparison a... | computer science |
15,658 | Distant IE by Bootstrapping Using Lists and Document Structure | cs.CL | Distant labeling for information extraction (IE) suffers from noisy training
data. We describe a way of reducing the noise associated with distant IE by
identifying coupling constraints between potential instance labels. As one
example of coupling, items in a list are likely to have the same label. A
second example of ... | computer science |
15,659 | Multi-Source Neural Translation | cs.CL | We build a multi-source machine translation model and train it to maximize
the probability of a target English string given French and German sources.
Using the neural encoder-decoder framework, we explore several combination
methods and report up to +4.8 Bleu increases on top of a very strong
attention-based neural tr... | computer science |
15,660 | The Role of Context Types and Dimensionality in Learning Word Embeddings | cs.CL | We provide the first extensive evaluation of how using different types of
context to learn skip-gram word embeddings affects performance on a wide range
of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic
tasks tend to exhibit a clear preference to particular types of contexts and
higher dime... | computer science |
15,661 | Incorporating Structural Alignment Biases into an Attentional Neural
Translation Model | cs.CL | Neural encoder-decoder models of machine translation have achieved impressive
results, rivalling traditional translation models. However their modelling
formulation is overly simplistic, and omits several key inductive biases built
into traditional models. In this paper we extend the attentional neural
translation mode... | computer science |
15,662 | Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON
2015 | cs.CL | This paper discusses the experiments carried out by us at Jadavpur University
as part of the participation in ICON 2015 task: POS Tagging for Code-mixed
Indian Social Media Text. The tool that we have developed for the task is based
on Trigram Hidden Markov Model that utilizes information from dictionary as
well as som... | computer science |
15,663 | Recurrent Memory Networks for Language Modeling | cs.CL | Recurrent Neural Networks (RNN) have obtained excellent result in many
natural language processing (NLP) tasks. However, understanding and
interpreting the source of this success remains a challenge. In this paper, we
propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only
amplifies the power of... | computer science |
15,664 | Language to Logical Form with Neural Attention | cs.CL | Semantic parsing aims at mapping natural language to machine interpretable
meaning representations. Traditional approaches rely on high-quality lexicons,
manually-built templates, and linguistic features which are either domain- or
representation-specific. In this paper we present a general method based on an
attention... | computer science |
15,665 | Joint Learning of the Embedding of Words and Entities for Named Entity
Disambiguation | cs.CL | Named Entity Disambiguation (NED) refers to the task of resolving multiple
named entity mentions in a document to their correct references in a knowledge
base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method
specifically designed for NED. The proposed method jointly maps words and
entities int... | computer science |
15,666 | Leveraging Sentence-level Information with Encoder LSTM for Semantic
Slot Filling | cs.CL | Recurrent Neural Network (RNN) and one of its specific architectures, Long
Short-Term Memory (LSTM), have been widely used for sequence labeling. In this
paper, we first enhance LSTM-based sequence labeling to explicitly model label
dependencies. Then we propose another enhancement to incorporate the global
information... | computer science |
15,667 | Empirical Gaussian priors for cross-lingual transfer learning | cs.CL | Sequence model learning algorithms typically maximize log-likelihood minus
the norm of the model (or minimize Hamming loss + norm). In cross-lingual
part-of-speech (POS) tagging, our target language training data consists of
sequences of sentences with word-by-word labels projected from translations in
$k$ languages fo... | computer science |
15,668 | Argumentation Mining in User-Generated Web Discourse | cs.CL | The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, ... | computer science |
15,669 | The Effects of Age, Gender and Region on Non-standard Linguistic
Variation in Online Social Networks | cs.CL | We present a corpus-based analysis of the effects of age, gender and region
of origin on the production of both "netspeak" or "chatspeak" features and
regional speech features in Flemish Dutch posts that were collected from a
Belgian online social network platform. The present study shows that combining
quantitative an... | computer science |
15,670 | Trans-gram, Fast Cross-lingual Word-embeddings | cs.CL | We introduce Trans-gram, a simple and computationally-efficient method to
simultaneously learn and align wordembeddings for a variety of languages, using
only monolingual data and a smaller set of sentence-aligned data. We use our
new method to compute aligned wordembeddings for twenty-one languages using
English as a ... | computer science |
15,671 | Environmental Noise Embeddings for Robust Speech Recognition | cs.CL | We propose a novel deep neural network architecture for speech recognition
that explicitly employs knowledge of the background environmental noise within
a deep neural network acoustic model. A deep neural network is used to predict
the acoustic environment in which the system in being used. The discriminative
embeddin... | computer science |
15,672 | Predicting the Effectiveness of Self-Training: Application to Sentiment
Classification | cs.CL | The goal of this paper is to investigate the connection between the
performance gain that can be obtained by selftraining and the similarity
between the corpora used in this approach. Self-training is a semi-supervised
technique designed to increase the performance of machine learning algorithms
by automatically classi... | computer science |
15,673 | Political Speech Generation | cs.CL | In this report we present a system that can generate political speeches for a
desired political party. Furthermore, the system allows to specify whether a
speech should hold a supportive or opposing opinion. The system relies on a
combination of several state-of-the-art NLP methods which are discussed in this
report. T... | computer science |
15,674 | Implicit Distortion and Fertility Models for Attention-based
Encoder-Decoder NMT Model | cs.CL | Neural machine translation has shown very promising results lately. Most NMT
models follow the encoder-decoder framework. To make encoder-decoder models
more flexible, attention mechanism was introduced to machine translation and
also other tasks like speech recognition and image captioning. We observe that
the quality... | computer science |
15,675 | EvoGrader: an online formative assessment tool for automatically
evaluating written evolutionary explanations | cs.CL | EvoGrader is a free, online, on-demand formative assessment service designed
for use in undergraduate biology classrooms. EvoGrader's web portal is powered
by Amazon's Elastic Cloud and run with LightSIDE Lab's open-source
machine-learning tools. The EvoGrader web portal allows biology instructors to
upload a response ... | computer science |
15,676 | Smoothing parameter estimation framework for IBM word alignment models | cs.CL | IBM models are very important word alignment models in Machine Translation.
