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Multimodal Spatial Role Labeling @ CLEF 2017

September 11 - September 14

The task of spatial role labeling (SpRL)[1] formalizes the spatial concepts and relations in the language to be mapped to qualitative spatial representation using machine learning.

The main goal of multi-modal SpRL is to explore the extraction of spatial information from two information resources that is image and text. This is important for various applications such as semantic search, question answering, geographical information systems and even in robotics for machine understanding of navigational instructions or instructions for grabbing and manipulating objects. It is also essential for some specific tasks such as text to scene conversion or vice-versa, scene understanding as well as general information retrieval tasks when using huge amount of available multimodal data from various resources. Moreover, there is an increasing interest in extraction of spatial information from medical images that are accompanied by natural language descriptions.


September 11
September 14


CLEF 2017
Dublin, Ireland