পদ্ধতির তুলনা করুন

	Constituency Parsing	নামযুক্ত সত্তা শনাক্তকরণ (NER)
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2003	—
প্রবর্তক	Michael Collins (statistical models, 2003)	—
ধরন	NLP syntactic-analysis task	NLP sequence-labelling task
মৌলিক উৎস	Collins, M. (2003). Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, 29(4), 589-637.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
অপর নাম	phrase-structure parsing, constituent parsing, Kurucu Öbek Ayrıştırma (Constituency Parsing)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
সম্পর্কিত	3	3
সারসংক্ষেপ	Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019), it exposes the hierarchical syntactic skeleton of a sentence for grammatical pattern extraction and grammar 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.