Sharif Digital Repository

Pronoun resolution is one of the challenges of natural language processing. The proposed solutions range from heuristic rule-based to machine learning data driven approaches. In this thesis, we followed a previous machine learning base work to Persian pronoun anaphora resolution. The primary goal of this thesis was to improve results, mainly by extracting more balanced data and to add more features to the extracted feature vectors used in classification. Using PCAC2008 dataset, we considered noun phrase structure as a way to extract more suitable training data. Features added to the extracted data include syntactic and semantic features. Then, we trained and tested different machine learning...