| Abstract |
FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. This talk will present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. The approach was tested in an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, showing that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. The presentation will also 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. |