Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper presents a neural network approach to document
semantic indexing. A Hopfield net algorithm was used to simulate human associative memory for concept exploration
in the domain of computer science and engineering. INSPEC, a collection of more than 320,000 document abstracts from leading journals, was used as the document testbed. Benchmark tests confirmed that three parameters (maximum number of activated nodes, E - maximum allowable error, and maximum number of iterations) were useful in positively influencing network convergence behavior without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests confirmed our expectation that the Hopfield net algorithm is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end-user vocabularies.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105466 |
| Date | January 1998 |
| Creators | Chen, Hsinchun, Zhang, Yin, Houston, Andrea L. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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