Capturing Ambiguity in Crowdsourcing Frame Disambiguation

Authors

  • Anca Dumitrache Vrije Universiteit Amsterdam
  • Lora Aroyo Vrije Universiteit Amsterdam
  • Chris Welty Google Research

DOI:

https://doi.org/10.1609/hcomp.v6i1.13330

Keywords:

crowdsourcing, FrameNet, semantic frames, disambiguation

Abstract

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence.  Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.

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Published

2018-06-15

How to Cite

Dumitrache, A., Aroyo, L., & Welty, C. (2018). Capturing Ambiguity in Crowdsourcing Frame Disambiguation. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 6(1), 12-20. https://doi.org/10.1609/hcomp.v6i1.13330