পদ্ধতির তুলনা করুন

	শব্দতাত্ত্বিক বিশ্লেষণ	টেক্সট সেগমেন্টেশন	TF-IDF
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1980	1997	1988
প্রবর্তক	M.F. Porter (Porter stemmer)	Marti A. Hearst (TextTiling)	Salton & Buckley
ধরন	Text-normalisation preprocessing task	NLP document-structure / topic-boundary detection	Text vectorization / term-weighting scheme
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	4	4	3
সারসংক্ষেপ	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.