Antje Müller (Bochum): The annotation of spatial preposition senses in German

Prepositions are considered to be polysemous, they denote spatial or temporal relations among others (1).

Notably, prepositions cannot only be polysemous between different semantic classes like the aforementioned. The term “spatial” denotes a set of rather different spatial relations (as e.g. inclusion, coverage, proximity, traversal), which in many cases are instantiated by one and the same preposition, as is illustrated in (2) for a traversal, a vertical, and a covering sense – which give rise to different spatial inferences. It is thus not sufficient – or at least not helpful – to classify a preposition simply as spatial.

While spatial prepositions and their interpretations have received lots of interest in theoretical syntax and semantics, an annotation scheme for spatial preposition senses in German was not available until recently (cf. Müller et al. 2011, Müller 2012). More generally, the task of developing annotation schemata for preposition senses is often discarded after initial drawbacks, because the effort required for the cyclic development of an annotation schema is underestimated (cf. Pustejovsky and Stubbs 2012). In this talk I am going to present an annotation scheme for spatial preposition senses for a limited subset of German prepositions. I do not only focus on the differentiation of spatial senses of one preposition but also consider relations between connected senses of different prepositions.

• Müller, A., Roch, C., Stadtfeld, T., Kiss, T. (2011). Annotating Spatial Interpretations of German Prepositions. In: O'Conner, Lisa (Ed.): 2011 Fifth IEEE International Conference on Semantic Computing, 459 - 466. Stanford, CA.
• Müller, A. (2012). Location and Path – Annotating Senses of the German Prepositions auf and über. Proceedings of the Joint ISA-7 Workshop on Interoperable Semantic Annotation SRSL-3 Workshop on  Semantic Representation for Spoken Language I2MRT Workshop on Multimodal Resources and Tools,  LREC 2012.
• Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning. Sebastopol: O'Reilly Media Inc.