Search (10 results, page 1 of 1)

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Date

29. 3.1996 18:16:49

Source

TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman

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Abstract

Describes FAQ Finder, a natural language question-answering system that uses files of frequently asked questions as its knowledge base. Unlike information retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ Finder uses a semantic knowledge base (Wordnet) to improve its ability to match question and answer. Includes results from an evaluation of the system's performance and shows that a combination of semantic and statistical techniques works better than any single approach

Theme

Internet

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Abstract

Explores the interaction of textual and photographic information in an integrated text and image database environment and describes 3 different applications involving the exploitation of linguistic context in vision. Describes the practical application of these ideas in working systems. PICTION uses captions to identify human faces in a photograph, wile Show&Tell is a multimedia system for semi automatic image annotation. The system combines advances in speech recognition, natural language processing and image understanding to assist in image annotation and enhance image retrieval capabilities. Presents an extension of this work to video annotation and retrieval

Date

22. 9.1997 19:16:05

Source

Digital image access and retrieval: Proceedings of the 1996 Clinic on Library Applications of Data Processing, 24-26 Mar 1996. Ed.: P.B. Heidorn u. B. Sandore

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Abstract

Describes experiments in the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since their words as well as contents are unknown. Addresses this problem, for a video mail application, by combining state of the art speech recognition with established document retrieval technologies so as to provide an effective and efficient retrieval tool. Tests with a small spoken message collection show that retrieval precision for the spoken file can reach 90% of that obtained when the same file is used, as a benchmark, in text transcription form

Footnote

Wiederabdruck in: Readings in informatio retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.493-502.

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Abstract

In contrast to traditional information retrieval systems, which return ranked lists of documents that users must manually browse through, a question answering system attempts to directly answer natural language questions posed by the user. Although such systems possess language-processing capabilities, they still rely on traditional document retrieval techniques to generate an initial candidate set of documents. In this article, the authors argue that document retrieval for question answering represents a task different from retrieving documents in response to more general retrospective information needs. Thus, to guide future system development, specialized question answering test collections must be constructed. They show that the current evaluation resources have major shortcomings; to remedy the situation, they have manually created a small, reusable question answering test collection for research purposes. In this article they describe their methodology for building this test collection and discuss issues they encountered regarding the notion of "answer correctness."

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Abstract

The standard method for making the full content of audio and video material searchable is to annotate it with human-generated meta-data that describes the content in a way that search can understand, as is done in the creation of multimedia CD-ROMs. However, for the huge amounts of data that could usefully be included in digital video and audio libraries, the cost of producing the meta-data is prohibitive. In the Informedia Digital Video Library, the production of the meta-data supporting the library interface is automated using techniques derived from artificial intelligence (AI) research. By applying speech recognition together with natural language processing, information retrieval, and image analysis, an interface has been prduced that helps users locate the information they want, and navigate or browse the digital video library more effectively. Specific interface components include automatc titles, filmstrips, video skims, word location marking, and representative frames for shots. Both the user interface and the information retrieval engine within Informedia are designed for use with automatically derived meta-data, much of which depends on speech recognition for its production. Some experimental information retrieval results will be given, supporting a basic premise of the Informedia project: That speech recognition generated transcripts can make multimedia material searchable. The Informedia project emphasizes the integration of speech recognition, image processing, natural language processing, and information retrieval to compensate for deficiencies in these individual technologies

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Abstract

Natrual language document retrieval queries from the Thomas Cooper Library, South Carolina Univ. were analysed in oder to investigate the frequency of various types of ill-formed input, such as spelling errors, cooccurrence violations, conjunctions, ellipsis, and missing or incorrect punctuation. Users were requested to write out their requests for information in complete sentences on the form normally used by the library. The primary reason for analysing ill-formed inputs was to determine whether there is a significant need to study ill-formed inputs in detail. Results indicated that most of the queries were sentence fragments and that many of them contained some type of ill-formed input. Conjunctions caused the most problems. The next most serious problem was caused by punctuation errors. Spelling errors occured in a small number of queries. The remaining types of ill-formed input considered, allipsis and cooccurrence violations, were not found in the queries

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Abstract

Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 an the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.

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Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Source

Knowledge organization. 29(2002) nos.3/4, S.135-155