Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science. The problems are overlapping, however, and there is therefore interdisciplinary research on document classification.

The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise specified, text classification is implied.

Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.). In the rest of this article only subject classification is considered. There are two main philosophies of subject classification of documents: the content-based approach and the request-based approach.

"Content-based" versus "request-based" classification

Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about the class to which the book is assigned. In automatic classification it could be the number of times given words appears in a document.

Classification versus indexing

Sometimes a distinction is made between assigning documents to classes ("classification") versus assigning subjects to documents ("subject indexing") but as Frederick Wilfrid Lancaster has argued, this distinction is not fruitful. "These terminological distinctions,” he writes, “are quite meaningless and only serve to cause confusion” (Lancaster, 2003, p. 21. The view that this distinction is purely superficial is also supported by the fact that a classification system may be transformed into a thesaurus and vice versa (cf., Aitchison, 1986, 2004;[5] Broughton, 2008;[6] Riesthuis&Bliedung, 1991. Therefore, is the act of labeling a document (say by assigning a term from a controlled vocabulary to a document) at the same time to assign that document to the class of documents indexed by that term (all documents indexed or classified as X belong to the same class of documents).

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,[8] where parts of the documents are labeled by the external mechanism. There are several software products under various license models available.

Techniques

Automatic document classification techniques include:

Expectation maximization (EM)

Naive Bayes classifier

tf–idf

Instantaneously trained neural networks

Latent semantic indexing

Support vector machines (SVM)

Artificial neural network

K-nearest neighbour algorithms

Decision trees such as ID3 or C4.5

Concept Mining

Rough set-based classifier

Soft set-based classifier

Multiple-instance learning

Natural language processing approaches

Applications

Classification techniques have been applied to spam filtering, a process which tries to discern E-mail spam messages from legitimate emails

email routing, sending an email sent to a general address to a specific address or mailbox depending on topic

language identification, automatically determining the language of a text

genre classification, automatically determining the genre of a text

readability assessment, automatically determining the degree of readability of a text, either to find suitable materials for different age groups or reader types or as part of a larger text simplification system

sentiment analysis, determining the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.

Article triage, selecting articles that are relevant for manual literature curation, for example as is being done as the first step to generate manually curated annotation databases in biology.

Reference: Wikipedia

Further reading

FabrizioSebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.

Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information Retrieval: Implementing and Evaluating Search Engines. MIT Press, 2010.


Thanks and Regards
Asheesh Kamal