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| 언어적 수용도 평가× | 감성 분석× | |
|---|---|---|
| 분야 | 텍스트 마이닝 | 텍스트 마이닝 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1957 (theory); 2019 (neural benchmark — CoLA) | — |
| 창시자≠ | Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019) | — |
| 유형≠ | NLP binary/continuous classification task | NLP text-classification task |
| 원전≠ | Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| 별칭≠ | grammaticality judgment, acceptability judgment, CoLA task, Dilbilgisel Kabul Edilebilirlik Değerlendirme | opinion mining, polarity detection, duygu analizi |
| 관련≠ | 4 | 3 |
| 요약≠ | Linguistic acceptability assessment is a natural-language-processing task that automatically estimates whether a sentence would be judged grammatically acceptable by a native speaker of the target language. Grounded in Chomsky's (1957) distinction between grammatical and ungrammatical utterances, the task was formalised as a neural benchmark by Warstadt, Singh and Bowman (2019) through the Corpus of Linguistic Acceptability (CoLA). It is used in language-learning research, linguistics studies, and quality auditing of natural-language-generation (NLG) systems. | 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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