Sammenlign metoder

	Morfologisk analyse	Sprogidentifikation (LID)	Tekstsegmentering
Fagområde	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	1980	—	1997
Ophavsperson	M.F. Porter (Porter stemmer)	—	Marti A. Hearst (TextTiling)
Type	Text-normalisation preprocessing task	NLP text-classification task	NLP document-structure / topic-boundary detection
Oprindelig kilde	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.
Aliasser	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)
Relaterede	4	4	4
Resumé	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.