A probabilistic model for a specialized information retrieval system /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

An analysis of rotational storage access scheduling in a multiprogrammed information retrieval system /

Thesis--University of Illinois at Urbana-Champaign.

Description of an experimental on-line, minicomputer-based information retrieval system /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Evaluation of selected features of the Eureka full-text information retrieval system /

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An experimental information retrieval system /

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A specialized computer for information retrieval /

Originally presented as the author's thesis, University of Illinois at Urbana-Champaign, 1974.

A thesaurus feature for the EUREKA information retrieval system /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

A list merging processor for inverted file information retrieval systems /

Originally presented as the author's thesis, University of Illinois at Urbana-Champaign.

A unique word-scanning facility for the Eureka full-text information retrieval system /

Thesis (M.S.)--University of Illinois at Urbana-Champaign.

Users' guide to the Solid Waste Information Retrieval System thesaurus /

"Originally issued in 1973 as open-file report SW-104of."

Research study of criteria and procedures for evaluating scientific information retrieval systems; final report. Prepared for Office of Science Information Service, National Science Foundation, Washington, D.C., contract NSF-C218.

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FIRES : Fire Information Retrieval and Evaluation System : a program for fire danger rating analysis /

"August 1997."

Information retrieval and machine translation : based on the International Conference for Standards on a Common Language for Machine Searching and Translation /

Mode of access: Internet.

Document re-ranking by generality in bio-medical information retrieval

Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.

Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.