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Using Machine Learning Approaches for Persian Pronoun Resolution

Sadat Moosavi, Nafiseh | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39075 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Ghasem Sani, Gholamreza
  7. Abstract:
  8. Coreference resolution is an essential step toward understanding discourses, and it is needed by many NLP tasks such as summarization, machine translation, question answering, etc. Pronoun resolution is a major and challenging subpart of coreference resolution, in which only the resolution of pronouns is considered. The existing coreference resolution approaches can be classified into two broad categories: linguistic and machine learning approaches. Linguistic approaches need a lot of linguistic information for the resolution process. Acquisition of such information is an error- prone and time-consuming process. In contrast, learning approaches need less linguistic information and provide the state of the art results. In this thesis, we present a framework for the use of machine learning methods for Persian pronoun resolution. We also introduce different methods which can improve the results of presented baseline framework. These methods include: methods for tackling the highly imbalanced training data, the use of ensemble learners rather than the base learners, and the use of ranking approach instead of classification methods
  9. Keywords:
  10. Natural Language Processing ; Machine Learning ; Ensemble Learning ; Ranking ; Classification ; Coreference Resolution ; Persian Pronoun Resolution ; Annotated Corpus ; Tackling Imbalanced Data

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