Abstract
Automatically extracting spatial information is a challenging novel task with many applications. We formalize it as an information extraction step required for a mapping from natural language to a formal spatial representation. Sentences may give rise to multiple spatial relations between words representing landmarks, trajectors and spatial indicators. Our contribution is to formulate the extraction task as a relational learning problem, for which we employ the recently introduced kLog framework. We discuss representational and modeling aspects, kLog’s flexibility in our task and we present current experimental results.
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Kordjamshidi, P., Frasconi, P., Van Otterlo, M., Moens, MF., De Raedt, L. (2012). Relational Learning for Spatial Relation Extraction from Natural Language. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_20
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DOI: https://doi.org/10.1007/978-3-642-31951-8_20
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