tf-idf批量提取英文文献关键词,并且备选关键词来自于特定文件
时间: 2024-05-20 07:16:10 浏览: 103
TF-IDF is a technique used to extract keywords from a batch of English literature, and the candidate keywords are taken from a specific file.
The following steps can be followed to perform TF-IDF keyword extraction:
1. Preprocessing: The text data is preprocessed to remove stopwords, punctuations, and other characters that do not provide any useful information.
2. Tokenization: The text is tokenized into individual words or phrases.
3. Calculation of TF-IDF score: The TF-IDF score is calculated for each term in the document. This score indicates the importance of each term in the document.
4. Rank the terms: The terms are ranked based on their TF-IDF scores.
5. Select the top keywords: The top keywords are selected based on their TF-IDF scores.
For the second part of the question, where the candidate keywords are taken from a specific file, the following steps can be used:
1. Read in the file containing the candidate keywords.
2. Preprocess the candidate keywords by removing any stopwords, punctuations, and other characters that do not provide any useful information.
3. Tokenize the candidate keywords into individual words or phrases.
4. Compare the candidate keywords with the keywords extracted using TF-IDF.
5. Select the candidate keywords that match with the extracted keywords.
6. Output the selected candidate keywords as the final set of keywords.
By using this approach, it is possible to extract relevant keywords from a batch of English literature while also ensuring that the candidate keywords are relevant to the domain or topic of interest.
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