One of the essential functions of natural language is to talk about spatial relationships between objects. Linguistic constructs can express highly complex, relational structures of objects, spatial relations between them, and patterns of motion through space relative to some reference point. Understanding such spatial utterances is a problem in many areas, including robotics, navigation, traffic management, and query answering systems. Learning how to map this information onto a formal representation from text is a challenging problem. The task of spatial role labeling introduces an annotation scheme proposed that is language-independent and facilitates the application of machine learning techniques. The framework consists of a set of spatial roles based on the theory of holistic spatial semantics with the intent of covering all aspects of spatial concepts, including both static and dynamic spatial relations. Let us illustrate the spatial role labeling task with the following example:
For the sentence:

Give me the gray book on the big table.

Spatial role labeling results in the following output:

Give me [the gray book]TRAJECTOR [on]SPATIAL_INDICATOR [the big table]LANDMARK.

The phrase headed by the token book is referring to a trajector object, the phrase headed by the token table is referring to the role of a landmark and these are related by the spatial expression on denoted as spatial indicator. The spatial indicator (often a preposition) establishes the type of spatial relation.


There are various datasets created related to this task according to two schemes, the spatial role labeling project has been started in KU Leuven university and the data specifically created for it and used in Semantic Evaluation Campaigns in 2012 and 2013: Related datasets: Question answering datasets:


We are re-implementing all SpRL models in a unified programming framework which makes changing, extending the models and re-producing the results easier. The models are programmed in Saul (HetSaul is a modified mirror). For detailed descriptions of each model see the code and the documentation in SpRL as a part of Saul project.



First International Workshop on Spatial Language Understanding (SpLU-2018). In conjunction with The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), June 6, 2018, New Orleans, Louisiana, USA.

Past workshops:


This web demo receives natural language sentences in English and annotates spatial roles and relations. (under construction)


Kordjamshidi, P., Moens, M., van Otterlo, M., (2010). Spatial Role Labeling: Task Definition and Annotation Scheme, LREC'10.

Kordjamshidi, P., van Otterlo, M., Moens, M. (2011). Spatial role labeling: Towards extraction of spatial relations from natural language. ACM Transactions on Speech and Language Processing, 8(3), 4-36.

Kordjamshidi, P., Moens, M. (2015). Global machine learning for spatial ontology population. Journal of Web Semantics, 30, 3-21. Download

Kordjamshidi, P., van Otterlo, M., Moens, M. (2015). Spatial role labeling annotation scheme. In: Pustejovsky J., Ide N. (Eds.), Handbook of Linguistic Annotation Springer Verlag. Download

Parisa Kordjamshidi