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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://www.aclweb.org/anthology/S17-1002
DOI:
10.18653/v1/S17-1002
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1002.pdf