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.

Relational Learning for Spatial Relation Extraction from Natural Language / Parisa Kordjamshidi; Paolo Frasconi; Martijn Otterlo; Marie-Francine Moens;Luc De Raedt. - STAMPA. - 7207:(2012), pp. 204-220. (Intervento presentato al convegno Inductive Logic Programming) [10.1007/978-3-642-31951-8_20].

Relational Learning for Spatial Relation Extraction from Natural Language

FRASCONI, PAOLO;
2012

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.
2012
Inductive Logic Programming
Inductive Logic Programming
Parisa Kordjamshidi; Paolo Frasconi; Martijn Otterlo; Marie-Francine Moens;Luc De Raedt
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/790977
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