Deep categorization models detect key pieces of information and act accordingly. Their functionality provides for advanced classification capacities that are able to process text considering morphosyntactic and semantic information by using an expanded set of operators and parameters to do so.
Models can be employed for many different purposes. They combine the most advanced technologies to enable complex functionalities, such as feature-level sentiment analysis and social media language processing. The objective is to extract high-value information from any given text.
For example, a possible application of this technology would be to classify documents according to their degree of confidentiality, so that if sensitive information is detected in the text, accessibility will be restricted. The model detects the type of information that warrants restriction through an advanced text processing language. Likewise, another example could be Voice of the Customer detection, where various models are used to analyze different aspects of customer feedback: the quality of customer service, the product the feedback relates to, the channel used, etc.
If you are already familiar with MeaningCloud, you'll probably know that there is another resource that focuses on text classification: classification models. The difference between these two resources is the scenarios in which they are applied. Text Classification is designed for large classification models where it's not necessary to define rules with great detail in order to successfully classify a text. Deep Categorization, on the other hand, is designed for categorization scenarios where there are not as many categories, but the casuistic for each one of them is complex enough that advanced morphosyntactic and semantic rules are needed for classification with an acceptable degree of precision/recall.
Deep categorization models are one of the analytics services available in MeaningCloud's APIs. In the following sections, we'll learn how to create and use models. You can try it out now.