Sammenlign metoder

	Leksikalsk substitusjon – kontekstsensitiv orderstatning	BERT Embeddings
Fagfelt	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2007	2019
Opphavsperson	McCarthy & Navigli (SemEval shared task, 2007/2009)	Devlin, Chang, Lee & Toutanova (Google AI)
Type	NLP lexical-level text transformation	Contextual transformer text-representation method
Opprinnelig kilde	McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Alias	sözcüksel ikame, Sözcüksel İkame (Lexical Substitution), context-aware synonym replacement, word-level paraphrasing	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Relaterte	4	4
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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.