So sánh phương pháp

	Gán nhãn vai trò ngữ nghĩa (SRL)	Phát hiện sự kiện	Nhận dạng thực thể có tên (NER)	Question Answering (QA)
Lĩnh vực	Khai phá văn bản	Khai phá văn bản	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	2002	—	—	—
Người khởi xướng	Daniel Gildea & Daniel Jurafsky	—	—	—
Loại	NLP shallow semantic parsing task	NLP information-extraction task	NLP sequence-labelling task	NLP text-comprehension task
Công trình gốc	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.	Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
Tên gọi khác	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	event extraction, Olay Tespiti (Event Detection)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Liên quan	3	4	3	4
Tóm tắt	Semantic role labeling, introduced by Gildea and Jurafsky in 2002, is a natural-language-processing task that assigns semantic roles — who did what to whom, where, when, and how — to the components around a verb (predicate) in a sentence. It turns plain text into structured predicate-argument representations and is a foundational tool for event extraction.	Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research.	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.	Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.