Abstract
We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm’s objective function through a regularizer. The incorporation of lexicographic data yields embeddings that are able to reflect expert-defined word senses, while retaining the robustness, high quality, and coverage of automatic corpus-based methods. These properties are observed in a manual inspection of the semantic clusters that different degrees of regularizer strength create in the vector space. Moreover, we evaluate the sense embeddings in two downstream applications: word sense disambiguation and semantic frame prediction, where they outperform simpler approaches. Our results show that a corpus-based model balanced with lexicographic data learns better representations and improve their performance in downstream tasks.- Anthology ID:
- I17-1029
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 284–294
- Language:
- URL:
- https://aclanthology.org/I17-1029
- DOI:
- Cite (ACL):
- Luis Nieto-Piña and Richard Johansson. 2017. Training Word Sense Embeddings With Lexicon-based Regularization. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 284–294, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Training Word Sense Embeddings With Lexicon-based Regularization (Nieto-Piña & Johansson, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/I17-1029.pdf