Võrdle meetodeid

	Teksti täitmine – lünkade täitmine	Nimetatud üksuste äratundmine (NER)	Sentimentanalüüs	Teksti klassifitseerimine
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1953 (cloze); 2019 (neural span infilling)	—	—	—
Looja	Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)	—	—	—
Tüüp	NLP conditional text generation task	NLP sequence-labelling task	NLP text-classification task	Supervised NLP classification task
Algallikas	Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Rööpnimetused	cloze procedure, cloze test, masked language modeling, span infilling	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Seotud	4	3	3	4
Kokkuvõte	Text infilling is a natural-language-processing task that completes missing words, phrases, or spans in a document by exploiting the surrounding context. Introduced as the cloze procedure by Wilson L. Taylor in 1953 as a readability measure, it was reformulated for neural models by Zhu et al. (2019) and is now used for data augmentation, writing assistance, and language-model evaluation.	Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.