Integer-Programming Ensemble of Temporal-Relations Classifiers
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|Title:||Integer-Programming Ensemble of Temporal-Relations Classifiers||Authors:||Kerr, Catherine; Hoare, Terri; Carroll, Paula; Mareček, Jakub||Permanent link:||http://hdl.handle.net/10197/11517||Date:||2-Jan-2020||Online since:||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.||Funding Details:||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||Keywords:||Natural language processing; Temporal reasoning; Ensemble methods; Integer programming||DOI:||10.1007/s10618-019-00671-x||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Business Research Collection|
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