Sense Tagging a Persian Corpus

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Farsi Nejad, Ali | 2013

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Type of Document: M.Sc. Thesis
Language: English
Document No: 44219 (31)
University: Sharif University of Technology
Department: Languages and Linguistics Center
Advisor(s): Khosravizade, Parvaneh; Shams Fard, Mehrnoosh
Abstract:
The main focus of this research is to resolve the semantic ambiguity in Persian. In this study, a semi-supervised machine learning method is proposed to choose the most proper meaning of a target word in the context. Several statistical methods are compared, and the most accurate one is chosen for developing a sense tagger. An initial seed data is built by searching collocation lists for each sense. After developing the sense tagger and initial seed set, a bootstrapping method is used to sense tag all occurences of a target word in corpus with 90% accuracy
Keywords:
Classification ; Bootstrapping ; Word Sense Disambiguation (WSD) ; Tagging ; Sense Tagged Corpus

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