Salīdzināt metodes
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| Analīze ar atkarību (Dependency Parsing)× | Leksikālā daudzveidība× | Teksta biežuma analīze× | |
|---|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | — | — | 1949 |
| Autors≠ | — | — | George K. Zipf (frequency-distribution foundation) |
| Tips≠ | NLP syntactic-analysis task | Text quantification / lexical richness measurement | Descriptive text-mining analysis |
| Pirmavots≠ | Nivre, J. (2005). Dependency Grammar and Dependency Parsing. MSI Report. link ↗ | 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. DOI ↗ | Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley. link ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 3 | 3 | 4 |
| Kopsavilkums≠ | 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. |
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