Document (#12107)

Author

Cortez, E.M.
Park, S.C.
Kim, S.

Title

¬The hybrid application of an inductive learning method and a neural network

Source

Information processing and management. 31(1995) no.6, S.789-813

Year

1995

Abstract

Traditional information retrieval systems based on Boolean logic suffer from 2 inherent problems: (1) inaccurate or incomplete query representation, and (2) inconsistent indexing. While many researchers have demonstrated that neural networks can solve the incomplete query problems for information retrieval, the inconsistent indexing problem still remains unsolved. In this paper, we present a hybrid methodology of integrating an indictive learning technique with a neural network (connectionist model) in order to solve both inconsistent indexing and incomplete query problems. Since an inductive learning technique has the ability to identify the most significant document index terms with various levels of relationship to their semantic significance, it provides a possible solution to the problem on inconsitent indexing. This paper reports the first phase of research that demonstrates how a neural network augmented by an inductive learning technique results in effective information retrieval performance in the ares that demand flexible inferencing and reasoning when incomplete queries and inconsistent problems are present

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2. Cordón, O.; Herrera-Viedma, E.; Luque, M.; Moya Anegón, F. de; Zarco, C.: ¬An inductive query by example technique for extended Boolean queries based on simulated annealing-programming (2003) 0.30

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3. Chen, H.: Machine learning for information retrieval : neural networks, symbolic learning, and genetic algorithms (1994) 0.24

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4. Fletcher, G.P.; Hinde, C.J.: Using a neural network as a tool for constructing rule based systems (1995) 0.19

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5. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.17

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