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Leveraging the foundation built in the prior workshops SPLU-RoboNLP 2019 and SpLU-2018 and focusing on the gaps identified therein, we propose the third workshop on Spatial Language Understanding. One of the essential functions of natural language is to express spatial relationships between objects. Spatial language understanding is useful in many research areas and real-world applications including robotics, navigation, geographic information systems, traffic management, human-machine interaction, query an

Spatial language meaning representation includes research related to cognitive and linguistically motivated spatial semantic representations, spatial knowledge representation and spatial ontologies, qualitative and quantitative representation models used for formal meaning representation, spatial annotation schemes and efforts for creating specialized corpora. Spatial language learning considers both symbolic and sub-symbolic (with continuous representations) techniques and computational models for spatial

The specific topics include but are not limited to:

Links to spatial-language.github.io (7)



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