Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …

Objective This article summarizes the preparation, organization, evaluation, and results of
Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on …

The ambitious goal of this work is to develop a cross-lingual name tagging and linking
framework for 282 languages that exist in Wikipedia. Given a document in any of these …

Vector space word representations are learned from distributional information of words in
large corpora. Although such statistics are semantically informative, they disregard the …

We present two simple modifications to the models in the popular Word2Vec tool, in order to
generate embeddings more suited to tasks involving syntax. The main issue with the original …

Continuous space word embeddings learned from large, unstructured corpora have been
shown to be effective at capturing semantic regularities in language. In this paper we …

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 …

Abstract We present Attract-Repel, an algorithm for improving the semantic quality of word
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …

In this paper, we develop a novel approach to aspect term extraction based on unsupervised
learning of distributed representations of words and dependency paths. The basic idea is to …

Current distributed representations of words show little resemblance to theories of lexical
semantics. The former are dense and uninterpretable, the latter largely based on familiar …