Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vārdšķiras atpazīšana (POS Tagging)× | Teksta segmentēšana× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | — | 1997 |
| Autors≠ | — | Marti A. Hearst (TextTiling) |
| Tips≠ | NLP sequence-labelling task | NLP document-structure / topic-boundary detection |
| Pirmavots≠ | Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP. link ↗ | Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64. link ↗ |
| Citi nosaukumi≠ | part-of-speech tagging, grammatical tagging, Sözcük Türü Etiketleme (POS Tagging) | topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation) |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text. |
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