Salīdzināt metodes
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| Vārdu nozīmes skaidrošana (WSD)× | Sentimentu analīze× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2009 | — |
| Autors≠ | Navigli (survey, 2009) | — |
| Tips≠ | NLP semantic-disambiguation task | NLP text-classification task |
| Pirmavots≠ | Navigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2), Article 10, 1-69. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Citi nosaukumi | WSD, sense tagging, Sözcük Anlamı Belirsizlik Giderme (WSD) | opinion mining, polarity detection, duygu analizi |
| Saistītās≠ | 2 | 3 |
| Kopsavilkums≠ | Word sense disambiguation (WSD) is the natural-language-processing task of choosing the correct meaning of a polysemous word from its context. Surveyed by Navigli (2009), it resolves which sense of a many-meaning word applies in a given sentence, improving the quality of information retrieval, machine translation, and question answering. | 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. |
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