Recently there has been an increase in psycholinguistics and neurolinguistics research using naturalistic stimulus following Willems’ (2015) encouragement to probe the neural bases of language comprehension with greater ecological validity. Along with naturalistic stimulus, applying tools from computational linguistics to neuroimaging data can help us gain further insight into real time language processing. In this talk, I will discuss Multiword Expressions (MWEs). From a processing perspective, human sentence comprehension must involve both memory retrieval and structural composition and MWE processing can be an instance of memory retrieval. MWEs have also posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. Using Association Measures like Pointwise Mutual Information or Dice’s Coefficient, we can capture these finer-grained distinctions within MWEs. I present a neuroimaging study where fMRI recordings of naturalistic narrative comprehension is used to investigate to what extent these computational measures and their underlying cognitive processes are observable during sentence processing.
Shohini Bhattasali is a postdoctoral researcher working with Philip Resnik at University of Maryland, College Park. She is currently investigating human language interpretation and misinterpretation, through psycholinguistic and neurolinguistic measures and computational modeling of the interpretive process. Shohini received her Ph.D in Linguistics from Cornell University in 2019 with John Hale as her advisor. Her dissertation explored noncompositionality and argument structure from a neurolinguistic perspective by using various tools from computational linguistics.