Neural Sequence Learning Models for Word Sense Disambiguation

Alessandro Raganato, Claudio Delli Bovi, Roberto Navigli


Abstract
Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.
Anthology ID:
D17-1120
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1156–1167
Language:
URL:
https://www.aclweb.org/anthology/D17-1120
DOI:
10.18653/v1/D17-1120
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1120.pdf