Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

Ranking documents with a thesaurus.

Abstract: This article reports on exploratory experiments in evaluating and improving a thesaurus through studying its effect on retrieval. A formula called DISTANCE was developed to measure the conceptual distance between queries and documents encoded as sets of thesaurus terms. DISTANCE references MeSH (Medical Subject Headings) and assesses the degree of match between a MeSH-encoded query and document. The performance of DISTANCE on MeSH is compared to the performance of people in the assessment of conceptual distance between queries and documents, and is found to simulate with surprising accuracy the human performance. The power of the computer simulation stems both from the tendency of people to rely heavily on broader-than (BT) relations in making decisions about conceptual distance and from the thousands of accurate BT relations in MeSH. One source for discrepancy between the algorithms' measurement of closeness between query and document and people's measurement of closeness between query and document is occasional inconsistency in the BT relations. Our experiments with adding non-BT relations to MeSH showed how these non-BT non-BT relations to MeSH showed how these non-BT relations could improve document ranking, if DISTANCE were also appropriately revised to treat these relations differently from BT relations.

Document retrieval using fuzzy-logic inference

Abstract: The results of a full-text, document search by a character string search processor are treated as vector patterns whose elements become a term match grade by use of a membership function of the term match frequency. The closest pattern to the query pattern is found by the similarity between the query pattern and each of the filed sample patterns. The similarity is calculated by use of fuzzy-logic. The similarity is ranked in order of similarity magnitude, thereby reducing the search time. The search time can be shortened by categorizing the filed patterns by term set and similarity to a cluster center pattern. If the cluster center patterns are stored, the closest cluster address can be inferred by fuzzy logic inference from the match between the query document and the term set or the similarity of the query to the cluster center.

Document retrieval system with access control

Abstract: An electonic document retrieval system and method for a collection of information distributed over a network having documents stored in web or document servers in which an access control list relates user identification to documents to which a user has access. No access control lists are contained in the documents themselves nor are comparisons made between lists of users, with their access levels, and the classifications of documents. Rather, by the use of URLs or pointers, it is possible to associate every document to which a user has access with the user identification number or code. URLs have a hierchical format which allows partial URLs to indicate levels of access. HTTP protocol, FTP and CGI protocol employ URL calls for documents and can use the access control method and system of the present invention. When a search query is applied to a query server, a list of hits is returned, together with pertinent URLs. The query server consults each access control list associated with each document server, to present to the user only those URLs for which he has a proper access level. Other URLs for which the user does not have proper access are kept hidden from the user.

Chinese text retrieval without using a dictionary

Abstract: It is generafly believed that words, rather than characters, should be the smallest indexing unit for Chinese text retrieval systems, and that it is essential to have a comprehensive Chinese dictionary or lexicon for Chhmse text retrieval systems to do well. Chinese text has no delimiters to mark woni boundaries. As a result, any text retrieval systems that build word-based indexes need to segment text into words. We implemented several statistical and dictionary-hazed word segmentation methods to study the effect on retrieval effectiveness of different segmentation methods using the TREC-S Chinese test collection and topics. The results show that, for all three sets of queries, the simple bigram indexing and the purely statistical word segmentation perform better than the popular dictionary-based maximum matching method with a dictionary of 138,955 entries.