Regardless of industry, the overload of information facing most organizations today is a drain on both individuals and the enterprise itself. When it comes to separating the useful information from the irrelevant, document classification is a worthwhile tool that can reduce the cost and time of searching and retrieving the information that matters.
How does document classification work?
Document classification is an age-old problem in information retrieval, and it plays an important role in a variety of applications for effectively managing text and large volumes of unstructured information. Automatic document classification can be defined as content-based assignment of one or more predefined categories (topics) to documents. This makes it easier to find the relevant information at the right time and for filtering and routing documents directly to users.
Document classification has two different methods: manual and automatic classification. In manual document classification, users interpret the meaning of text, identify the relationships between concepts and categorize documents. While this gives users more control over classification, manual classification is both expensive and time consuming.
Automatic document classification applies machine learning or other technologies to automatically classify documents; this results in faster, scalable and more objective classification. There are at least 3 approaches:
- Supervised method: The classifier is trained on a manually tagged set of documents. The classifier can predict new categories and can also provide a confidence indicator. With supervised document classification, the user labels a set of documents that the automated system can use as a model.
- Unsupervised method: Documents are mathematically organized based on similar words and phrases.
- Rules-based method: This method consists of leveraging the natural language understanding capability of a system and writing linguistic rules that would instruct the system to act like a person in classifying a document. This means using the semantically relevant elements of a text to drive the automatic categorization. This method has the advantage of constantly improving the performance (open box approach) instead of relying solely on statistics or mathemathics like the previous two methods. This method is associated with higher quality performance, especially in complex scenarios.
By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort. This is especially useful for publishers, financial institutions, insurance companies or any industry that deals with large amounts of content. An automatic document classification tool can realize a significant reduction in manual entry costs and improve the speed and turnaround time for document processing.
Why Semantic Intelligence is the best option for document classification?
Semantic technology processes and interprets content by relying on a variety of linguistic techniques including text mining, entity extraction, concept analysis, natural language processing, categorization and sentiment analysis. Semantic technology allows the automatic comprehension of words and entire documents, and understands the meanings of words in context.
As opposed to keyword and statistical technologies that process content as data, semantic technology is based on not just data, but the relationships between the data. This ability to understand words in context is what makes automatic classification possible, and enables not only the management of large volumes of data, but the ability to optimize it for even further analysis and intelligence.