Following the Maximum Likelihood Estimation principle to estimate their
parameters, the models will easily overfit the training data when the data are
sparse. While smoothing is a very popular solution in Language Model, there
still lacks studi... | computer science |
15,677 | Towards Turkish ASR: Anatomy of a rule-based Turkish g2p | cs.CL | This paper describes the architecture and implementation of a rule-based
grapheme to phoneme converter for Turkish. The system accepts surface form as
input, outputs SAMPA mapping of the all parallel pronounciations according to
the morphological analysis together with stress positions. The system has been
implemented ... | computer science |
15,678 | Multimodal Pivots for Image Caption Translation | cs.CL | We present an approach to improve statistical machine translation of image
descriptions by multimodal pivots defined in visual space. The key idea is to
perform image retrieval over a database of images that are captioned in the
target language, and use the captions of the most similar images for
crosslingual reranking... | computer science |
15,679 | Detecting and Extracting Events from Text Documents | cs.CL | Events of various kinds are mentioned and discussed in text documents,
whether they are books, news articles, blogs or microblog feeds. The paper
starts by giving an overview of how events are treated in linguistics and
philosophy. We follow this discussion by surveying how events and associated
information are handled... | computer science |
15,680 | Modeling Coverage for Neural Machine Translation | cs.CL | Attention mechanism has enhanced state-of-the-art Neural Machine Translation
(NMT) by jointly learning to align and translate. It tends to ignore past
alignment information, however, which often leads to over-translation and
under-translation. To address this problem, we propose coverage-based NMT in
this paper. We mai... | computer science |
15,681 | Hierarchical Latent Word Clustering | cs.CL | This paper presents a new Bayesian non-parametric model by extending the
usage of Hierarchical Dirichlet Allocation to extract tree structured word
clusters from text data. The inference algorithm of the model collects words in
a cluster if they share similar distribution over documents. In our
experiments, we observed... | computer science |
15,682 | On Structured Sparsity of Phonological Posteriors for Linguistic Parsing | cs.CL | The speech signal conveys information on different time scales from short
time scale or segmental, associated to phonological and phonetic information to
long time scale or supra segmental, associated to syllabic and prosodic
information. Linguistic and neurocognitive studies recognize the phonological
classes at segme... | computer science |
15,683 | Syntax-Semantics Interaction Parsing Strategies. Inside SYNTAGMA | cs.CL | This paper discusses SYNTAGMA, a rule based NLP system addressing the tricky
issues of syntactic ambiguity reduction and word sense disambiguation as well
as providing innovative and original solutions for constituent generation and
constraints management. To provide an insight into how it operates, the
system's genera... | computer science |
15,684 | Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing | cs.CL | One of the limitations of semantic parsing approaches to open-domain question
answering is the lexicosyntactic gap between natural language questions and
knowledge base entries -- there are many ways to ask a question, all with the
same answer. In this paper we propose to bridge this gap by generating
paraphrases of th... | computer science |
15,685 | A Kernel Independence Test for Geographical Language Variation | cs.CL | Quantifying the degree of spatial dependence for linguistic variables is a
key task for analyzing dialectal variation. However, existing approaches have
important drawbacks. First, they are based on parametric models of dependence,
which limits their power in cases where the underlying parametric assumptions
are violat... | computer science |
15,686 | Sentiment Analysis of Twitter Data: A Survey of Techniques | cs.CL | With the advancement of web technology and its growth, there is a huge volume
of data present in the web for internet users and a lot of data is generated
too. Internet has become a platform for online learning, exchanging ideas and
sharing opinions. Social networking sites like Twitter, Facebook, Google+ are
rapidly g... | computer science |
15,687 | Recurrent Neural Network Postfilters for Statistical Parametric Speech
Synthesis | cs.CL | In the last two years, there have been numerous papers that have looked into
using Deep Neural Networks to replace the acoustic model in traditional
statistical parametric speech synthesis. However, far less attention has been
paid to approaches like DNN-based postfiltering where DNNs work in conjunction
with tradition... | computer science |
15,688 | Zipf's law is a consequence of coherent language production | cs.CL | The task of text segmentation may be undertaken at many levels in text
analysis---paragraphs, sentences, words, or even letters. Here, we focus on a
relatively fine scale of segmentation, hypothesizing it to be in accord with a
stochastic model of language generation, as the smallest scale where
independent units of me... | computer science |
15,689 | Efficient Character-level Document Classification by Combining
