השוואת שיטות

	תיוג תפקידים סמנטיים (SRL)	זיהוי אירועים	זיהוי ישויות מוכרות (NER)
תחום	כריית טקסט	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2002	—	—
הוגה השיטה	Daniel Gildea & Daniel Jurafsky	—	—
סוג	NLP shallow semantic parsing task	NLP information-extraction task	NLP sequence-labelling task
מקור מכונן	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.
כינויים	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)	event extraction, Olay Tespiti (Event Detection)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
קשורות	3	4	3
תקציר	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.