Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Abstract
Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.- Anthology ID:
- E17-1008
- 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
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 76–85
- Language:
- URL:
- https://aclanthology.org/E17-1008
- DOI:
- Cite (ACL):
- Kim Anh Nguyen, Sabine Schulte im Walde, and Ngoc Thang Vu. 2017. Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 76–85, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network (Nguyen et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/E17-1008.pdf
- Code
- nguyenkh/AntSynNET