Methoden vergleichen

	Abhängigkeitsanalyse	Lexikalische Diversität – Messung des Wortschatzreichtums	Text Frequency Analysis
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	—	—	1949
Urheber	—	—	George K. Zipf (frequency-distribution foundation)
Typ	NLP syntactic-analysis task	Text quantification / lexical richness measurement	Descriptive text-mining analysis
Wegweisende Quelle	Nivre, J. (2005). Dependency Grammar and Dependency Parsing. MSI Report.	McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.
Aliasnamen	syntactic dependency analysis, dependency tree parsing, Bağımlılık Ayrıştırma (Dependency Parsing)	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi
Verwandt	3	3	4
Zusammenfassung	Dependency parsing is a natural-language-processing task that reveals the syntactic dependency relations between the words of a sentence as a tree structure. Surveyed in the dependency-grammar tradition by Nivre (2005) and made fast and accurate with neural networks by Chen and Manning (2014), it is commonly used as a prerequisite step for information extraction and relation detection.	Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.