Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

We introduce a globally normalized transition-based neural network model that achieves
state-of-the-art part-of-speech tagging, dependency parsing and sentence compression …

Syntactic constituency parsing is a fundamental problem in naturallanguage processing
which has been the subject of intensive researchand engineering for decades. As a result …

We present a new edition of the Google Books Ngram Corpus, which describes how often
words and phrases were used over a period of five centuries, in eight languages; it reflects …

Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical forxm and computing the answer given a structured …

We present structured perceptron training for neural network transition-based dependency
parsing. We learn the neural network representation using a gold corpus augmented by a …

The prototypical NLP experiment trains a standard architecture on labeled English data and
optimizes for accuracy, without accounting for other dimensions such as fairness …

Question Answering (QA) is undoubtedly a growing field of current research in Artificial
Intelligence. Question classification, a QA subtask, aims to associate a category to each …

We revisit domain adaptation for parsers in the neural era. First we show that recent
advances in word representations greatly diminish the need for domain adaptation when the …

In sentence modeling and classification, convolutional neural network approaches have
recently achieved state-of-the-art results, but all such efforts process word vectors …