Who did what to whom? Syntactic parsing in man and machine

Lead Research Organisation: University of Birmingham
Department Name: School of Psychology


This industrial collaboration project with Google Research London bridges between artificial intelligence, computational linguistics, computational and cognitive neuroscience.

Language processing is one of the most complex tasks facing the human brain in everyday life. In order to understand 'who does what to whom', a biological or artificial agent needs to assign a syntactic structure to each sentence - a process coined 'syntactic parsing'.

Artificial intelligence and computational linguistics has developed a range of machine learning algorithms (e.g. dependency parsing) that are trained on large annotated linguistic corpora to assign grammatical classes and syntactic structures automatically to novel natural sentences. Yet, while these algorithms are relatively efficient, they do not obtain the accuracy of human readers.

This interdisciplinary project will combine expertise from human neuroscience (University of Birmingham) and computational linguistics (Google Research London) to determine the neural mechanisms underlying sentence comprehension in the human brain and advance parsing algorithms in machines. To study natural language processing and the underlying neural mechanisms in humans, we will measure eye movements, behavioural (psychophysics) and electrophysiological responses (EEG/fMRI) in participants reading natural sentences from syntactically annotated corpora. We will employ advanced machine learning algorithms to characterize the computational operations and neural mechanisms underlying syntactic processing in the human brain. Conversely, the insights obtained from human neuroimaging (EEG/fMRI) and eye tracking will provide critical constraints on the parameters and algorithms used in machine.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M01116X/1 01/10/2015 30/09/2023
1898610 Studentship BB/M01116X/1 02/10/2017 24/01/2022 Alex Murphy