Xem 1-20 trên 499 kết quả Retrieving information
  • We present C*ST*RD, a cross-language information delivery system that supports cross-language information retrieval, information space visualization and navigation, machine translation, and text summarization of single documents and clusters of documents. C*ST*RD was assembled and trained within one month, in the context of DARPA's Surprise Language Exercise, that selected as source a heretofore unstudied language, Hindi. Given the brief time, we could not create deep Hindi capabilities for all the modules, but instead experimented with combining....

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  • Astronomers are the oldest data collectors. The first catalogue of stars is due to Hipparchus, in the second century B.C. Since that time, and more precisely since the end of the last century, there has been an important increase in astronomical data. EHie to the development of space astronomy during recent decades, we have witnessed a veritable inflation. Confronted with this flood of data, astronomers have to change their methodology. It is necessary not only to manage large databases, but also to take into account recent developments in information retrieval....

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  • In this chapter, the learning objectives are: Integrate separate REA diagrams for individual business cycles into a single, comprehensive organization-wide REA diagram; build a set of tables to implement an REA model of an AIS in a relational database; explain how to write queries to retrieve information from an AIS relational database built according to the REA data model.

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  • First the notion "paraphrase" is defined, and then several different types of paraphrase are analyzed: transformational, attenuated, lexical, derivational, and real-world. Next, several different methods of retrieving information are discussed utilizing the notions of paraphrase defined previously.

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  • This paper investigates an application of the ranked region algebra to information retrieval from large scale but unannotated documents. We automatically annotated documents with document structure and semantic tags by using taggers, and retrieve information by specifying structure represented by tags and words using ranked region algebra. We report in detail what kind of data can be retrieved in the experiments by this approach.

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  • Chapter 18 - Implementing an REA model in a relational database. In this chapter, the learning objectives are: Integrate separate REA diagrams for individual business cycles into a single, comprehensive organization-wide REA diagram; build a set of tables to implement an REA model of an AIS in a relational database; explain how to write queries to retrieve information from an AIS relational database built according to the REA data model.

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  • SQL For Dummies, 7th Edition, shows programmers and web developers how to use SQL to build relational databases and get valuable information from them. This book covers the core features of the SQL language. Topics covered include how to use SQL to structure a DBMS and implement a database design; secure a database; and retrieve information from a database. This edition will be revised to reflect the new enhancements of SQL/XML:2008.

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  • This research examines how differences in the organization of brand information in memory between higher and lower knowledge consumers affects which brands are retrieved when consumers are provided with a usage situation. A spreading activation network model of memory is used to predict the results of an experiment where the usage situations were varied at encoding and repeated recall sessions.

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  • We live in the Information Age. This term often alludes to the global economy's shift in focus away from the Industrial Age. The relatively recent field of information technology concerns the use of computer-based information systems to convert, store, protect, process, transmit and retrieve information. Technological advances in this field have changed lifestyles all over the world.

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  • [ Team LiB ] Recipe 0.2 Retrieving Database Schema Information from SQL Server Problem You need to retrieve database schema information from a SQL Server database.

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  • As digital libraries and the World Wide Web (WWW) continue to grow exponentially, the ability to find useful information will greatly depend on the associated underlying framework of the indexing infrastructure or search engine. The push to get information on-line must be mediated by the design of automated techniques for extracting that information for a variety of users and needs. What algorithms and software environments are plausible for achieving both accuracy and speed in text searching today?...

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  • This paper discusses research on distinguishing word meanings in the context of information retrieval systems. We conducted experiments with three sources of evidence for making these distinctions: morphology, part-of-speech, and phrases. We have focused on the distinction between h o m o n y m y and polysemy (unrelated vs. related meanings). Our results support the need to distinguish h o m o n y m y and p o l y semy. We found: 1) grouping morphological variants makes a significant improvement in retrieval performance, 2) that more than half of all words in a dictionary that differ...

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  • [ Team LiB ] Recipe 6.9 Retrieving Constraints from a SQL Server Database Problem You need to programmatically define constraints in a DataSet and retrieve constraint information defined in a SQL Server database. Solution Use the INFORMATION_SCHEMA views and SQL Server system tables to get information about primary keys, foreign keys, and check constraints.

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  • [ Tam LiB ] Recipe 10.3 Retrieving Column Default Values from SQL Server Problem The DataColumn object exposes a Default property. While the FillSchema( ) method of the DataAdapter returns schema information, it does not include the default values for columns.

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  • Biomedical engineering and medical informatics represent challenging and rapidly growing areas. Applications of information technology in these areas are of paramount importance.

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  • Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level. This modelling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted. We propose to use HMMs to model text at the segment level, in which the extraction process consists of two steps: a segment retrieval step followed by an extraction step. ...

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  • This paper proposes a method by which 5WlH (who, when, where, what, why, how, and predicate) information is used to classify and navigate Japaneselanguage texts. 5WlH information, extracted from text data, has an access platform with three functions: episodic retrieval, multi-dimensional classification, and overall classification. In a six-month trial, the platform was used by 50 people to access 6400 newspaper articles. The three functions proved to be effective for office documentation work and the precision of extraction was approximately 82%. ...

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  • Part 1 book "The Organization of information" presented to the reader the contents: Organization of recorded information, retrieval tools, development of the organization of recorded information in western civilization, metadata, encoding standards,... Invite your reference.

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  • Information retrieval (IR) and figurative language processing (FLP) could scarcely be more different in their treatment of language and meaning. IR views language as an open-ended set of mostly stable signs with which texts can be indexed and retrieved, focusing more on a text’s potential relevance than its potential meaning. In contrast, FLP views language as a system of unstable signs that can be used to talk about the world in creative new ways.

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  • The use of phrases in retrieval models has been proven to be helpful in the literature, but no particular research addresses the problem of discriminating phrases that are likely to degrade the retrieval performance from the ones that do not. In this paper, we present a retrieval framework that utilizes both words and phrases flexibly, followed by a general learning-to-rank method for learning the potential contribution of a phrase in retrieval.

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