LanguageNet: Learning to Find Sense Relevant Example Sentences

Shang-Chien Cheng, Jhih-Jie Chen, Chingyu Yang, Jason Chang


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
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
99–102
Language:
URL:
https://www.aclweb.org/anthology/C18-2022
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-2022.pdf