A keyword extraction method for Chinese professional field text based on improved RAKE

Abstract

An improved RAKE (Rapid Automatic Keyword Extraction) algorithm is proposed to solve the problems of phrase conglutination and inability to obtain professional words in the process of extracting keywords from Chinese professional texts. Through the TTF-IDF (Total Term Frequency-Inverse Document Frequency) method, professional field stop words are extracted and added to the general stop word dictionary for phrase segmentation. Professional domain entity words are introduced into the general word segmentation dictionary, and appropriate weight is given to them in the degree calculation to ensure that professional entity words get higher scores and are effectively extracted as keywords, because in professional field texts, professional entity words contain more core information. The experiments show that this algorithm is better than the basic RAKE and other algorithms in keyword extraction for Chinese professional field texts.