Editing Document classification (section)

==Automatic document classification (ADC)==
Automatic document classification tasks can be divided into three sorts: '''supervised document classification''' where some external mechanism (such as human feedback) provides information on the correct classification for documents, '''unsupervised document classification''' (also known as [[document clustering]]), where the classification must be done entirely without reference to external information, and '''semi-supervised document classification''',<ref>
Rossi, R. G., Lopes, A. d. A., and Rezende, S. O. (2016). [https://www.sciencedirect.com/science/article/pii/S0306457315000990 Optimization and label propagation in bipartite heterogeneous networks to improve transductive classification of texts].
Information Processing & Management, 52(2):217–257.
</ref> where parts of the documents are labeled by the external mechanism. There are several software products under various license models available.<ref>{{Cite web |url=https://pdfs.semanticscholar.org/bea4/a204239556a29228decc9e029c326e4900b7.pdf |title=An Interactive Automatic Document Classification Prototype |access-date=2017-11-14 |archive-url=https://web.archive.org/web/20171115082749/https://pdfs.semanticscholar.org/bea4/a204239556a29228decc9e029c326e4900b7.pdf |archive-date=2017-11-15 |url-status=dead }}</ref><ref>[https://seer.lcc.ufmg.br/index.php/jidm/article/download/43/41An Interactive Automatic Document Classification Prototype] {{webarchive |url=https://web.archive.org/web/20150424122349/https://seer.lcc.ufmg.br/index.php/jidm/article/download/43/41An |date=April 24, 2015 }}</ref><ref>[https://archive.today/20141208063727/http://www.artsyltech.com/da_classification.htmlAutomatic Document Classification - Artsyl]</ref><ref>[http://www.abbyy.com/ocr_sdk_windows/what_is_new/classification/ ABBYY FineReader Engine 11 for Windows]</ref><ref>[http://www.antidot.net/classifier/ Classifier - Antidot]</ref><ref>{{Cite web|title=3 Document Classification Methods for Tough Projects|url=https://www.bisok.com/grooper-data-capture-method-features/document-classification/|access-date=2021-08-04|website=www.bisok.com|language=en-US}}</ref>

=== Techniques ===
Automatic document classification techniques include:

* [[Artificial neural network]]
* [[Concept Mining]]
* [[Decision tree learning|Decision trees]] such as [[ID3 algorithm|ID3]] or [[C4.5 algorithm|C4.5]]
* [[Expectation maximization]] (EM)
* [[Instantaneously trained neural networks]]
* [[Latent semantic indexing]]
* [[Multiple-instance learning]]
* [[Naive Bayes classifier]]
* [[Natural language processing]] approaches
* [[Rough set]]-based classifier
* [[Soft set]]-based classifier
* [[Support vector machines]] (SVM)
* [[k-nearest neighbor algorithm|K-nearest neighbour algorithms]]
* [[tf–idf]]