@inproceedings{cheng-etal-2018-languagenet,
title = "{L}anguage{N}et: Learning to Find Sense Relevant Example Sentences",
author = "Cheng, Shang-Chien and
Chen, Jhih-Jie and
Yang, Chingyu and
Chang, Jason",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2022",
pages = "99--102",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cheng-etal-2018-languagenet">
<titleInfo>
<title>LanguageNet: Learning to Find Sense Relevant Example Sentences</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shang-Chien</namePart>
<namePart type="family">Cheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jhih-Jie</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chingyu</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jason</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dongyan</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Santa Fe, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">cheng-etal-2018-languagenet</identifier>
<location>
<url>https://aclanthology.org/C18-2022</url>
</location>
<part>
<date>2018-08</date>
<extent unit="page">
<start>99</start>
<end>102</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LanguageNet: Learning to Find Sense Relevant Example Sentences
%A Cheng, Shang-Chien
%A Chen, Jhih-Jie
%A Yang, Chingyu
%A Chang, Jason
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F cheng-etal-2018-languagenet
%X 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.
%U https://aclanthology.org/C18-2022
%P 99-102
Markdown (Informal)
[LanguageNet: Learning to Find Sense Relevant Example Sentences](https://aclanthology.org/C18-2022) (Cheng et al., COLING 2018)
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.