LanguageNet: Learning to Find Sense Relevant Example Sentences
Abstract
In this paper, we present a system, LanguageNet, which can help second language learners to search for different meanings and usages of a word. We disambiguate word senses based on the pairs of an English word and its corresponding Chinese translations in a parallel corpus, UM-Corpus. The process involved performing word alignment, learning vector space representations of words and training a classifier to distinguish words into groups of senses. LanguageNet directly shows the definition of a sense, bilingual synonyms and sense relevant examples.- Anthology ID:
- C18-2022
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico
- Editor:
- Dongyan Zhao
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 99–102
- Language:
- URL:
- https://aclanthology.org/C18-2022
- DOI:
- Cite (ACL):
- Shang-Chien Cheng, Jhih-Jie Chen, Chingyu Yang, and Jason Chang. 2018. LanguageNet: Learning to Find Sense Relevant Example Sentences. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 99–102, Santa Fe, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- LanguageNet: Learning to Find Sense Relevant Example Sentences (Cheng et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C18-2022.pdf
- Data
- LanguageNet