Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Word Sense Disambiguation
Deep Learning
Machine Learning

Author(s) Name:  Michele Bevilacqua, Tommaso Pasini, Alessandro Raganato, Roberto Navigli

Publisher name:  IJCAI

DOI:  10.24963/ijcai.2021/593

Volume Information:  

Paper Link:   https://www.ijcai.org/proceedings/2021/593