On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping

Mohammad Taher Pilehvar


Abstract
Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.
Anthology ID:
N19-1222
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2151–2156
Language:
URL:
https://aclanthology.org/N19-1222
DOI:
10.18653/v1/N19-1222
Bibkey:
Cite (ACL):
Mohammad Taher Pilehvar. 2019. On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2151–2156, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (Pilehvar, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1222.pdf
Video:
 https://aclanthology.org/N19-1222.mp4