Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners

Chieh-Yang Huang, Nicole Peinelt, Lun-Wei Ku


Abstract
In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.
Anthology ID:
C16-2063
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Editor:
Hideo Watanabe
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
302–306
Language:
URL:
https://aclanthology.org/C16-2063
DOI:
Bibkey:
Cite (ACL):
Chieh-Yang Huang, Nicole Peinelt, and Lun-Wei Ku. 2016. Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 302–306, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners (Huang et al., COLING 2016)
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PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/C16-2063.pdf