Salīdzināt metodes

	Teksta sarežģītības analīze	Konstituentu parsēšana	Sentimentu analīze
Nozare	Teksta ieguve	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	—	2003	—
Autors	—	Michael Collins (statistical models, 2003)	—
Tips	Linguistic-feature measurement pipeline	NLP syntactic-analysis task	NLP text-classification task
Pirmavots	Vajjala, S. & Meurers, D. (2014). Readability Assessment for Text Simplification: From Analysing Documents to Identifying Sentential Simplifications. International Journal of Applied Linguistics, 165(2), 194-222.	Collins, M. (2003). Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, 29(4), 589-637.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Citi nosaukumi	readability analysis, linguistic complexity assessment, Metin Karmaşıklığı Analizi	phrase-structure parsing, constituent parsing, Kurucu Öbek Ayrıştırma (Constituency Parsing)	opinion mining, polarity detection, duygu analizi
Saistītās	2	3	3
Kopsavilkums	Text complexity analysis measures the linguistic difficulty of a text along dimensions such as syntactic complexity (sentence length, embedded clauses), lexical density, and referential chains. Grounded in readability research consolidated by Vajjala and Meurers (2014) and Crossley and colleagues (2011), it turns prose into quantitative scores that estimate how hard a document is to read.	Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019), it exposes the hierarchical syntactic skeleton of a sentence for grammatical pattern extraction and grammar research.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.