A Hybrid Approach for Spatial Information Extraction from Natural Language Text

Commonly spatial data are stored in the Geographic Database (GDB) which is the backbone of Geographic information System (GIS). Setting up such database is a tedious and cost task. Moreover, the overwhelming amount of spatial information, particularly in the textual data is growing continuously. Hence, efforts were devoted to extract such data convoyed in the text streams freely available. In this context, we address this issue by proposing a novel hybrid approach that combines several Natural Language Processing (NLP) techniques, rules, and gazetteers to extract spatial named entities and retrieve relationships amongst them.