Abstract
This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.- Anthology ID:
- W17-1905
- Volume:
- Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Jose Camacho-Collados, Mohammad Taher Pilehvar
- Venue:
- SENSE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 37–46
- Language:
- URL:
- https://aclanthology.org/W17-1905
- DOI:
- 10.18653/v1/W17-1905
- Cite (ACL):
- Kentaro Kanada, Tetsunori Kobayashi, and Yoshihiko Hayashi. 2017. Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 37–46, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations (Kanada et al., SENSE 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W17-1905.pdf