Cross-domain textual geocoding: influence of domain-specific training data
by Mike Kolkman
Modern technology is more and more able to understand natural language. To do so, unstructured texts need to be analysed and structured. One such structuring method is geocoding, which is aimed at recognizing and disambiguating references to geographical locations in text. These locations can be countries and cities, but also streets and buildings, or even rivers and lakes. A word or phrase that refers to a location is called a toponym. Approaches to tackle the geocoding task mainly use natural language processing techniques and machine learning. The difficulty of the geocoding task is dependent of multiple aspects, one of which is the data domain. The domain of a text describes the type of the text, like its goal, degree of formality, and target audience. When texts come from two (or more) different domains, like a Twitter post and a news item, they are said to be cross-domain.
An analysis of baseline geocoding systems shows that identifying toponyms on cross-domain data has still room for improvement, as existing systems depend significantly on domain-specific metadata. Systems focused on Twitter data are often dependent on account information of the author and other Twitter specific metadata. This causes the performance of these systems to drop significantly when applied on news item data.
This thesis presents a geocoding system, called XD-Geocoder, aimed at robust cross-domain performance by using text-based and lookup list based features only. Such a lookup list is called a gazetteer and contains a vast amount of geographical locations and information about these locations. Features are built up using word shape, part-of-speech tags, dictionaries and gazetteers. The features are used to train SVM and CRF classifiers.
Both classifiers are trained and evaluated on three corpora from three domains: Twitter posts, news items and historical documents. These evaluations show Twitter data to be the best for training out of the tested data sets, because both classifiers show the best overall performance when trained on tweets. However, this good performance might also be caused by the relatively high toponym to word ratio in the used Twitter data.
Furthermore, the XD-Geocoder was compared to existing geocoding systems. Although the XD-Geocoder is outperformed by state-of-the-art geocoders on single-domain evaluations (trained and evaluated on data from the same domain), it outperforms the baseline systems on cross-domain evaluations.