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Validation Methodology for Expert-Annotated Datasets: Event Annotation Case Study

Authors Oana Inel, Lora Aroyo

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ACM Subject Classification
  • Information systems → Crowdsourcing
  • Human-centered computing → Empirical studies in HCI
  • Computing methodologies → Machine learning
Keywords
  • Crowdsourcing
  • Human-in-the-Loop
  • Event Extraction
  • Time Extraction

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Abstract

Event detection is still a difficult task due to the complexity and the ambiguity of such entities. On the one hand, we observe a low inter-annotator agreement among experts when annotating events, disregarding the multitude of existing annotation guidelines and their numerous revisions. On the other hand, event extraction systems have a lower measured performance in terms of F1-score compared to other types of entities such as people or locations. In this paper we study the consistency and completeness of expert-annotated datasets for events and time expressions. We propose a data-agnostic validation methodology of such datasets in terms of consistency and completeness. Furthermore, we combine the power of crowds and machines to correct and extend expert-annotated datasets of events. We show the benefit of using crowd-annotated events to train and evaluate a state-of-the-art event extraction system. Our results show that the crowd-annotated events increase the performance of the system by at least 5.3%.

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Oana Inel and Lora Aroyo. Validation Methodology for Expert-Annotated Datasets: Event Annotation Case Study. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 12:1-12:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.LDK.2019.12

Author Details

Oana Inel
  • Delft University of Technology, The Netherlands
  • Vrije Universiteit Amsterdam, The Netherlands
Lora Aroyo
  • Google Research, New York, US

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