Grounding spatial named entities for information extraction and question answering

Grounding spatial named entities for information extraction and question answering Jochen L. Leidner Gail Sinclair School of InformaticsBonnie Webber University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW Scotland, UK jochen.leidner@ed.ac.uk, csincla1@inf.ed.ac.uk, bonnie@inf.ed.ac.uk Abstract The task of named entity annotation of unseen text has recently been successfully automated with near-human performance. But the full task involves more than annotation, i.e. identifying the scope of each (continuous) text span and its class (such as place name). It also involves grounding the named entity (i.e. establishing its denotation with respect to the world or a model). The latter aspect has so far been neglected. In this paper, we show how geo-spatial named entities can be grounded using geographic co- ordinates, and how the results can be visual- ized using off-the-shelf software. We use this to compare a “textual surrogate” of a newspa- per story, with a “visual surrogate” based on geographic coordinates. 1 Introduction The task of named entity annotation of unseen text has recently been successfully automated, achiev- ing near-human performance using machine learning (Zheng and Su, 2002). But many applications also re- quire grounding – i.e., associating each classified text span with a referent in the world or some model thereof. The current paper discusses spatial grounding of named entities that may be referentially ambiguous, using a min- imality heuristic that is informed by external geographic knowledge sources. We then apply these ideas to the cre- ation of “visual surrogates” for news articles. This paper is structured as follows: Section 2 discusses how spatial named entities can be grounded and how this interacts with their extraction and applications. Section 3 describes a geo-spatial resolution algorithm. Section 4 shows how maps can be automatically constructed from named-entity tagged newswire text using resolved place names, hence introducing a new, graphical document sur- rogate. Section 5 deals with the usefulness of grounded named entities for question answering. Section 6 presents some related work, and Section 7 concludes this paper. 2 Spatial Grounding Gazetteers are large lists of names of geographic entities, usually enriched with further information, such as their class (e.g., town, river, dam, etc.), their size, and their location (i.e. with respect to some relative or absolute coordinate system such as longitude and latitude). Appendix A identifies some publicly available sources. UN-LOCODE is the official gazetteer by the United Nations; it is also freely available from the UNECE Web site1 and contains more than 36 000 locations in 234 countries (UNECE, 1998). The Alexandria Gazetteer (Smith et al., 1996; Frew et al., 1998) is another database of geographical entities, including both their coor- dinates and relationships such as: in-state-of, in-province-of, in-county-of, in-country-of, in-region-of, part-of and formerly-known-as. To date, Named Entity Recognition (NER) has only used gazetteers as evidence that a text span could be some kind of place name (LOCATION), even though their finite nature makes lists of names of limited use for classifica- tion (Mikheev et al., 1999). Here we use them for spatial grounding – relating linguistic entities of subtype …

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