Vertaile menetelmiä
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| Tekstin deduplikointi× | Tekstinluokittelu× | |
|---|---|---|
| Tieteenala | Tekstinlouhinta | Tekstinlouhinta |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1997 | — |
| Kehittäjä≠ | Andrei Z. Broder (MinHash / Resemblance theory, 1997) | — |
| Tyyppi≠ | Text preprocessing / corpus quality pipeline | Supervised NLP classification task |
| Alkuperäislähde≠ | Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES. link ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| Rinnakkaisnimet | near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection) | text categorization, document classification, topic classification, metin sınıflandırma |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGateAineisto ↗ |
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