Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction
Paper summary They incorporate fMRI features into POS tagging, under the assumption that reading semantically/functionally different words will activate the brain in different ways. For this they use a dataset of fMRI recordings, where the subjects were reading a chapter of Harry Potter. The main issue is that fMRI has very low temporal resolution – there is only one fMRI reading per 4 tokens, and in general it takes around 4-14 seconds for something to show up in fMRI. Nevertheless, they construct token-level vectors by using a Gaussian weighted average, integrate them into an unsupervised POS tagger, and show that it is able to improve performance. https://i.imgur.com/TU60N6w.png
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Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction
Bingel, Joachim and Barrett, Maria and Søgaard, Anders
Association for Computational Linguistics - 2016 via Local Bibsonomy
Keywords: dblp


Summary by Marek Rei 4 months ago
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