Abstract
Many knowledge-based solutions were proposed to solve Word Sense Disambiguation (WSD) problem with limited annotated resources. Such WSD algorithms are able to cover very large sense repositories, but still being outperformed by supervised ones on benchmark data. In this paper, we start with analysis identifying key properties and issues in application of spreading activation algorithms in knowledge-based WSD, e.g. influence of the network local structures, interaction with context information and sense frequency. Taking our observations as a point of departure, we introduce a novel solution with new context-to-sense matching using BERT embeddings, iterative parallel spreading activation function and selective sense alignment using contextual BERT embeddings. The proposed solution obtains performance beyond the state-of-the-art for the contemporary knowledge-based WSD approaches for both English and Polish data.- Anthology ID:
- 2023.gwc-1.17
- Volume:
- Proceedings of the 12th Global Wordnet Conference
- Month:
- January
- Year:
- 2023
- Address:
- University of the Basque Country, Donostia - San Sebastian, Basque Country
- Editors:
- German Rigau, Francis Bond, Alexandre Rademaker
- Venue:
- GWC
- SIG:
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 140–149
- Language:
- URL:
- https://aclanthology.org/2023.gwc-1.17
- DOI:
- Cite (ACL):
- Arkadiusz Janz and Maciej Piasecki. 2023. Word Sense Disambiguation Based on Iterative Activation Spreading with Contextual Embeddings for Sense Matching. In Proceedings of the 12th Global Wordnet Conference, pages 140–149, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.
- Cite (Informal):
- Word Sense Disambiguation Based on Iterative Activation Spreading with Contextual Embeddings for Sense Matching (Janz & Piasecki, GWC 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.gwc-1.17.pdf