Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network
Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz
Abstract
Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.- Anthology ID:
- S17-1002
- Volume:
- Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Nancy Ide, Aurélie Herbelot, Lluís Màrquez
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–21
- Language:
- URL:
- https://aclanthology.org/S17-1002
- DOI:
- 10.18653/v1/S17-1002
- Cite (ACL):
- Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, and Vered Shwartz. 2017. Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 12–21, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network (Rajana et al., *SEM 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S17-1002.pdf