Lexical Simplification with the Deep Structured Similarity Model
Research output: Conference Papers (RGC: 31A, 31B, 32, 33) › 32_Refereed conference paper (without host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
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| Pages | 430–435 |
| Publication status | Published - 30 Nov 2017 |
Conference
| Title | 8th International Joint Conference on Natural Language Processing (IJCNLP 2017) |
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| Location | Taipei Nangang Exhibition Hall |
| Place | Taiwan |
| City | Taipei |
| Period | 27 November - 1 December 2017 |
Link(s)
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(6cface30-0aae-49f8-ab65-4b1f3cf8b359).html |
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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.
Citation Format(s)
Lexical Simplification with the Deep Structured Similarity Model. / Pereira, Lis; Liu, Xiaodong; Lee, John.
2017. 430–435 Paper presented at 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), Taipei, Taiwan.
2017. 430–435 Paper presented at 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), Taipei, Taiwan.
Research output: Conference Papers (RGC: 31A, 31B, 32, 33) › 32_Refereed conference paper (without host publication) › peer-review
