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  5. Integer-Programming Ensemble of Temporal-Relations Classifiers
 
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Integer-Programming Ensemble of Temporal-Relations Classifiers

Author(s)
Kerr, Catherine  
Hoare, Terri  
Carroll, Paula  
Mareček, Jakub  
Uri
http://hdl.handle.net/10197/11517
Date Issued
2020-01-02
Date Available
2020-08-28T15:51:01Z
Abstract
The extraction of temporal events from text and the classification of temporal relations among both temporal events and time expressions are major challenges for the interface of data mining and natural language processing. We present an ensemble method, which reconciles the outputs of multiple heterogenous classifiers of temporal expressions. We use integer programming, a constrained optimisation technique, to improve on the best result of any individual classifier by choosing consistent temporal relations from among those recommended by multiple classifiers. Our ensemble method is conceptually simple and empirically powerful. It allows us to encode knowledge about the structure of valid temporal expressions as a set of constraints. It obtains new state-of-the-art results on two recent natural language processing challenges, SemEval-2013 TempEval-3 (Temporal Annotation) and SemEval-2016 Task 12 (Clinical TempEval), with F1 scores of 0.3915 and 0.595 respectively.
Sponsorship
European Commission Horizon 2020
Type of Material
Journal Article
Publisher
Springer
Journal
Data Mining and Knowledge Discovery
Volume
34
Start Page
533
End Page
562
Copyright (Published Version)
2020 the Authors
Subjects

Natural language proc...

Temporal reasoning

Ensemble methods

Integer programming

DOI
10.1007/s10618-019-00671-x
Language
English
Status of Item
Peer reviewed
ISSN
1384-5810
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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Name

IP_Ensemble_TemporalRelationClassifiers_DMKD_Accepted_Revision.pdf

Size

602.83 KB

Format

Adobe PDF

Checksum (MD5)

a410d62f9d207c1c6249ce06b7b89af0

Owning collection
Business Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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