Spatial role labeling with convolutional neural networks
Alexey Mazalov, Bruno Martins, David Martins de Matos · 2015
Many natural language processing applications require information about the spatial locations of objects referenced in text, or spatial relations between these objects in space. For example, the phrase a book on the shelf contains information about the location of the object book, corresponding to a trajector, with respect to the object shelf, which in turn corresponds to a landmark. Spatial role labeling concerns with the task of automatically processing textual sentences and identifying objects of spatial scenes and relations between them. In this paper, we describe the application of modern machine learning methods to extract spatial roles and their relations, specifically by adapting a pre-existing system based on a convolutional neural network architecture that has been recently proposed for the more general task of semantic role labeling. We report on experiments with datasets from the SemEval challenges on spatial role labeling, showing that our method can achieve results in line with the current state-of-the-art. We therefore argue that that spatial role labeling can leverage on recent developments in semantic role labeling, requiring only minimal adaptations.