Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task Rahmad Mahendra, Heninggar Septiantri, Haryo Akbarianto Wibowo, Ruli Manurung, and Mirna Adriani Faculty of Computer Science, Universitas Indonesia Depok 16424, West Java, Indonesia
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[email protected] Abstract after the target word), the nearest verb from the target word, the transitive verb around the target Ambiguity is a problem we frequently face word, and the document context. Unfortunately, in Natural Language Processing. Word neither the model nor the corpus from this research Sense Disambiguation (WSD) is a task to is made publicly available. determine the correct sense of an ambigu- This paper reports our study on WSD task for ous word. However, research in WSD for Indonesian using the combination of the cross- Indonesian is still rare to find. The avail- lingual and supervised learning approach. Train- ability of English-Indonesian parallel cor- ing data is automatically acquired using Cross- pora and WordNet for both languages can Lingual WSD (CLWSD) approach by utilizing be used as training data for WSD by ap- WordNet and parallel corpus. Then, the monolin- plying Cross-Lingual WSD method. This gual WSD model is built from training data and training data is used as an input to build it is used to assign the correct sense to any previ- a model using supervised machine learn- ously unseen word in a new context. ing algorithms. Our research also exam- ines the use of Word Embedding features 2 Related Work to build the WSD model. WSD task is undertaken in two main steps, namely 1 Introduction listing all possible senses for a word and deter- mining the right sense given its context (Ide and One of the biggest challenges in Natural Language Veronis,´ 1998).