Cross-lingual and supervised learning approach for Indonesian word sense disambiguation task

Rahmad Mahendra; Heninggar Septiantri; Haryo Akbarianto Wibowo; Ruli Manurung; Mirna Adriani

Cross-lingual and supervised learning approach for Indonesian word sense disambiguation task

Rahmad Mahendra, Heninggar Septiantri, Haryo Akbarianto Wibowo, Ruli Manurung, Mirna Adriani

Research output: Contribution to conference › Paper › peer-review

9 Citations (Scopus)

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.

Original language	English
Publication status	Published - 2018
Event	9th Global WordNet Conference, GWC 2018 - Singapore, Singapore Duration: 8 Jan 2018 → 12 Jan 2018

Conference

Conference	9th Global WordNet Conference, GWC 2018
Country/Territory	Singapore
City	Singapore
Period	8/01/18 → 12/01/18

Cite this

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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.",

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TY - CONF

T1 - Cross-lingual and supervised learning approach for Indonesian word sense disambiguation task

AU - Mahendra, Rahmad

AU - Septiantri, Heninggar

AU - Wibowo, Haryo Akbarianto

AU - Manurung, Ruli

AU - Adriani, Mirna

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85043721949&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85043721949

T2 - 9th Global WordNet Conference, GWC 2018

Y2 - 8 January 2018 through 12 January 2018

ER -

Cross-lingual and supervised learning approach for Indonesian word sense disambiguation task

Abstract

Conference

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