Abstract
The use of distributional language representations have opened new paths in solving a variety of NLP problems. However, alternative approaches can take advantage of information unavailable through pure statistical means. This paper presents a method for building vector representations from meaning unit blocks called concept definitions, which are obtained by extracting information from a curated linguistic resource (Wiktionary). The representations obtained in this way can be compared through conventional cosine similarity and are also interpretable by humans. Evaluation was conducted in semantic similarity and relatedness test sets, with results indicating a performance comparable to other methods based on single linguistic resource extraction. The results also indicate noticeable performance gains when combining distributional similarity scores with the ones obtained using this approach. Additionally, a discussion on the proposed method’s shortcomings is provided in the analysis of error cases.- Anthology ID:
- E17-1085
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 905–915
- Language:
- URL:
- https://aclanthology.org/E17-1085
- DOI:
- Cite (ACL):
- Danilo Silva de Carvalho and Minh Le Nguyen. 2017. Building Lexical Vector Representations from Concept Definitions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 905–915, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Building Lexical Vector Representations from Concept Definitions (Silva de Carvalho & Nguyen, EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/E17-1085.pdf