Sammenlign metoder

	Leksikalsk substitusjon – kontekstsensitiv orderstatning	Navngitt enhetsgjenkjenning (NER)
Fagfelt	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2007	—
Opphavsperson	McCarthy & Navigli (SemEval shared task, 2007/2009)	—
Type	NLP lexical-level text transformation	NLP sequence-labelling task
Opprinnelig kilde	McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	sözcüksel ikame, Sözcüksel İkame (Lexical Substitution), context-aware synonym replacement, word-level paraphrasing	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Relaterte	4	3
Sammendrag	Lexical substitution is a natural-language-processing task — formalised by McCarthy and Navigli through the SemEval shared task series starting in 2007 — that replaces a target word in a sentence with a semantically equivalent alternative that preserves the meaning of the surrounding context. It draws on synonym resources such as WordNet or on distributional word embeddings and masked language models to generate and rank candidate replacements, and is used for text robustness testing, style adaptation, and training-data augmentation.	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.