Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utambuzi wa uhakika× | Uainishaji wa Maandishi× | |
|---|---|---|
| Nyanja | Uchimbaji wa Matini | Uchimbaji wa Matini |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili | — | — |
| Mwanzilishi | — | — |
| Aina≠ | NLP text-classification task | Supervised NLP classification task |
| Chanzo asilia≠ | Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| Majina mbadala≠ | subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection) | text categorization, document classification, topic classification, metin sınıflandırma |
| Zinazohusiana≠ | 3 | 4 |
| Muhtasari≠ | Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGateSeti ya data ↗ |
|
|