手法を比較

	固有表現抽出 (Coreference Resolution)	述語項関係付与（SRL）
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	1978	2002
提唱者	Hobbs (1978); Lee et al. (2017, neural end-to-end)	Daniel Gildea & Daniel Jurafsky
種類	NLP information-extraction task	NLP shallow semantic parsing task
原典	Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.	Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.
別名	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)	SRL, shallow semantic parsing, Anlamsal Rol Etiketleme (SRL)
関連	4	3
概要	Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding.	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.