Uporedite metode

	Mašinsko razumevanje teksta (MRC)	Domensko prilagođavanje	Klasifikacija teksta
Oblast	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Porodica	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	2016	—	—
Tvorac	Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)	—	—
Tip	NLP question-answering task	NLP transfer-learning / fine-tuning pipeline	Supervised NLP classification task
Temeljni izvor	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Drugi nazivi	MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC)	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	text categorization, document classification, topic classification, metin sınıflandırma
Srodne	3	4	4
Sažetak	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.	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.