Article Free Access Share on New techniques for open-vocabulary spoken document retrieval Authors: Martin Wechsler Swiss Federal Institute of Technology (ETH), Zürich Swiss Federal Institute of Technology (ETH), ZürichView Profile , Eugen Munteanu Swiss Federal Institute of Technology (ETH), Zürich Swiss Federal Institute of Technology (ETH), ZürichView Profile , Peter Schäuble Swiss Federal Institute of Technology (ETH), Zürich Swiss Federal Institute of Technology (ETH), ZürichView Profile Authors Info & Claims SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrievalAugust 1998Pages 20–27https://doi.org/10.1145/290941.290950Published:01 August 1998Publication History 25citation640DownloadsMetricsTotal Citations25Total Downloads640Last 12 Months37Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteeReaderPDF

We present here the realisation of cross-language speech retrieval system which retrieves German speech documents in response to user queries specified as French text. This has been achieved through the integration of two existing modules of the SPIDER information retrieval system, namely the query pseudo-translation module and the speech retrieval module. In the process of integrating these modules for cross-language speech retrieval, we have also made improvements to each in turn. We have progressed our research on aligning comparable corpora, an important resource for training the similarity thesauri used in our query pseudo-translation module, and we have now encorporated an automatic refinement loop into our speech retrieval module, using a probabilistic matching approach that has been proven effective in retrieving error-prone documents. We have also introduced the use of automatic relevance feedback for query expansion and tested this both as a refinement to automatically pseudo...

We report on a probabilistic weighting approach to indexing the scanned images of very short documents.This fully automatic process copes with short and very noisy texts (67% word accuracy)derived from the images by Optical Character Recognition (OCR).The probabilistic term weighting

Two main topics are addressed. First, an algebraic approach is presented to define a general notion of expressive power. Heterogeneous algebras represent information systems and morphisms represent the correspondences between the instances of databases, the correspondences between answers, and the correspondences between queries. An important feature of this new notion of expressive power is that query languages of different types can be compared with respect to their expressive power. In the case of relational query languages, the new notion of expressive power is shown to be equivalent to the notion used by Chandra and Harel. In the case of nonrelational query languages, the versatility of the new notion of expressive power is demonstrated by comparing the fixpoint query languages with an object-oriented query language called FQL. The expressive power of the Functional Query Language FQL is the second main topic of this paper. The specifications of FQL functions can be recursive or even mutually recursive, FQL has a fixpoint semantics based on a complete lattice consisting of bag functions. The query language FQL is shown to be more expressive than the fixpoint query languages. This result implies that FQL is also more expressive than Datalog with stratified negation. Examples of recursive FQL functions are given that determine the ancestors of persons and the bill of materials.

The access structure, the retrieval model, and the system architecture of the SPIDER information retrieval system are described. The access structure provides efficient weighted retrieval on dynamic data collections. It is based on signatures and non-inverted item descriptions. The signatures provide upper bounds for the exact retrieval status values such that only a small number of exact retrieval status values have to be computed. SPIDER's retrieval model is a probabilistic retrieval model that is capable to exploit the database scheme of semistructured data collections. This model can be considred as a further development of the Binary Independence Indexing (BII) model. The system architecture was derived systematically from a given set of requirements such as effective and efficient retrieval on dynamic data collections, exploitation of the database scheme, computed views, and the integration of information retrieval functionality and database functionality.

An information retrieval model is presented for the retrieval of speech documents, i.e. audio recordings containing speech. The indexing vocabulary consists of indexing features that have the following characteristics. First, they are easy to recognize by speech recognition methods. Second, the number of different indexing features is small such that a reasonable amount of training data is sufficent to train the hidden Markov models that are used by the speech recognition process. Third, the retrieval method based on such indexing features achieves an acceptable retrieval effectiveness as shown by experiments on text collections. Fourth, these indexing features cannot only be identified in speech documents but also in text documents. From the last characteristic follows that speech documents and text documents can be retrieved simultaneously. Analogously, the queries may contain either speech or text. Thus, we have a simple multimedia retrieval model where two different medias are indexed coherently. We also describe a prototype retrieval system under development.

An information structure (IS) that is regarded as a formal description of a domain of discourse is proposed. This IS is aimed at increasing the effectiveness of an information retrieval system. It is shown how the retrieval algorithm can take into account the term dependencies that are provided by the IS. Moreover, these term dependencies can be used by an automatic indexing procedure in order to interpret polysemic terms. The theoretical framework of our IS has some favorable properties. As a consequence, the construction and maintenance of such an IS is simpler than that of a thesaurus.

Article Free Access Share on Thesaurus based concept spaces Author: P. Schauble Institut für Informatik, Swiss Federal Institute of Technology, Zurich, Switzerland Institut für Informatik, Swiss Federal Institute of Technology, Zurich, SwitzerlandView Profile Authors Info & Claims SIGIR '87: Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrievalNovember 1987 Pages 254–262https://doi.org/10.1145/42005.42034Online:01 November 1987Publication History 7citation371DownloadsMetricsTotal Citations7Total Downloads371Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF