An improved TextRank keywords extraction algorithm

Keywords extraction is widely used in the field of natural language processing. How to quickly and accurately extract keywords has become the key issue in text processing. At present, there are many methods for keywords extraction, but the accuracy and versatility of the method still have much room for improvement. Thus, an improved TextRank keywords extraction algorithm is proposed in this paper. The algorithm uses the TF-IDF algorithm and the average information entropy algorithm to calculate the importance of words, and then calculates the comprehensive weight of words based on the calculation results in the text. The initial weight of the TextRank algorithm node and the node probability transfer matrix are improved by using the comprehensive weight of words, and the weights of all nodes are iteratively calculated until convergence. The weights of the nodes are sorted to obtain the weight information of the words, then the top N words are selected as the keywords.