পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| এন-গ্রাম ভাষা মডেল (N-gram Language Model)× | TF-IDF× | |
|---|---|---|
| ক্ষেত্র | টেক্সট খনন | টেক্সট খনন |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | — | 1988 |
| প্রবর্তক≠ | — | Salton & Buckley |
| ধরন≠ | Statistical language model | Text vectorization / term-weighting scheme |
| মৌলিক উৎস≠ | Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed. link ↗ | Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. DOI ↗ |
| অপর নাম | n-gram model, statistical language model, N-gram Dil Modeli | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition. | TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere. |
| ScholarGateডেটাসেট ↗ |
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