Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques

📝 Original Info

• Title: Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques
• ArXiv ID: 1303.0445
• Date: 2013-03-05
• Authors: Researchers from original ArXiv paper

📝 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

💡 Deep Analysis

Deep Dive into Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques.

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

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