Lexical Simplification with the Deep Structured Similarity Model

Lis Pereira, Xiaodong Liu, John Lee


Abstract
We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.
Anthology ID:
I17-2073
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
430–435
Language:
URL:
https://aclanthology.org/I17-2073
DOI:
Bibkey:
Cite (ACL):
Lis Pereira, Xiaodong Liu, and John Lee. 2017. Lexical Simplification with the Deep Structured Similarity Model. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 430–435, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Lexical Simplification with the Deep Structured Similarity Model (Pereira et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2073.pdf