Search (3 results, page 1 of 1)

  • × author_ss:"Liddy, E.D."
  • × theme_ss:"Retrievalalgorithmen"
  1. Liddy, E.D.; Diamond, T.; McKenna, M.: DR-LINK in TIPSTER (2000) 0.00 
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    Type
    a
  2. Liddy, E.D.; Paik, W.; McKenna, M.; Yu, E.S.: ¬A natural language text retrieval system with relevance feedback (1995) 0.00 
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    Abstract
    Outlines a fully integrated retrieval engine that processes documents and queries at the multiple, complex linguistic levels that humans use to construe meaning. Currently undergoing beta site trials, the DR-LINK natural language text retrieval system allows searchers to state queries as fully formed, natural sentences. The meaning and matching of both queries and documents is accomplished at the conceptual level of human expression, not by the simple concurrence of keywords. Furthermore, the natural browsing behaviour of information searchers is accomodated by allowing documents identified as potentially relevant by the explicit semantics of the system to be used as relevance feedback queries which provide an appropriate implicit semantic representation of the information seeker's need
    Type
    a
  3. Liddy, E.D.: ¬An alternative representation for documents and queries (1993) 0.00 
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    Abstract
    Describes an alternative method of representation for documents and queries in information retrieval systems to the 2 most common methods: free text, natural language representation and controlled language representation. The alternative method combines the advantage of both traditional approaches and overcomes the difficulties associated with each. The scheme was developed for use with Longman's Dictionary of Contemporary English and uses a computerized version of the dictionary for the automatic generation of summary level semantic representations of each document and query. The system tags each word in a document with the appropriate Subject Field Code (SFC) from the dictionary and the SFCs are summed and normalized to produce a weighted, fixed length vector of the SFC. The search system matches the query SFC vector to the document SFC vectors in the database. The documents are then ranked on the basis of their vector's similarity to the query
    Type
    a

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