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Text-Learning and Related Intelligent Agents: A Survey
July/August 1999 (vol. 14 no. 4)
pp. 44-54
Analysis of text data using intelligent information retrieval, machine learning, natural language processing or other related methods is becoming an important issue for the development of intelligent agents. There are two frequently used approaches to the development of intelligent agents using machine learning techniques: a content-based and a collaborative approach. In the first approach, the content (eg., text) plays an important role, while in the second approach, the existence of several knowledge sources (eg., several users) is required. We can say that the usage of machine learning techniques on text databases (usually referred to as text-learning) is an important part of the content-based approach. Examples are agents for locating information on World Wide Web and Usenet news filtering agents. There are different research questions important for the development of text-learning intelligent agents. We focus on three of them: what representation is used for documents, how is the high number of features dealt with and which learning algorithm is used. These questions are addressed in an overview of the existing approaches to text classification. For illustration we give a brief description of the content-based personal intelligent agent named Personal WebWatcher that uses text-learning for user customized Web browsing.
Citation:
Dunja Mladenic, "Text-Learning and Related Intelligent Agents: A Survey," IEEE Intelligent Systems, vol. 14, no. 4, pp. 44-54, July-Aug. 1999, doi:10.1109/5254.784084
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