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| Tekstualno izvođenje× | Analiza sentimenta× | |
|---|---|---|
| Oblast | Rudarenje teksta | Rudarenje teksta |
| Porodica | Process / pipeline | Process / pipeline |
| Godina nastanka | — | — |
| Tvorac | — | — |
| Tip≠ | NLP sentence-pair classification task | NLP text-classification task |
| Temeljni izvor≠ | Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Drugi nazivi≠ | natural language inference, NLI, recognising textual entailment, RTE | opinion mining, polarity detection, duygu analizi |
| Srodne≠ | 4 | 3 |
| Sažetak≠ | Textual entailment, also known as natural language inference (NLI), is the natural-language-processing task of deciding whether one piece of text (the premise) entails a second piece of text (the hypothesis), contradicts it, or is neutral with respect to it. Formalised by the PASCAL Recognising Textual Entailment Challenge (Dagan, Glickman & Magnini, 2006) and broadened by the MultiNLI corpus (Williams, Nangia & Bowman, 2018), it underpins question answering and fact-verification pipelines. | 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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