Advances in Computing and Network Communications
نویسندگان
چکیده
Keyword extraction has received an increasing attention as important research topic which can lead to have advancements in diverse applications such document context categorization, text indexing and classification. In this paper we propose STF-IDF, a novel semantic method based on TF-IDF, for scoring word importance of informal documents corpus. A set nearly four million from health-care social media was collected trained order draw model find the embeddings. Then, features space were utilized rearrange original TF-IDF scores through iterative solution so improve moderate performance algorithm texts. After testing proposed with 200 randomly chosen documents, our managed decrease mean error rate by factor 50% reaching 13.7%, opposed 27.2% TF-IDF.
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ژورنال
عنوان ژورنال: Lecture notes in electrical engineering
سال: 2021
ISSN: ['1876-1100', '1876-1119']
DOI: https://doi.org/10.1007/978-981-33-6977-1