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Composite Feature Extraction and Selection for Text Classification
2019
IEEE Access
Although words are basic semantic units in text, phrases, and expressions contain additional information, which is important for text classification. To capture this information, traditional algorithms extract composite features via word sequences or co-occurrences, such as bigrams and termsets, but ignore the influence of stop words and punctuation, which results in huge amounts of weak features. In this paper, we propose a text structure-based algorithm to extract composite features. Termsets
doi:10.1109/access.2019.2904602
fatcat:ghqr2yb77ja2fjnw3cnmgjiqau