@inproceedings{ulinski-etal-2019-spatialnet,
title = "{S}patial{N}et: A Declarative Resource for Spatial Relations",
author = "Ulinski, Morgan and
Coyne, Bob and
Hirschberg, Julia",
editor = "Bhatia, Archna and
Bisk, Yonatan and
Kordjamshidi, Parisa and
Thomason, Jesse",
booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1607",
doi = "10.18653/v1/W19-1607",
pages = "61--70",
abstract = "This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.",
}
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%0 Conference Proceedings
%T SpatialNet: A Declarative Resource for Spatial Relations
%A Ulinski, Morgan
%A Coyne, Bob
%A Hirschberg, Julia
%Y Bhatia, Archna
%Y Bisk, Yonatan
%Y Kordjamshidi, Parisa
%Y Thomason, Jesse
%S Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ulinski-etal-2019-spatialnet
%X This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.
%R 10.18653/v1/W19-1607
%U https://aclanthology.org/W19-1607
%U https://doi.org/10.18653/v1/W19-1607
%P 61-70
Markdown (Informal)
[SpatialNet: A Declarative Resource for Spatial Relations](https://aclanthology.org/W19-1607) (Ulinski et al., RoboNLP 2019)
ACL
- Morgan Ulinski, Bob Coyne, and Julia Hirschberg. 2019. SpatialNet: A Declarative Resource for Spatial Relations. In Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP), pages 61–70, Minneapolis, Minnesota. Association for Computational Linguistics.