Chinese Keyword Extraction Method Based on Context and Word Classification

The traditional keyword extraction algorithm TextRank increases the weight of the central word through the correlation between the words and the central word in the co-occurrence window, but the number of words in the co-occurrence window is limited, which will cause the poor expression of the semantic information of the word, and then affect accuracy of the keyword extraction results. In order to solve the limitation of word information in traditional keyword extraction methods and the problem of ignoring polysemy, this paper proposes a Chinese keyword extraction method based on context and semantic classification.