Multifeature Fusion Keyword Extraction Algorithm Based on TextRank

• Published in: IEEE access Vol. 10; pp. 71805 - 71813
• Main Authors: Guo, Wenming, Wang, Zihao, Han, Fang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; BERT word vector; Bit error rate; Data mining; Dictionaries; Electronic mail; Feature extraction; Information retrieval; Iterative methods; K-Truss graph; Keyword extraction; Keywords; Semantics; Task analysis; TextRank; Trusses; Words (language)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3188861

Keyword extraction is the predecessor of many tasks, and its results directly affect search, recommendation, classification, and other tasks. In this study, we take Chinese text as the research object and propose a multi-feature fusion keyword extraction algorithm combined with BERT semantics and K-Truss graph(BSKT). The BSKT algorithm is based on the TextRank algorithm, which combines BERT semantic features, K-Truss features, and other features. First, the BSKT algorithm obtains the word vectors from the BERT pretraining model to calculate the semantic difference, which is used to optimize the iterative process of the TextRank word graph. Then, the BSKT algorithm obtains its K-Truss graph by decomposing the TextRank word graph and obtains the truss level feature of the word. Finally, by combining the word IDF and truss level features, the BSKT algorithm scores the words to extract keywords. Experimental results show that the BSKT algorithm achieves better performance than the latest keyword extraction algorithm SCTR in the task of extracting 1-10 keywords. Furthermore, the increment in F1 increased by 11.2% when the BSKT algorithm was used to extract three keywords from the Sensor dataset.