Abstract
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be seamlessly extended to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.- Anthology ID:
- 2021.eacl-main.286
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3269–3275
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.286
- DOI:
- 10.18653/v1/2021.eacl-main.286
- Cite (ACL):
- Simone Conia and Roberto Navigli. 2021. Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3269–3275, Online. Association for Computational Linguistics.
- Cite (Informal):
- Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (Conia & Navigli, EACL 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.eacl-main.286.pdf
- Code
- sapienzanlp/multilabel-wsd
- Data
- Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison