Сравнение методов

	Заполнение текста	Векторные представления BERT	Распознавание именованных сущностей (NER)	Классификация текстов
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1953 (cloze); 2019 (neural span infilling)	2019	—	—
Автор метода	Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)	Devlin, Chang, Lee & Toutanova (Google AI)	—	—
Тип	NLP conditional text generation task	Contextual transformer text-representation method	NLP sequence-labelling task	Supervised NLP classification task
Основополагающий источник	Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Другие названия	cloze procedure, cloze test, masked language modeling, span infilling	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	text categorization, document classification, topic classification, metin sınıflandırma
Связанные	4	4	3	4
Сводка	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.	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.