Keyword Extraction from Scientific Research Projects Based on SRP-TF-IDF

WANG Zhuohao; WANG Dong; LI Qing

doi:10.1049/cje.2021.05.007

Volume 30 Issue 4

Jul. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(4): 652-657

WANG Zhuohao, WANG Dong, LI Qing, “Keyword Extraction from Scientific Research Projects Based on SRP-TF-IDF,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 652-657, 2021, doi: 10.1049/cje.2021.05.007

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WANG Zhuohao, WANG Dong, LI Qing, “Keyword Extraction from Scientific Research Projects Based on SRP-TF-IDF,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 652-657, 2021, doi: 10.1049/cje.2021.05.007

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Keyword Extraction from Scientific Research Projects Based on SRP-TF-IDF

doi: 10.1049/cje.2021.05.007

Institute of Scientific and Technical Information of China, Beijing 100038, China

Received Date: 2021-03-03
Available Online: 2021-07-19
Publish Date: 2021-07-05

Abstract

Abstract

Keyword extraction by Term frequency-Inverse document frequency (TF-IDF) is used for text information retrieval and mining in many domains, such as news text, social contact text, and medical text. However, keyword extraction in special domains still needs to be improved and optimized, particularly in the scientific research field. The traditional TF-IDF algorithm considers only the word frequency in documents, but not the domain characteristics. Therefore, we propose the Scientific research project TF-IDF (SRP-TF-IDF) model, which combines TF-IDF with a weight balance algorithm designed to recalculate candidate keywords. We have implemented the SRP-TF-IDF model and verified that our method has better precision, recall, and F1 score than the traditional TF-IDF and TextRank methods. In addition, we investigated the parameter of our weight balance algorithm to find an optimal value for keyword extraction from scientific research projects.
- Keyword extraction,
- TF-IDF,
- Scientific research project,
- Word vector

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References

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