Open AccessKeyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF
Author(s) -
Zhuohao WANG,
Dong WANG,
Qing LI
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.05.007
Subject(s) - tf–idf , keyword extraction , computer science , information retrieval , precision and recall , social media , word (group theory) , domain (mathematical analysis) , data mining , term (time) , mathematics , world wide web , mathematical analysis , physics , geometry , quantum mechanics
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.