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
Venues:
*SEM | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://aclanthology.org/S17-1002
DOI:
10.18653/v1/S17-1002
Bibkey:
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., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-1002.pdf