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  • × author_ss:"Fuketa, M."
  • × year_i:[2000 TO 2010}
  1. Rokaya, M.; Atlam, E.; Fuketa, M.; Dorji, T.C.; Aoe, J.-i.: Ranking of field association terms using Co-word analysis (2008) 0.00 
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    Abstract
    Information retrieval involves finding some desired information in a store of information or a database. In this paper, Co-word analysis will be used to achieve a ranking of a selected sample of FA terms. Based on this ranking a better arranging of search results can be achieved. Experimental results achieved using 41 MB of data (7660 documents) in the field of sports. The corpus was collected from CNN newspaper, sports field. This corpus was chosen to be distributed over 11 sub-fields of the field sports from the experimental results, the average precision increased by 18.3% after applying the proposed arranging scheme depending on the absolute frequency to count the terms weights, and the average precision increased by 17.2% after applying the proposed arranging scheme depending on a formula based on "TF*IDF" to count the terms weights.
    Type
    a
  2. Atlam, E.-S.; Morita, K.; Fuketa, M.; Aoe, J.-i.: ¬A new method for selecting English field association terms of compound words and its knowledge representation (2002) 0.00 
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    Abstract
    This paper presents a strategy for building a morphological machine dictionary of English that infers meaning of derivations by considering morphological affixes and their semantic classification. Derivations are grouped into a frame that is accessible to semantic stem and knowledge base. This paper also proposes an efficient method for selecting compound Field Association (FA) terms from a large pool of single FA terms for some specialized fields. For single FA terms, five levels of association are defined and two ranks are defined, based on stability and inheritance. About 85% of redundant compound FA terms can be removed effectively by using levels and ranks proposed in this paper. Recall averages of 60-80% are achieved, depending on the type of text. The proposed methods are applied to 22,000 relationships between verbs and nouns extracted from the large tagged corpus.
    Type
    a

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