Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Teksta segmentēšana× | TF-IDF× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1997 | 1988 |
| Autors≠ | Marti A. Hearst (TextTiling) | Salton & Buckley |
| Tips≠ | NLP document-structure / topic-boundary detection | Text vectorization / term-weighting scheme |
| Pirmavots≠ | Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64. link ↗ | Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. DOI ↗ |
| Citi nosaukumi≠ | topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation) | term weighting, tf-idf weighting, TF-IDF Vektörizasyonu |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | 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. |
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