পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| স্বয়ংক্রিয় পাঠ্য মূল্যায়ন× | টপিক মডেলিং× | |
|---|---|---|
| ক্ষেত্র≠ | টেক্সট খনন | গভীর শিখন |
| পরিবার≠ | Process / pipeline | Machine learning |
| উদ্ভবের বছর≠ | 2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore) | 1999–2003 |
| প্রবর্তক≠ | BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020) | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| ধরন≠ | Reference-based NLG evaluation metric suite | Unsupervised generative probabilistic model |
| মৌলিক উৎস≠ | Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| অপর নাম≠ | Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| সম্পর্কিত≠ | 4 | 5 |
| সারসংক্ষেপ≠ | Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
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