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."

DTIC retrieval and indexing terminology /

"January 1987."

A citizen's guide on using the Freedom of Information Act and the Privacy Act of 1974 to request government records : second report /

"September 20, 2005."

SFAP home pages : student aid information on the Web.

Mode of access: Internet.

Automatic indexing in information retrieval

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Enhancing Information Retrieval by Using Evolution Strategies

Similar to Genetic algorithm, Evolution strategy is a process of continuous reproduction, trial and selection. Each new generation is an improvement on the one that went before. This paper presents two different proposals based on the vector space model (VSM) as a traditional model in information Retrieval (TIR). The first uses evolution strategy (ES). The second uses the document centroid (DC) in query expansion technique. Then the results are compared; it was noticed that ES technique is more efficient than the other methods.

Digital Preservation and Web Access to the Konkoly Observatory Schmidt Telescope Plate Archive

ACM Computing Classification System (1998): J.2.

Web-based report generators, data visualization, and Web-to-database connectivity

Because some Web users will be able to design a template to visualize information from scratch, while other users need to automatically visualize information by changing some parameters, providing different levels of customization of the information is a desirable goal. Our system allows the automatic generation of visualizations given the semantics of the data, and the static or pre-specified visualization by creating an interface language. We address information visualization taking into consideration the Web, where the presentation of the retrieved information is a challenge. ^ We provide a model to narrow the gap between the user's way of expressing queries and database manipulation languages (SQL) without changing the system itself thus improving the query specification process. We develop a Web interface model that is integrated with the HTML language to create a powerful language that facilitates the construction of Web-based database reports. ^ As opposed to other papers, this model offers a new way of exploring databases focusing on providing Web connectivity to databases with minimal or no result buffering, formatting, or extra programming. We describe how to easily connect the database to the Web. In addition, we offer an enhanced way on viewing and exploring the contents of a database, allowing users to customize their views depending on the contents and the structure of the data. Current database front-ends typically attempt to display the database objects in a flat view making it difficult for users to grasp the contents and the structure of their result. Our model narrows the gap between databases and the Web. ^ The overall objective of this research is to construct a model that accesses different databases easily across the net and generates SQL, forms, and reports across all platforms without requiring the developer to code a complex application. This increases the speed of development. In addition, using only the Web browsers, the end-user can retrieve data from databases remotely to make necessary modifications and manipulations of data using the Web formatted forms and reports, independent of the platform, without having to open different applications, or learn to use anything but their Web browser. We introduce a strategic method to generate and construct SQL queries, enabling inexperienced users that are not well exposed to the SQL world to build syntactically and semantically a valid SQL query and to understand the retrieved data. The generated SQL query will be validated against the database schema to ensure harmless and efficient SQL execution. (Abstract shortened by UMI.)^

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.

Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.