Context (archaeology)

Linguistics

Philosophy

Biology

Social psychology

Word-sense disambiguation

Scope (computer science)

Economics

Set (abstract data type)

Computer science

Semantics (computer science)

WordNet

Paleontology

Natural language processing

Psychology

Task (project management)

Mathematics

Politics

Management

Political science

Psychotherapist

SemEval

Artificial intelligence

Geometry

Representation (politics)

Strengths and weaknesses

Information retrieval

Point (geometry)

Programming language

Word (group theory)

Meaning (existential)

Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory. Recent breakthroughs in representation learning have fueled intensive WSD research, resulting in considerable performance improvements, breaching the 80% glass ceiling set by the inter-annotator agreement. In this survey, we provide an extensive overview of current advances in WSD, describing the state of the art in terms of i) resources for the task, i.e., sense inventories and reference datasets for training and testing, as well as ii) automatic disambiguation approaches, detailing their peculiarities, strengths and weaknesses. Finally, we highlight the current limitations of the task itself, but also point out recent trends that could help expand the scope and applicability of WSD, setting up new promising directions for the future.

Recent Trends in Word Sense Disambiguation: A Survey