Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

Coreference resolution aims at resolving repeated references to an object in a document
and forms a core component of natural language processing (NLP) research. When used as …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Commonsense reasoning remains a major challenge in AI, and yet, recent progresses on
benchmarks may seem to suggest otherwise. In particular, the recent neural language …

Large pre-trained neural networks such as BERT have had great recent success in NLP,
motivating a growing body of research investigating what aspects of language they are able …

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

We present an empirical study of gender bias in coreference resolution systems. We first
introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by …

This paper explores the knowledge of linguistic structure learned by large artificial neural
networks, trained via self-supervision, whereby the model simply tries to predict a masked …

Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

This paper develops a novel framework for semantic image retrieval based on the notion of
a scene graph. Our scene graphs represent objects (" man"," boat"), attributes of objects (" …