Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Klasifikasi Sifar-Shot× | Klasifikasi Teks Sedikit Contoh× | Analisis Sentimen× | |
|---|---|---|---|
| Bidang | Perlombongan Teks | Perlombongan Teks | Perlombongan Teks |
| Keluarga | Process / pipeline | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2019 | — | — |
| Pengasas≠ | Yin, Hay & Roth | — | — |
| Jenis≠ | NLP text-classification task | NLP text-classification task (low-resource) | NLP text-classification task |
| Sumber perintis≠ | Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923. DOI ↗ | Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias≠ | zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification) | few-shot learning for text, Az Atışlı Metin Sınıflandırma (Few-Shot) | opinion mining, polarity detection, duygu analizi |
| Berkaitan≠ | 3 | 4 | 3 |
| Ringkasan≠ | 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. | Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels. | 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. |
| ScholarGateSet data ↗ |
|
|
|