Presentation 2014-06-15
Computing Cross-Lingual Semantic Textual Similarities based on Machine Learning
WENTAO Luo, Yoshihiko HAYASHI,
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Abstract(in English) This paper proposes a method to compute cross-lingual semantic textual similarities between sentences in different languages. The method is based on a machine-learning approach, which employs several monolingual similarity features chiefly proposed by projects participated in the *SEM STS (Semantic Textual Similarity) task. To utilize the monolingual features in a cross-lingual setting, we tested two methods to cross language borders: one utilizes bilingual dictionaries and the other employs off-the-shelf machine-translation engines. While our experimental results demonstrated such a translate then monolingual similarity method was reasonably effective in cross-lingual STS for English, Japanese, and Chinese, it has also been insisted that syntactic/structural differences have to be incorporated into the machine-learning based framework.
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Keyword(in English) semantic textual similarities / cross-lingual similarities / machine learning / machine translation
Paper # NLC2014-16
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Committee NLC
Conference Date 2014/6/7(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Computing Cross-Lingual Semantic Textual Similarities based on Machine Learning
Sub Title (in English)
Keyword(1) semantic textual similarities
Keyword(2) cross-lingual similarities
Keyword(3) machine learning
Keyword(4) machine translation
1st Author's Name WENTAO Luo
1st Author's Affiliation Graduate School of Language and Culture, Osaka University()
2nd Author's Name Yoshihiko HAYASHI
2nd Author's Affiliation Graduate School of Language and Culture, Osaka University
Date 2014-06-15
Paper # NLC2014-16
Volume (vol) vol.114
Number (no) 81
Page pp.pp.-
#Pages 6
Date of Issue