Document (#23706)

Author

Chen, H.
Zhang, Y.
Houston, A.L.

Title

Semantic indexing and searching using a Hopfield net

Source

Journal of information science. 24(1998) no.1, S.3-18.

Year

1998

Abstract

Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour 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 conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies

Theme

Retrievalalgorithmen
Semantisches Umfeld in Indexierung u. Retrieval

Object

Hopfield-Netze

Aid

Hopfield-Netze

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