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A LEARNING SCHEME FOR INFORMATION-RETRIEVAL IN HYPERTEXT
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AI
DOI：10.1016/0306-4573(94)90037-X.png)
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摘要
En 中文
In proposing a searching strategy well suited to the hypertext environment, we have considered four criteria: (1) the retrieval scheme should be integrated into a large hypertext environment; (2) the retrieval process should be operable with an unrestricted text collection; (3) the processing time should be reasonable; and (4) the system should be capable of learning in order to improve its retrieval effectiveness. To satisfy these four criteria, we have designed and implemented a search strategy for hypertext systems based on an extended Boolean model (the p-norm scheme) and supplemented it with links to improve the ranking of the retrieved items in a sequence most likely to fulfill the intent of the user. These links, representing additional information about document content, are established according to the requests and relevance judgments. Using a fully automatic procedure, our retrieval scheme can be applied to most existing systems. Based on the CACM test collection, which includes 3,204 documents and the CISI corpus (1,460 documents), we have built a hypertext and evaluated our proposed strategy. The retrieval effectiveness of our solution presents encouraging results.
Keyword:
DOCUMENT-RETRIEVAL
SYSTEMS
TERMS
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