Abstract
This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such “linguistic regularities” as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity.- Anthology ID:
- S17-1017
- Volume:
- Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 135–148
- Language:
- URL:
- https://aclanthology.org/S17-1017
- DOI:
- 10.18653/v1/S17-1017
- Cite (ACL):
- Anna Rogers, Aleksandr Drozd, and Bofang Li. 2017. The (too Many) Problems of Analogical Reasoning with Word Vectors. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 135–148, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The (too Many) Problems of Analogical Reasoning with Word Vectors (Rogers et al., SemEval-*SEM 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S17-1017.pdf