Toponym resolution in text (abstract only): "which sheffield is it?"

ABSTRACT

Named entity tagging comprises the sub-tasks of identifying a text span and classifying it, but this view ignores the relationship between the entities and the world. Spatial and temporal entities ground events in space-time, and this relationship is vital for applications such as question answering and event tracking. There is much recent work regarding the temporal dimension (Setzer and Gaizauskas 2002, Mani and Wilson 2000), but no detailed study of the spatial dimension.I propose to investigate how spatial named entities (which are often referentially ambiguous) can be automatically resolved with respect to an extensional coordinate model (toponym resolution). To this end, various information sources including linguistic cue patterns, co-occurrence information, discourse/positional information, world knowledge (such as size and population) as well as minimality heuristics (Leidner et al. 2003) will be combined in a supervised machine learning regime.The major contributions of this research project will be a corpus of text manually annotated for spatial named entities with their model correlates as a training and evaluation resource, a novel method to spatially ground toponyms in text and a component-based evaluation based on this new reference corpus.