Sep 24, 2021 · TF-IDF is a popularly used keyword extraction method, but the chance of a word to be chosen as a keyword is only determined by word frequency.

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The topic model based methods extract keywords according to the topic distribution of documents[8]. Among the graph model based keyword extraction, a popular ...

The results showed that the multi-feature fusion TextRank proposed in this paper was significantly better than the five keyword extraction methods. We concluded ...

To solve the problem, this paper proposes an improved keyword extraction algorithm featuring multi-feature fusion, which fuses the respective weights of the ...

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- ...

TextRank Keyword Extraction Method Based on Multi-feature Fusion. https://doi.org/10.1007/978-981-16-2377-6_46. Journal: Proceedings of Sixth International ...

Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword ...

The BSKT algorithm is based on the TextRank algorithm, which combines BERT semantic features, K-Truss features, and other features and achieves better ...

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- ...