Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Podstawienie leksykalne× | Analiza sentymentu× | |
|---|---|---|
| Dziedzina | Eksploracja tekstu | Eksploracja tekstu |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2007 | — |
| Twórca≠ | McCarthy & Navigli (SemEval shared task, 2007/2009) | — |
| Typ≠ | NLP lexical-level text transformation | NLP text-classification task |
| Źródło pierwotne≠ | McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Inne nazwy≠ | sözcüksel ikame, Sözcüksel İkame (Lexical Substitution), context-aware synonym replacement, word-level paraphrasing | opinion mining, polarity detection, duygu analizi |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | 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. | Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models. |
| ScholarGateZbiór danych ↗ |
|
|