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 conferencePaperpeer-review

1 Citation (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 languageEnglish
Publication statusPublished - 1 Jan 2018
Event9th Global WordNet Conference, GWC 2018 - Singapore, Singapore
Duration: 8 Jan 201812 Jan 2018

Conference

Conference9th Global WordNet Conference, GWC 2018
Country/TerritorySingapore
CitySingapore
Period8/01/1812/01/18

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