Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task

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


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.
Anthology ID:
2018.gwc-1.28
Volume:
Proceedings of the 9th Global Wordnet Conference
Month:
January
Year:
2018
Address:
Nanyang Technological University (NTU), Singapore
Venue:
GWC
SIG:
Publisher:
Global Wordnet Association
Note:
Pages:
245–250
Language:
URL:
https://aclanthology.org/2018.gwc-1.28
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task (Mahendra et al., GWC 2018)
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PDF:
https://aclanthology.org/2018.gwc-1.28.pdf