방법 비교

	형태소 분석	언어 식별 (LID)	Text Segmentation	TF-IDF
분야	텍스트 마이닝	텍스트 마이닝	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1980	—	1997	1988
창시자	M.F. Porter (Porter stemmer)	—	Marti A. Hearst (TextTiling)	Salton & Buckley
유형	Text-normalisation preprocessing task	NLP text-classification task	NLP document-structure / topic-boundary detection	Text vectorization / term-weighting scheme
원전	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	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.
별칭	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	language detection, LID, Dil Tanımlama (Language Identification)	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
관련	4	4	4	3
요약	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.	Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.	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.