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When the text corpus is queried with a spatial-keyword, FERC returns a set of relevant documents sorted in view of the fuzzy pertinence score. Any two documents may be compared in light of the spatial references that exist in them and their fuzzy similarity score is presented. This enables finding the degree to which the two documents speak about a specified location. The proposed architecture provides a better result set to the user, unlike a Boolean search where the document is either rated relevant or irrelevant.<\/p>","DOI":"10.4018\/ijssmet.2020070101","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T13:26:43Z","timestamp":1591709203000},"page":"1-17","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient Methodology for Resolving Uncertain Spatial References in Text Documents"],"prefix":"10.4018","volume":"11","author":[{"family":"Raja K.","sequence":"first","affiliation":[{"name":"Dhaanish Ahmed College of Engineering, Chennai, India"}]},{"family":"Kanagavalli V. R.","sequence":"additional","affiliation":[{"name":"Sri Sai Ram Engineering College, Chennai, India"}]},{"family":"Nizar Banu P. 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