Articles

AbstractIn the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document …...

In this paper, we explore a novel framework to generate a well-known text cloud visualiza- tion with the conceptual sense. The traditional text cloud is usually generated according to the word occurrence, possibly including the idf-based concept for word weight. The solution is applicable for the long articles. However, for …...

Neural Machine Translation (NMT), like many other deep learning domains, typ- ically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT mod- els, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are com- puted for …...

ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson cicerons@us.ibm.com Ça˘glar G˙ulçehre Université de Montréal gulcehrc@iro.umontreal.ca Bing Xiang IBM Watson bingxia@us.ibm.com Abstract In this work, we model abstractive text summarization using Attentional Encoder- Decoder Recurrent Neural …...

We propose an unsupervised approach for substring-based transliteration which in- corporates two new sources of knowledge in the learning process: (i) context by learning substring mappings, as opposed to single character mappings, and (ii) pho- netic features which capture cross-lingual character similarity via prior distributions. Our approach is a two-stage …...

For most entity disambiguation systems, the secret recipes are feature representa- tions for mentions and entities, most of which are based on Bag-of-Words (BoW) representations. several drawbacks:Commonly, BoW has (1) It ignores the intrinsic meaning of words/entities; (2) It often results in high-dimension vector spaces and expensive computation; (3) For …...

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 pro- posed method jointly maps words and enti- …...

Coreference resolution for event men- tions enables extraction systems to process document-level information. Current sys- tems in this area base their decisions on rich semantic features from various knowledge bases, thus restricting them to domains where such external sources are available. We propose a model for this task which does …...

Wikipedia is a resource of choice ex- ploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource. In this work, we revisit a seldom studied task which consists in identifying in a Wikipedia article all the mentions of the main …...

Named Entity Recognition (NER) mod- els for language L are typically trained using annotated data in that language. We study cross-lingual NER, where a model for NER in L is trained on an- other, source, language (or multiple source languages). We introduce a language independent method for NER, building on …...

Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation en- vironments. Such scenarios can be im- proved if quality predictions are accompa- nied by a measure of uncertainty. How- ever, models in this task are tradition- ally evaluated only in terms of point …...

Current automatic machine translation systems require heavy human proof- reading to produce high-quality transla- tions. We present a new interactive ma- chine translation approach aimed at pro- viding a natural collaboration between hu- mans and translation systems. As such, we grant the user complete freedom to vali- date and correct …...

We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy in- ference algorithm has linear time, includ- ing feature extraction. On the CoNLL 2008–9 English shared tasks, we obtain the best published …...

Cross lingual projection of linguistic an- notation suffers from many sources of bias and noise, leading to unreliable annota- tions 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 layer. …...

We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for mod- els to capitalize on contextual features, be- yond lexical and syntactic cues present in utterances. For example, different speak- ers will tend to employ sarcasm regard- ing different subjects and, thus, sarcasm …...

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of read- ers. Statistical classification using our …...

This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a se- quence of utterances. We create a manually- labeled dataset of dialogue from TV series ‘Friends’ annotated with sarcasm. Our goal is to predict sarcasm …...

We present methods for investigating pro- cesses of evolution in a language fam- ily by modeling relationships among the observed languages. The models aim to find regularities—regular correspon- dences in lexical data. We present an al- gorithm which codes the data using pho- netic features of sounds, and learns long- …...

In this work, we explore how learners can infer second-language noun meanings in the context of their native language. Mo- tivated by an interest in building interac- tive tools for language learning, we collect data on three word-guessing tasks, analyze their difficulty, and explore the types of er- rors that …...

This paper studies how word embeddings trained on the British National Corpus in- teract with part of speech boundaries. Our work targets the Universal PoS tag set, which is currently actively being used for annotation of a range of languages. We ex- periment with training classifiers for pre- dicting PoS …...