Abstract
In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings achieved 43.15% weighted-F1 accuracy on subtask-1 (checking existence of a relation between two terms) and 25.24% weighted-F1 accuracy on subtask-2 (classifying relation types).- Anthology ID:
- W16-5314
- Volume:
- Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Michael Zock, Alessandro Lenci, Stefan Evert
- Venue:
- CogALex
- SIG:
- SIGLEX
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 104–109
- Language:
- URL:
- https://aclanthology.org/W16-5314
- DOI:
- Cite (ACL):
- Chinnappa Guggilla. 2016. CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 104–109, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks (Guggilla, CogALex 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-5314.pdf