Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Normalização de Texto× | Reconhecimento de Entidades Nomeadas (NER)× | |
|---|---|---|
| Área | Mineração de texto | Mineração de texto |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem | — | — |
| Autor original | — | — |
| Tipo≠ | NLP preprocessing pipeline | NLP sequence-labelling task |
| Fonte seminal≠ | Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| Outros nomes≠ | Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Relacionados | 3 | 3 |
| Resumo≠ | 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). | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGateConjunto de dados ↗ |
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