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- W70488655 abstract "This paper presents the results of some experiments investigating the use of Neural Networks in the learning engine of an Connectionist Information Retrieval system called CIRS. CIRS uses the learning and generalisation capabilities of the Back Propagation learning algorithm to acquire and use application domain knowledge in the form of a sub-symbolic knowledge representation. This paper describes the architecture of CIRS and reports on experiments on three di erent learning strategies. 1 The Information Retrieval problem The storage, management, and retrieval of weakly structured or unstructured data is usually called IR application. The objects handled by an Information Retrieval (IR) application are usually called documents, where for document we mean any information bearer such as a book, a report, a letter, an image, or a drawing. The software tool which automatically manages these documents is called Information Retrieval System (IRS). The task of an IRS is to help a user to nd, in a collection of documents, those documents which contain the information the user is searching, that is helping the user to satisfy his information need . To give a clue to the size of the task, it must be noticed that usually these collections contain several thousands or even millions of documents. Frequently IR is confused with database (DB) technology. The fundamental di erence between IR and DB is that IR systems usually provide only references to or a description of the data they manage, while a DBMS provides the actual data. User queries to an IRS are usually in the form: I want documents about" @default.
- W70488655 created "2016-06-24" @default.
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- W70488655 modified "2023-09-27" @default.
- W70488655 title "Domain knowledge acquisition for Information Retrieval using neural networks" @default.
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