Extracting Addresses from News Reports Using Conditional Random Fields

Spatial analysis in many fields requires effective address extraction from text reports. This problem is of particular importance in social science where news reports contain information about socially relevant incidents. Previous address extraction work focuses on web pages where addresses are separated from other text, however news reports contain addresses embedded in text. Hence, the need for different methods. This paper describes and compares three supervised learning approaches and one semi-supervised learning approach to automatically extract street addresses from news reports. Experimental results with actual news reports show performance close to that achieved for web pages and some lift in accuracy from the semi-supervised approach. These results also show that different news sources produce different outcomes.