Document classification helps organizations find important information. Supervised and unsupervised methods are used to categorize documents, with rule-based classification giving the user maximum control. Semi-supervised classification combines manual and automated methods. Different algorithms are used for hierarchical organization of documents.
Just as a web browser must organize data so that users can get search results, document classification helps organizations make it easier for them to find important information. Categorizing documents is done differently than using search engine algorithms because specific keywords can have different meanings. This method must be able to assess the context of specific business documents. With supervised document classification, the user tags a set of documents that the automated system can use as a template. In the unsupervised method, they are organized mathematically based on similar words and phrases.
The user has maximum control over document classification when rule-based classification is used. Context, categories and rules are created based on what you enter manually. During the document recovery process, everything is classified according to the exact rules specified by the user. Categories must also be assigned during the supervised method. However, the step of writing the rules that the search system should follow is completed automatically.
With document grouping, also called unsupervised classification, the groupings and categories are all done automatically. There is no manual entry of rules, which can be both beneficial and disadvantageous. This process saves time as there is no need to write rules and you often find similar documents that weren’t initially considered similar. The downside is that documents that weren’t originally intended to be in the same category may appear together. The more automated approach is also more taxing on IT systems.
To strike a balance between the two different methods, computer specialists have come up with the semi-supervised document classification method. Manually classified documents are combined with unlabelled document sets. Programs that can combine information from both use the data to learn how each document is classified. Information retrieval is aided by some control over the classification process. Document clustering is made more efficient when sentences can be used to group them, such as with Suffix Tree Clustering, especially for documents stored online.
Information science has explored various ways to make data mining more efficient. Most businesses are connected to the Internet, so web mining should take as little time as possible to find the relevant documents. Computer scientists have also created several algorithms for organizing documents in a hierarchical manner. Each is effective in its own way, and document classification continues to be studied and defined by different software programs and custom business methods.
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