方法对比

	词形分析	文本分段	TF-IDF
领域	文本挖掘	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1980	1997	1988
提出者	M.F. Porter (Porter stemmer)	Marti A. Hearst (TextTiling)	Salton & Buckley
类型	Text-normalisation preprocessing task	NLP document-structure / topic-boundary detection	Text vectorization / term-weighting scheme
开创性文献	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
别名	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
相关	4	4	3
摘要	Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.	Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.