विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| टेक्स्ट नॉर्मलाइज़ेशन× | पार्ट-ऑफ-स्पीच टैगिंग (POS टैगिंग)× | |
|---|---|---|
| क्षेत्र | पाठ खनन | पाठ खनन |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष | — | — |
| प्रवर्तक | — | — |
| प्रकार≠ | NLP preprocessing pipeline | NLP sequence-labelling task |
| मौलिक स्रोत≠ | Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015. link ↗ | Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP. link ↗ |
| उपनाम≠ | Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation | part-of-speech tagging, grammatical tagging, Sözcük Türü Etiketleme (POS Tagging) |
| संबंधित | 3 | 3 |
| सारांश≠ | Text normalization is an NLP preprocessing pipeline that converts noisy, abbreviated, or misspelled text — such as SMS messages, social-media posts, and OCR output — into a clean, standardised form. It is a prerequisite step for virtually every downstream NLP task, ensuring that inconsistent surface forms do not degrade tokenisation, parsing, or classification. The method gained systematic academic treatment through Baldwin and Li (2015) and Sproat and Jaitly (2017). | Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction. |
| ScholarGateडेटासेट ↗ |
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