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
- Editors:
- Francis Bond, Piek Vossen, Christiane Fellbaum
- Venue:
- GWC
- SIG:
- SIGLEX
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 245–250
- Language:
- URL:
- https://aclanthology.org/2018.gwc-1.28
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2018.gwc-1.28.pdf