Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Null-skudds klassifisering× | Tekstklassifisering× | |
|---|---|---|
| Fagfelt | Tekstutvinning | Tekstutvinning |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 2019 | — |
| Opphavsperson≠ | Yin, Hay & Roth | — |
| Type≠ | NLP text-classification task | Supervised NLP classification task |
| Opprinnelig kilde≠ | Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923. 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 ↗ |
| Alias≠ | zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification) | text categorization, document classification, topic classification, metin sınıflandırma |
| Relaterte≠ | 3 | 4 |
| Sammendrag≠ | Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets. | 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. |
| ScholarGateDatasett ↗ |
|
|