Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval

Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval

Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval

Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval

Excerpt

Paul S. Jacobs Artificial Intelligence Program GE Research and Development Center Schenectady, NY 12301 USA

1.1 A New Opportunity

Huge quantities of readily available on-line text raise new challenges and opportunities for artificial intelligence systems. The ease of acquiring text knowledge suggests replacing, or at least augmenting, knowledge-based systems with "text-based" intelligence wherever possible. Making use of this text knowledge demands more work in robust processing, retrieval, and presentation of information, but raises a host of new applications of AI technologies, where on-line information exists but knowledge bases do not.

Most AI programs have failed to "scale up" because of the difficulty of developing large, robust knowledge bases. At the same time, rapid advances in networks and information storage now provide access to knowledge bases millions of times larger--in text form. No knowledge representation claims the expressive power or the compactness of this raw text. The next generation of AI applications, therefore, may well be "text-based" rather than knowledge based, deriving more power from large quantities of stored text than from hand-crafted rules.

Text-based intelligent systems can combine artificial intelligence techniques with more robust but "shallower" methods. Natural language processing (NLP) research has been hampered, on the one hand, by the limitations of deep systems that work only on a very small number of texts (often only one), and, on the other hand, by the failure of more mature technologies, such as parsing, to apply to practical systems. Information retrieval (IR) systems offer a vehicle where selected NLP methods can produce useful results; hence, there is a natural and potentially important marriage between IR and NLP. This synergy extends beyond the traditional realms of either technology to a variety of emerging applications.

As examples, we must consider what a knowledge-based system can offer in the domain of medical diagnosis, on-line operating systems, fault diagnosis in engines, or financial advising, that cannot be found in a medical text-

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