Supporting web-based address extraction with unsupervised tagging

The manual acquisition and modeling of tourist information as e.g. addresses of points of interest is time and, therefore, cost intensive. Furthermore, the encoded information is static and has to be refined for newly emerging sight seeing objects, restaurants or hotels. Automatic acquisition can support and enhance the manual acquisition and can be implemented as a run-time approach to obtain information not encoded in the data or knowledge base of a tourist information system. In our work we apply unsupervised learning to the challenge of web-based address extraction from plain text data extracted from web pages dealing with locations and containing the addresses of those. The data is processed by an unsupervised part-of-speech tagger (Biemann, 2006a), which constructs domain-specific categories via distributional similarity of stop word contexts and neighboring content words. In the address domain, separate tags for street names, locations and other address parts can be observed. To extract the addresses, we apply a Conditional Random Field (CRF) on a labeled training set of addresses, using the unsupervised tags as features. Evaluation on a gold standard of correctly annotated data shows that unsupervised learning combined with state of the art machine learning is a viable approach to support web-based information extraction, as it results in improved extraction quality as compared to omitting the unsupervised tagger.