Порівняння методів

	Машинне читання з розумінням (MRC)	Адаптація до домену	Сентимент-аналіз
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту
Родина	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	2016	—	—
Автор методу	Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)	—	—
Тип	NLP question-answering task	NLP transfer-learning / fine-tuning pipeline	NLP text-classification task
Основоположне джерело	Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392.	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Інші назви	MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC)	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	opinion mining, polarity detection, duygu analizi
Пов'язані	3	4	3
Підсумок	Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases.	Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.	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.