विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्राकृतिक भाषा जनरेशन× | ट्रांसफ़ॉर्मर (एनएलपी)× | |
|---|---|---|
| क्षेत्र≠ | पाठ खनन | गहन अधिगम |
| परिवार≠ | Process / pipeline | Machine learning |
| उद्भव वर्ष≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2017 |
| प्रवर्तक≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Vaswani, A. et al. |
| प्रकार≠ | NLP generative task — structured data to natural language | Attention-based deep neural network |
| मौलिक स्रोत≠ | Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170. link ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| उपनाम | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| संबंधित≠ | 7 | 4 |
| सारांश≠ | Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents. | The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel. |
| ScholarGateडेटासेट ↗ |
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