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Effective Document Management With Categorization
Organizations use a document management system (DMS) to store, track, and manage electronic documents. Document management systems also commonly provide search/retrieval and automatic document routing capabilities. For document management software to truly be capable of accurately finding the documents you need at the time you need them or to route them appropriately, documents must first be labeled in the form of metadata information attached to them. Since each organization has very different types of content and classification categories, document management software must be tailored to each organization. After all, no business is the same.
But how can you customize your document management software to fit your business?
The importance of document categorization for the success of DMS
Document categorization software assists in the organization of electronic documentation via the automatic identification of semantic themes in your documents. Categorization allows each document in your system to be found or routed and analyzed most effectively based on your organization’s needs.
For maximum flexibility and accuracy, categorization software must provide various categorization strategies.
- Machine learning categorization. This type of categorization is best used when your business has training data available for the software to use. From training data, the categorization software is then able to create models which the system can use to categorize new documents. It is important that machine-learning categorization be possible — even with a small amount of training data.
- Topic tagging categorization. With this type of categorization, training data is not needed. The user provides concept tagging rules based on simple phrases, words, suffixes, or prefixes. This approach is simple and useful when the user is familiar with the domain and can craft accurate rules. A limitation of this approach is that the terms used for the categorization rules must be relatively unambiguous to avoid conflicts in categorization.
- Semantic extraction categorization. This type of categorization utilizes semantic entity and event extraction to categorize documents based on names of organization, places, and events. It is suitable when the target categories relate to concepts already covered by available semantic extraction software.
NetOwl’s categorization software provides all three types of categorization strategies, which can be used separately or in combination, and provides an application programming interface. Through both rule-based and learning-based techniques, NetOwl software is able to provide your business with the customizable categorization your business needs.