Convolution and Recurrent Layers | cs.CL | Document classification tasks were primarily tackled at word level. Recent
research that works with character-level inputs shows several benefits over
word-level approaches such as natural incorporation of morphemes and better
handling of rare words. We propose a neural network architecture that utilizes
both convoluti... | computer science |
15,690 | The Grail theorem prover: Type theory for syntax and semantics | cs.CL | As the name suggests, type-logical grammars are a grammar formalism based on
logic and type theory. From the prespective of grammar design, type-logical
grammars develop the syntactic and semantic aspects of linguistic phenomena
hand-in-hand, letting the desired semantics of an expression inform the
syntactic type and ... | computer science |
15,691 | Many Languages, One Parser | cs.CL | We train one multilingual model for dependency parsing and use it to parse
sentences in several languages. The parsing model uses (i) multilingual word
clusters and embeddings; (ii) token-level language information; and (iii)
language-specific features (fine-grained POS tags). This input representation
enables the pars... | computer science |
15,692 | Massively Multilingual Word Embeddings | cs.CL | We introduce new methods for estimating and evaluating embeddings of words in
more than fifty languages in a single shared embedding space. Our estimation
methods, multiCluster and multiCCA, use dictionaries and monolingual data; they
do not require parallel data. Our new evaluation method, multiQVEC-CCA, is
shown to c... | computer science |
15,693 | Fantastic 4 system for NIST 2015 Language Recognition Evaluation | cs.CL | This article describes the systems jointly submitted by Institute for
Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine
(LIUM), Nanyang Technology University (NTU) and the University of Eastern
Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The
submitted system is a fusion... | computer science |
15,694 | Swivel: Improving Embeddings by Noticing What's Missing | cs.CL | We present Submatrix-wise Vector Embedding Learner (Swivel), a method for
generating low-dimensional feature embeddings from a feature co-occurrence
matrix. Swivel performs approximate factorization of the point-wise mutual
information matrix via stochastic gradient descent. It uses a piecewise loss
with special handli... | computer science |
15,695 | Exploring the Limits of Language Modeling | cs.CL | In this work we explore recent advances in Recurrent Neural Networks for
large scale Language Modeling, a task central to language understanding. We
extend current models to deal with two key challenges present in this task:
corpora and vocabulary sizes, and complex, long term structure of language. We
perform an exhau... | computer science |
15,696 | Simple Search Algorithms on Semantic Networks Learned from Language Use | cs.CL | Recent empirical and modeling research has focused on the semantic fluency
task because it is informative about semantic memory. An interesting interplay
arises between the richness of representations in semantic memory and the
complexity of algorithms required to process it. It has remained an open
question whether re... | computer science |
15,697 | Automatic Sarcasm Detection: A Survey | cs.CL | Automatic sarcasm detection is the task of predicting sarcasm in text. This
is a crucial step to sentiment analysis, considering prevalence and challenges
of sarcasm in sentiment-bearing text. Beginning with an approach that used
speech-based features, sarcasm detection has witnessed great interest from the
sentiment a... | computer science |
15,698 | TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice
Questions | cs.CL | We describe two new related resources that facilitate modelling of general
knowledge reasoning in 4th grade science exams. The first is a collection of
curated facts in the form of tables, and the second is a large set of
crowd-sourced multiple-choice questions covering the facts in the tables.
Through the setup of the... | computer science |
15,699 | Attention-Based Convolutional Neural Network for Machine Comprehension | cs.CL | Understanding open-domain text is one of the primary challenges in natural
language processing (NLP). Machine comprehension benchmarks evaluate the
system's ability to understand text based on the text content only. In this
work, we investigate machine comprehension on MCTest, a question answering (QA)
benchmark. Prior... | computer science |
15,700 | Exploiting Lists of Names for Named Entity Identification of Financial
Institutions from Unstructured Documents | cs.CL | There is a wealth of information about financial systems that is embedded in
document collections. In this paper, we focus on a specialized text extraction
task for this domain. The objective is to extract mentions of names of
financial institutions, or FI names, from financial prospectus documents, and
to identify the... | computer science |
15,701 | Cross-Language Domain Adaptation for Classifying Crisis-Related Short
Messages | cs.CL | Rapid crisis response requires real-time analysis of messages. After a
disaster happens, volunteers attempt to classify tweets to determine needs,
e.g., supplies, infrastructure damage, etc. Given labeled data, supervised
machine learning can help classify these messages. Scarcity of labeled data
causes poor performanc... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.