Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information

Michele Bevilacqua, Roberto Navigli


Abstract
Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB). We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80% ceiling on the concatenation of all the standard all-words English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English.
Anthology ID:
2020.acl-main.255
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2854–2864
Language:
URL:
https://aclanthology.org/2020.acl-main.255
DOI:
10.18653/v1/2020.acl-main.255
Bibkey:
Cite (ACL):
Michele Bevilacqua and Roberto Navigli. 2020. Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2854–2864, Online. Association for Computational Linguistics.
Cite (Informal):
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (Bevilacqua & Navigli, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.255.pdf
Video:
 http://slideslive.com/38929222
Code
 SapienzaNLP/ewiser
Data
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison