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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/C16-2063.pdf