Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

Statistical significance testing is a standard statistical tool designed to ensure that
experimental results are not coincidental. In this opinion/theoretical paper we discuss the …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

Abstract Word Sense Disambiguation (WSD) systems automatically choose the intended
meaning of a word in context. In this article we present a WSD algorithm based on random …

Word sense disambiguation (WSD) systems based on supervised learning achieved the
best performance in SensE-val and SemEval workshops. However, there are few publicly …

This chapter describes how vector space models have been used for document retrieval.
These document‐based models represent the meaning, or topic, of a whole document. It …

Contextualized word representations are able to give different representations for the same
word in different contexts, and they have been shown to be effective in downstream natural …

While neural machine translation (NMT) models provide improved translation quality in an
elegant, end-to-end framework, it is less clear what they learn about language. Recent work …