方法对比

	词汇替换	命名实体识别 (NER)
领域	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline
起源年份	2007	—
提出者	McCarthy & Navigli (SemEval shared task, 2007/2009)	—
类型	NLP lexical-level text transformation	NLP sequence-labelling task
开创性文献	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.
别名	sözcüksel ikame, Sözcüksel İkame (Lexical Substitution), context-aware synonym replacement, word-level paraphrasing	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
相关	4	3
摘要	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.