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        <title>Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task</title>
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        <namePart type="given">Rahmad</namePart>
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    <name type="personal">
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        <namePart type="family">Septiantri</namePart>
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    <name type="personal">
        <namePart type="given">Ruli</namePart>
        <namePart type="family">Manurung</namePart>
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    <name type="personal">
        <namePart type="given">Mirna</namePart>
        <namePart type="family">Adriani</namePart>
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            <title>Proceedings of the 9th Global Wordnet Conference</title>
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            <namePart type="given">Christiane</namePart>
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    <abstract>Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task
%A Mahendra, Rahmad
%A Septiantri, Heninggar
%A Wibowo, Haryo Akbarianto
%A Manurung, Ruli
%A Adriani, Mirna
%Y Bond, Francis
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 9th Global Wordnet Conference
%D 2018
%8 January
%I Global Wordnet Association
%C Nanyang Technological University (NTU), Singapore
%F mahendra-etal-2018-cross
%X Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.
%U https://aclanthology.org/2018.gwc-1.28
%P 245-250

Markdown (Informal)

[Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task](https://aclanthology.org/2018.gwc-1.28) (Mahendra et al., GWC 2018)

• Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task (Mahendra et al., GWC 2018)

ACL

• Rahmad Mahendra, Heninggar Septiantri, Haryo Akbarianto Wibowo, Ruli Manurung, and Mirna Adriani. 2018. Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task. In Proceedings of the 9th Global Wordnet Conference, pages 245–250, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.