Abstract
Spatial Role Labelling involves identification of text segments which emit spatial semantics such as describing an object of interest, a reference point or the object’s relative position with the reference. Tasks in SemEval exercises of 2012 and 2013 propose problems and datasets for Spatial Role Labelling. In this paper, we propose a simple two-step neural network based approach to identify static spatial relations along with the three primary roles - Trajector, Landmark and Spatial Indicator. Our approach outperforms the task submission results and other state-of-the-art results on these datasets. We also include a discussion on the explainability of our model.
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Ramrakhiyani, N., Palshikar, G., Varma, V. (2019). A Simple Neural Approach to Spatial Role Labelling. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_13
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DOI: https://doi.org/10.1007/978-3-030-15719-7_13
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