Keywords Extraction from Persian Thesis Using Statistical Features and Bayesian Classification

Keyword extraction aims to extract words that are able to represent the corpus meaning. Keyword extraction has a crucial role in information retrieval, recommendation systems and corpora classification. In Persian language, keyword extraction is known as hard task due to Persian’s inherent complication. In this research work, we aim to address keyword extraction with a combination of statistical and Machine Learning as a novel approach to this problem. First the required preprocessing is applied to the corpora. Then three statistical methods and Bayesian classifier was utilized to the corpora to extract the keywords pattern. Also, a post processing methods was used to decrease the number of True Positive outputs. It should be pointed out that the built model can extract up to 20 keywords and they will be compared with keywords in the corresponding corpus. The evaluation results indicate that the proposed method, could extract keywords from scientific corpora (Specifically Thesis and Dissertations) with a good accuracy.