Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques
Abstract
Information extraction identifies useful and relevant text in a document and converts unstructured text into a form that can be loaded into a database table. Named entity extraction is a main task in the process of information extraction and is a classification problem in which words are assigned to one or more semantic classes or to a default non-entity class. A word which can belong to one or more classes and which has a level of uncertainty in it can be best handled by a self learning Fuzzy Logic Technique. This paper proposes a method for detecting the presence of spatial uncertainty in the text and dealing with spatial ambiguity using named entity extraction techniques coupled with self learning fuzzy logic techniques
Cite
@article{arxiv.1303.0445,
title = {Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques},
author = {Kanagavalli V R and Raja. K},
journal= {arXiv preprint arXiv:1303.0445},
year = {2013}
}
Comments
National Conference on Recent Trends in Data Mining and Distributed Systems September 2011