A Research of Term Extraction Based on TF-IDF on LDA Models

Terminology extraction has been gradually introduced into human daily life since the 1980s. In recent years, with the introduction of Open AI, more and more terms appear in text retrieval and other applications. In the research field of term extraction, how to accurately and efficiently obtain key terms from a large number of texts is crucial. This study focuses on the application of TF-IDF algorithm and LDA topic model in this process.TF-IDF algorithm effectively identifies highly distinguishable terms by considering the frequency of the lexical items in the documents and their inverse document frequency in the whole document collection, providing a strong support based on the word frequency statistics for term extraction. Meanwhile, the LDA topic model mines the potential topic structure in the text data from the perspective of topic distribution, helping to extract terms that are closely related to a specific topic. Through the combined use of these two methods, the accuracy of term extraction is not only significantly improved, but also more comprehensively covers the key terms in different subject areas, laying a solid foundation for text analysis and knowledge mining in related fields.