Open AccessBert-Based Text Keyword Extraction
Author(s) -
Qian Yili,
Chaochao Jia,
Yimei Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1992/4/042077
Subject(s) - computer science , key (lock) , keyword extraction , sentence , rank (graph theory) , word (group theory) , set (abstract data type) , artificial intelligence , information retrieval , data set , natural language processing , data mining , mathematics , programming language , geometry , computer security , combinatorics
With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.