research-article

Spatial role labeling with convolutional neural networks

Authors:

Alexey Mazalov,

David MatosAuthors Info & Claims

GIR '15: Proceedings of the 9th Workshop on Geographic Information Retrieval

Article No.: 12, Pages 1 - 7

https://doi.org/10.1145/2837689.2837706

Published: 26 November 2015 Publication History

Abstract

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.

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Cited By

Bonic SBonic JSchmid S(2024)Broomrocket: Open Source Text-to-3D Algorithm for 3D Object PlacementGames: Research and Practice10.1145/36482332:3(1-16)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1145/3648233
Jeon WYoon HAhn C(2023)Storm-based Real-Time Analysis System Design and Development for Spatial Information ExtractionJournal of Digital Contents Society10.9728/dcs.2023.24.1.7924:1(79-89)Online publication date: 31-Jan-2023
https://doi.org/10.9728/dcs.2023.24.1.79
Radke MGupta AStock KJones C(2022)Disambiguating spatial prepositions: The case of geo‐spatial sense detectionTransactions in GIS10.1111/tgis.1297626:6(2621-2650)Online publication date: 6-Sep-2022
https://doi.org/10.1111/tgis.12976
Show More Cited By

Index Terms

Spatial role labeling with convolutional neural networks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Other conferences

GIR '15: Proceedings of the 9th Workshop on Geographic Information Retrieval

November 2015

90 pages

ISBN:9781450339377

DOI:10.1145/2837689

Editors:
Ross S. Purves
University of Zurich
,
Christopher B. Jones
Cardiff University

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 November 2015

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Fundação para a Ciência e a Tecnologia

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GIR '15

GIR '15: 9th Workshop on Geographic Information Retrieval

November 26 - 27, 2015

Paris, France

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Overall Acceptance Rate 46 of 61 submissions, 75%

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Cited By

Bonic SBonic JSchmid S(2024)Broomrocket: Open Source Text-to-3D Algorithm for 3D Object PlacementGames: Research and Practice10.1145/36482332:3(1-16)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1145/3648233
Jeon WYoon HAhn C(2023)Storm-based Real-Time Analysis System Design and Development for Spatial Information ExtractionJournal of Digital Contents Society10.9728/dcs.2023.24.1.7924:1(79-89)Online publication date: 31-Jan-2023
https://doi.org/10.9728/dcs.2023.24.1.79
Radke MGupta AStock KJones C(2022)Disambiguating spatial prepositions: The case of geo‐spatial sense detectionTransactions in GIS10.1111/tgis.1297626:6(2621-2650)Online publication date: 6-Sep-2022
https://doi.org/10.1111/tgis.12976
Stock KJones CTenbrink T(2022)Speaking of location: a review of spatial language researchSpatial Cognition & Computation10.1080/13875868.2022.209527522:3-4(185-224)Online publication date: 19-Jul-2022
https://doi.org/10.1080/13875868.2022.2095275
Zeng ZDeng YLi XNaumann TLuo Y(2019)Natural Language Processing for EHR-Based Computational PhenotypingIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2018.284996816:1(139-153)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TCBB.2018.2849968
Ramrakhiyani NPalshikar GVarma V(2019)A Simple Neural Approach to Spatial Role LabellingAdvances in Information Retrieval10.1007/978-3-030-15719-7_13(102-108)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-3-030-15719-7_13
Liang HSun XSun YGao Y(2017)Text feature extraction based on deep learning: a reviewEURASIP Journal on Wireless Communications and Networking10.1186/s13638-017-0993-12017:1Online publication date: 15-Dec-2017
https://doi.org/10.1186/s13638-017-0993-1

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