Articles

Public health surveillance is challenging due to difficulties accessing medical data in real-time. We present a novel, ef- fective and computationally inexpensive method for syndromic surveillance using Twitter data. The proposed method uses a regression model on a database previ- ously built using named entity recognition to identify mentions of …...

This study investigates the use of unsu- pervised 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 set of baseline …...

There is a growing body of work study- ing suicide ideation, expressions of in- tentions to kill oneself, on social media. We explore the problem of detecting such ideation on Twitter, focusing on the impact of a set of features drawn from the liter- ature and on the role of …...

This paper presents an extension of neu- ral machine translation (NMT) model to incorporate additional word-level linguis- tic factors. Adding such linguistic fac- tors may be of great benefits to learn- ing of NMT models, potentially reduc- ing language ambiguity or alleviating data sparseness problem (Koehn and Hoang, 2007). We …...

Large volumes of electronic health records, including free-text documents, are extensively generated within various sectors of healthcare. Medical concept annotation systems are designed to enrich these documents with key concepts in the domain using reference terminolo- gies. Although there is a wide range of annotation systems, there is a lack …...

We propose a new open question answer- ing framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is ex- tracted automatically by an open information extraction model, IE KB. Our system consists of only one layer of paraphrase, compared …...

Understanding the nature of the errors of a machine learning system is often difficult for multiclass classification problems with a large number of classes. This is true even more so if the number of examples for each class is low. To interpret the performance of a mul- ticlass classifier, we …...

This paper presents an end-to-end neural net- work model, named Neural Generative Ques- tion Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specif- ically, the model is built on the encoder- decoder framework for sequence-to-sequence learning, while equipped with the …...

We introduce a highly scalable approach for open-domain question answering with no de- pendence on any data set for surface form to logical form mapping or any linguistic ana- lytic tool such as POS tagger or named en- tity recognizer. We define our approach under the Constrained Conditional Models frame- …...

Modern search engines have made dramatic progress in the answering of many user’s ques- tions about facts, such as those that might be retrieved or directly inferred from a knowl- edge base. However, many other questions that real users ask are more complex, such as asking for opinions or advice …...

This work discusses a mix of challenges aris- ing from Watson Discovery Advisor (WDA), an industrial strength descendant of the Wat- son Jeopardy! Question Answering system currently used in production in industry set- tings. Typical challenges include generation of appropriate training questions, adaptation to new industry domains, and iterative im- …...

We propose NEURAL ENQUIRER — a neu- ral network architecture for answering natural language (NL) questions given a knowledge base (KB) table. Unlike previous work on end-to-end training of semantic parsers, NEU- RAL ENQUIRER is fully “neuralized”: it gives distributed representations of queries and KB tables, and executes queries through …...

Understanding open-domain text is one of the primary challenges in NLP. Machine com- prehension benchmarks evaluate a system’s ability to understand text based on the text content only. In this work, we investi- gate machine comprehension on MCTest, a question answering (QA) benchmark. Prior work is mainly based on feature …...

We study the relationship between word order freedom and preordering in statisti- cal machine translation. To assess word order freedom, we first introduce a novel entropy measure which quantifies how dif- ficult it is to predict word order given a source sentence and its syntactic analysis. We then address preordering …...

This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We ex- tensively compare various word-level vo- cabularies to show that the performance of smoothing is not significantly affected by the choice of vocabulary. This result provides empirical evidence that the stan- dard phrase translation model …...

Speed of access is a very important prop- erty for phrase tables in phrase based sta- tistical machine translation as they are queried many times per sentence. In this paper we present a new standalone phrase table, optimized for query speed and mem- ory locality. The phrase table is cache …...

Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic in- formation. In this paper we show that the strong learning capability of neural MT models does not make linguistic fea- tures redundant; they can be easily incor- porated to provide further improvements …...

Training discriminative rule selection models is usually expensive because of the very large size of the hierarchical gram- mar. Previous approaches reduced the training costs either by (i) using mod- els that are local to the source side of the rules or (ii) by heavily pruning out negative samples. Moreover, …...

Neural machine translation (NMT) has emerged recently as a promising statis- tical machine translation approach. In NMT, neural networks (NN) are directly used to produce translations, without re- lying on a pre-existing translation frame- work. In this work, we take a step to- wards bridging the gap between conven- tional …...

We explore two approaches to model com- plement types (NPs and PPs) in an English- to-German SMT system: A simple abstract representation inserts pseudo-prepositions that mark the beginning of noun phrases, to improve the symmetry of source and tar- get complement types, and to provide a flat structural information on …...