So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Thay thế từ vựng× | Nhận dạng thực thể có tên (NER)× | |
|---|---|---|
| Lĩnh vực | Khai phá văn bản | Khai phá văn bản |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2007 | — |
| Người khởi xướng≠ | McCarthy & Navigli (SemEval shared task, 2007/2009) | — |
| Loại≠ | NLP lexical-level text transformation | NLP sequence-labelling task |
| Công trình gốc≠ | McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Tên gọi khác≠ | sözcüksel ikame, Sözcüksel İkame (Lexical Substitution), context-aware synonym replacement, word-level paraphrasing | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|