Titlebook: Cross-Language Information Retrieval; Gregory Grefenstette Book 1998 Springer Science+Business Media New York 1998 corpus.database.filteri

北極人

Infinite Ergodic Transformations,ieve documents in other languages (as well as the original language). This is accomplished by a method that automatically constructs a multi-lingual semantic space using Latent Semantic Indexing (LSI). We present strong preliminary test results for our cross-language LSI (CL-LSI) method for a French

搖曳的微光

https://doi.org/10.1007/978-4-431-55172-0hen confronted with systems that rely on standardized language, such as MeSH, SNOMED, or ICD, and the special terms sets of systems such as HELP, INTERNIST-I/QMR, and DXplain. Indeed, the need to map natural language into appropriate special terms—as well as the need to map one system’s specialized

大喘氣

Weakly Wandering Sequences in Ergodic Theoryes referring to the same domain. The first version of the system has been developed to retrieve natural language lexical equivalents from sets of sublanguage texts in English and Italian; given the necessary lexical and morphological components it could be extended to cover other languages. The init

SPECT

散布

The Teesta River and Its Basin Area,ranslating queries, or even the entire textual database, from one language to another. The information that machine translation technology can provide to cross-language information retrieval has been more extensively explored at SYSTRAN. This chapter is a description of the implementation of SYSTRAN

phlegm

folliculitis

袋鼠

Utilization of Interface Potential,igned for monolingual evaluations. Our methodology, based on normative relevance assessments by expert users, is well suited for comparing the effect of different cross-language mapping techniques on filtering accuracy. By measuring the degradation introduced by the use of existing test collections,