Implementation of an information-retrieval based CAI system /

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

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.

An experimental information retrieval system /

Bibliography: p. 114.

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.

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.

"AD 273 115."

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.

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.

Internet search:subdivision-based interactive query expansion and the soft semantic web

The expansion of the Internet has made the task of searching a crucial one. Internet users, however, have to make a great effort in order to formulate a search query that returns the required results. Many methods have been devised to assist in this task by helping the users modify their query to give better results. In this paper we propose an interactive method for query expansion. It is based on the observation that documents are often found to contain terms with high information content, which can summarise their subject matter. We present experimental results, which demonstrate that our approach significantly shortens the time required in order to accomplish a certain task by performing web searches.

Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.