Detecting and resolving spatial ambiguity in text using named entity extraction and self learning fuzzy logic techniques
This addresses spatial ambiguity in text for information extraction tasks, but appears incremental as it builds on existing techniques.
The paper tackles the problem of spatial ambiguity in text by proposing a method that combines named entity extraction with self-learning fuzzy logic techniques to detect and resolve spatial uncertainty, aiming to improve information extraction accuracy.
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