Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data
Paper summary The paper explores the usefulness of eye tracking for the task of POS tagging. The assumption is that readers skip quickly over closed class words, and fixate longer on rare on ambiguous words. The experiments are performed on unsupervised POS tagging – a second-order HMM uses constraints on possible tags for each word (based on a dictionary), but no explicit annotated data is required. They show that including the eye tracking features improves performance by quite a bit. Surprisingly, it seems to be better to average eye tracking features over all training tokens of the same type, as opposed to using using the data for each individual token, which means eye tracking is only used during the training stage.
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Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data
Barrett, Maria and Bingel, Joachim and Keller, Frank and Søgaard, Anders
Association for Computational Linguistics - 2016 via Local Bibsonomy
Keywords: dblp


Summary by Marek Rei 6 months ago
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