A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia

We target in this paper the challenge of extracting geospatial data from the article text of the English Wikipedia. We present the results of a Hidden Markov Model (HMM) based approach to identify location-related named entities in the our corpus of Wikipedia articles, which are primarily about battles and wars due to their high geospatial content. The HMM NER process drives a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name (often referred to as grounding). We compare our results to a previously developed data structure and algorithm for disambiguating place names that can have multiple coordinates. We demonstrate an overall f-measure of 79.63\% identifying and geocoding place names. Finally, we compare the results of the HMM-driven process to earlier work using a Support Vector Machine.

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