TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles

• URL: http://arxiv.org/abs/2310.17802v1
• Date: Thu, 26 Oct 2023 22:23:38 GMT
• Title: TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles
• Authors: Sarah Alsayyahi and Riza Batista-Navarro
• Abstract summary: This paper presents a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated. We also propose a method for annotating all temporal relations -- including long-distance ones -- which automates the process. The result is a new dataset, the TIMELINE corpus, in which improved inter-annotator agreement was obtained.
• Score: 4.314956204483074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs. This paper aims to alleviate these issues by presenting a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated. Additionally, the scheme includes events even if they are not expressed as verbs (e.g., nominalised events). Furthermore, we propose a method for annotating all temporal relations -- including long-distance ones -- which automates the process, hence reducing time and manual effort on the part of annotators. The result is a new dataset, the TIMELINE corpus, in which improved inter-annotator agreement was obtained, in comparison with previously reported temporal relation datasets. We report the results of training and evaluating baseline temporal relation extraction models on the new corpus, and compare them with results obtained on the widely used MATRES corpus.

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