Capturing Ambiguity in Crowdsourcing Frame Disambiguation

A. Dumitrache, L.M. Aroyo, Chris Welty

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the Sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP-18)
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages9
ISBN (Electronic)9781577357995
ISBN (Print)9781577357995
Publication statusPublished - 2018
Eventthe Sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP-18) - Zürich, Switzerland
Duration: 5 Jul 20188 Jul 2018
Conference number: 6th


Conferencethe Sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP-18)


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