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- W1819468558 abstract "Efficient document retrieval that answers a user query is achieved by indexing. The current technique uses word stems to index a document [1]. Such a technique suffers from the inability to match words in a query with their related words such as synonyms, hypernyms and hyponyms [2] in the documents. Therefore, there are recent attempts to index the document based on conceptual terms, however, the knowledge sources are usually incomplete. As a result, past research reveals that although using conceptual terms for document indexing can solve some of the problems, it cannot outperform the word-stem-based model [3, 4, 5, 6]. To remedy the deficiency of the knowledge sources, we propose a phrase-based indexing model where we parse a document into phrases based on the conceptual terms in domain specific knowledge sources, and calculate the similarity between two documents using both the similarity between the concepts and the common word stems in them. Including word stems in addition to concepts in document similarity evaluation compensates for the shortcoming of using concept terms alone caused by the incompleteness of the knowledge sources.KeywordsChronic Fatigue SyndromeKnowledge SourceQuery ExpansionVector Space ModelRetrieval AccuracyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1819468558 date "2004-01-01" @default.
- W1819468558 modified "2023-09-27" @default.
- W1819468558 title "Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining" @default.
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- W1819468558 doi "https://doi.org/10.1007/978-1-4613-0227-8_5" @default.
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