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'The First Day of Summer': Parsing Temporal Expressions with Distributed Semantics / Tom, Crick

Research and Development in Intelligent Systems XXX, Pages: 389 - 402

Swansea University Author: Tom, Crick

DOI (Published version): 10.1007/978-3-319-02621-3_29

Abstract

Detecting and understanding temporal expressions are key tasks in natural language processing (NLP), and are important for event detection and information retrieval. In the existing approaches, temporal semantics are typically represented as discrete ranges or specific dates, and the task is restric...

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Published in: Research and Development in Intelligent Systems XXX
ISBN: 978-3-319-02620-6 978-3-319-02621-3
Published: Cambridge, UK Springer 2013
Online Access: https://link.springer.com/chapter/10.1007%2F978-3-319-02621-3_29
URI: https://cronfa.swan.ac.uk/Record/cronfa43777
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spelling 2018-10-23T17:03:22.1206085 v2 43777 2018-09-12 &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2018-09-12 EDUC Detecting and understanding temporal expressions are key tasks in natural language processing (NLP), and are important for event detection and information retrieval. In the existing approaches, temporal semantics are typically represented as discrete ranges or specific dates, and the task is restricted to text that conforms to this representation. We propose an alternate paradigm: that of distributed temporal semantics—where a probability density function models relative probabilities of the various interpretations. We extend SUTime, a state-of-the-art NLP system to incorporate our approach, and build definitions of new and existing temporal expressions. A worked example is used to demonstrate our approach: the estimation of the creation time of photos in online social networks (OSNs), with a brief discussion of how the proposed paradigm relates to the point- and interval-based systems of time. An interactive demonstration, along with source code and datasets, are available online. Book chapter Research and Development in Intelligent Systems XXX 389 402 Springer Cambridge, UK 978-3-319-02620-6 978-3-319-02621-3 10 12 2013 2013-12-10 10.1007/978-3-319-02621-3_29 https://link.springer.com/chapter/10.1007%2F978-3-319-02621-3_29 33rd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2013) COLLEGE NANME School of Education COLLEGE CODE EDUC Swansea University 2018-10-23T17:03:22.1206085 2018-09-12T06:33:09.3960093 College of Arts and Humanities College of Arts and Humanities Ben Blamey 1 Tom Crick 0000-0001-5196-9389 2 Giles Oatley 3 0043777-12092018063433.pdf blamey-et-al-2013.pdf 2018-09-12T06:34:33.1370000 Output 180484 application/pdf Accepted Manuscript true 2018-09-12T00:00:00.0000000 true eng
title &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
spellingShingle &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
Tom, Crick
title_short &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
title_full &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
title_fullStr &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
title_full_unstemmed &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
title_sort &apos;The First Day of Summer&apos;: Parsing Temporal Expressions with Distributed Semantics
author_id_str_mv 200c66ef0fc55391f736f6e926fb4b99
author_id_fullname_str_mv 200c66ef0fc55391f736f6e926fb4b99_***_Tom, Crick
author Tom, Crick
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description Detecting and understanding temporal expressions are key tasks in natural language processing (NLP), and are important for event detection and information retrieval. In the existing approaches, temporal semantics are typically represented as discrete ranges or specific dates, and the task is restricted to text that conforms to this representation. We propose an alternate paradigm: that of distributed temporal semantics—where a probability density function models relative probabilities of the various interpretations. We extend SUTime, a state-of-the-art NLP system to incorporate our approach, and build definitions of new and existing temporal expressions. A worked example is used to demonstrate our approach: the estimation of the creation time of photos in online social networks (OSNs), with a brief discussion of how the proposed paradigm relates to the point- and interval-based systems of time. An interactive demonstration, along with source code and datasets, are available online.
published_date 2013-12-10T03:58:42Z
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