Research on Keyword Extraction Algorithm Using PMI and TextRank

Keyword extraction is a basic text retrieval technique in natural language processing, which can highly summarize text content and reflect the author's writing purposes. It plays an important role in document retrieval, text classification and data mining. In this paper, we propose a TextRank algorithm based on PMI (pointwise mutual information) weighting for extracting keywords from documents. The initial transition probability of the candidate words is constructed by calculating the PMI between vocabularies, which is used for iterative calculation of the vocabulary graph model within TextRank and keyword extraction. Taking into account the mutual information between the vocabulary in the document set, the word relationship in the single document is corrected, which is helpful to improve the accuracy of document keyword extraction. Experiments show that our method achieves better performance in extracting keywords in large-scale text data.