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Spatial role labeling: Towards extraction of spatial relations from natural language

Spatial role labeling: Towards extraction of spatial relations from natural language Spatial Role Labeling: Towards Extraction of Spatial Relations from Natural Language PARISA KORDJAMSHIDI, MARTIJN VAN OTTERLO, and MARIE-FRANCINE MOENS, Katholieke Universiteit Leuven, Belgium This article reports on the novel task of spatial role labeling in natural language text. It proposes machine learning methods to extract spatial roles and their relations. This work experiments with both a step-wise approach, where spatial prepositions are found and the related trajectors, and landmarks are then extracted, and a joint learning approach, where a spatial relation and its composing indicator, trajector, and landmark are classi ed collectively. Context-dependent learning techniques, such as a skip-chain conditional random eld, yield good results on the GUM-evaluation (Maptask) data and CLEF-IAPR TC-12 Image Benchmark. An extensive error analysis, including feature assessment, and a cross-domain evaluation pinpoint the main bottlenecks and avenues for future research. Categories and Subject Descriptors: I.2.7 [Arti cial Intelligence]: Natural Language Processing ” Language parsing and understanding; Text analysis; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing ”Linguistic processing General Terms: Experimentations, Languages Additional Key Words and Phrases: Semantic labeling, spatial relations, spatial information extraction ACM Reference Format: Kordjamshidi, P., van Otterlo, M., and Moens, M.-F. 2011. Spatial role labeling: Towards extraction of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Spatial role labeling: Towards extraction of spatial relations from natural language

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/2050104.2050105
Publisher site
See Article on Publisher Site

Abstract

Spatial Role Labeling: Towards Extraction of Spatial Relations from Natural Language PARISA KORDJAMSHIDI, MARTIJN VAN OTTERLO, and MARIE-FRANCINE MOENS, Katholieke Universiteit Leuven, Belgium This article reports on the novel task of spatial role labeling in natural language text. It proposes machine learning methods to extract spatial roles and their relations. This work experiments with both a step-wise approach, where spatial prepositions are found and the related trajectors, and landmarks are then extracted, and a joint learning approach, where a spatial relation and its composing indicator, trajector, and landmark are classi ed collectively. Context-dependent learning techniques, such as a skip-chain conditional random eld, yield good results on the GUM-evaluation (Maptask) data and CLEF-IAPR TC-12 Image Benchmark. An extensive error analysis, including feature assessment, and a cross-domain evaluation pinpoint the main bottlenecks and avenues for future research. Categories and Subject Descriptors: I.2.7 [Arti cial Intelligence]: Natural Language Processing ” Language parsing and understanding; Text analysis; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing ”Linguistic processing General Terms: Experimentations, Languages Additional Key Words and Phrases: Semantic labeling, spatial relations, spatial information extraction ACM Reference Format: Kordjamshidi, P., van Otterlo, M., and Moens, M.-F. 2011. Spatial role labeling: Towards extraction of

Journal

ACM Transactions on Speech and Language Processing (TSLP)Association for Computing Machinery

Published: Dec 1, 2011

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