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  • × author_ss:"Evens, M.W."
  • × theme_ss:"Computerlinguistik"
  1. Lee, Y.-H.; Evens, M.W.: Natural language interface for an expert system (1998) 0.00 
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    Abstract
    Presents a complete analysis of the underlying principles of natural language interfaces from the screen manager to the parser / understander. The main focus is on the design and development of a subsystem for understanding natural language input in an expert system. Considers that fast response time and user friendliness are the most important considerations in the design. The screen manager provides an easy editing capability for users and the spelling correction system can detect most spelling errors and correct them automatically, quickly and effectively. The Lexical Functional Grammar (LFG) parser and the understander are designed to handle most types of simple sentences, fragments, and ellipses
    Type
    a
  2. Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999) 0.00 
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    Abstract
    Stemming is one of the most important factors that affect the performance of information retrieval systems. This article investigates how to improve the performance of an Arabic information retrieval system by imposing the retrieval method over individual words of a query depending on the importance of the WORD, the STEM, or the ROOT of the query terms in the database. This method, called Mxed Stemming, computes term importance using a weighting scheme that use the Term Frequency (TF) and the Inverse Document Frequency (IDF), called TFxIDF. An extended version of the Arabic IRS system is designed, implemented, and evaluated to reduce the number of irrelevant documents retrieved. The results of the experiment suggest that the proposed method outperforms the Word index method using the TFxIDF weighting scheme. It also outperforms the Stem index method using the Binary weighting scheme but does not outperform the Stem index method using the TFxIDF weighting scheme, and again it outperforms the Root index method using the Binary weighting scheme but does not outperform the Root index method using the TFxIDF weighting scheme
    Type
    a

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