Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Teksti segmentimine× | Keel(t)e tuvastamine (LID)× | |
|---|---|---|
| Valdkond | Tekstikaeve | Tekstikaeve |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1997 | — |
| Looja≠ | Marti A. Hearst (TextTiling) | — |
| Tüüp≠ | NLP document-structure / topic-boundary detection | NLP text-classification task |
| Algallikas≠ | Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64. link ↗ | Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations. link ↗ |
| Rööpnimetused≠ | topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation) | language detection, LID, Dil Tanımlama (Language Identification) |
| Seotud | 4 | 4 |
| Kokkuvõte≠ | 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. | Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets. |
| ScholarGateAndmestik ↗ |
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