A comparative study of keyword extraction algorithms for English texts

Abstract

This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency-inverse document frequency (TF-IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF-IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF-IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF-IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF-IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.