Almost all current dependency parsers classify based on millions of sparse indicator
features. Not only do these features generalize poorly, but the cost of feature computation …

We propose to use the visual denotations of linguistic expressions (ie the set of images they
describe) to define novel denotational similarity metrics, which we show to be at least as …

Syntactic complexity has been an area of significant interest in L2 writing development
studies over the past 45 years. Despite the regularity in which syntactic complexity measures …

Hercules Dalianis Secondary Use of Electronic Patient Records Page 1 Hercules Dalianis
Clinical Text Mining Secondary Use of Electronic Patient Records Page 2 Clinical Text …

This book offers a highly accessible introduction to natural language processing, the field
that supports a variety of language technologies, from predictive text and email filtering to …

Text mining (sometimes called natural language processing (1) or computational linguistics)
is an umbrella term for a range of computational tools and statistical techniques that quantify …

This article provides a comprehensive and comparative overview of question answering
technology. It presents the question answering task from an information retrieval perspective …

Transition-based dependency parsers generally use heuristic decoding algorithms but can
accommodate arbitrarily rich feature representations. In this paper, we show that we can …

We present a simple method for transferring dependency parsers from source languages
with labeled training data to target languages without labeled training data. We first …

Shift-reduce dependency parsers give comparable accuracies to their chartbased
counterparts, yet the best shiftreduce constituent parsers still lag behind the state-of-the-art …