Syntactic Structure Processing in the Brain while Listening

02/16/2023
by   Subba Reddy Oota, et al.
0

Syntactic parsing is the task of assigning a syntactic structure to a sentence. There are two popular syntactic parsing methods: constituency and dependency parsing. Recent works have used syntactic embeddings based on constituency trees, incremental top-down parsing, and other word syntactic features for brain activity prediction given the text stimuli to study how the syntax structure is represented in the brain's language network. However, the effectiveness of dependency parse trees or the relative predictive power of the various syntax parsers across brain areas, especially for the listening task, is yet unexplored. In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other. Further, we explore the relative importance of syntactic information (from these syntactic embedding methods) versus semantic information using BERT embeddings. We find that constituency parsers help explain activations in the temporal lobe and middle-frontal gyrus, while dependency parsers better encode syntactic structure in the angular gyrus and posterior cingulate cortex. Although semantic signals from BERT are more effective compared to any of the syntactic features or embedding methods, syntactic embedding methods explain additional variance for a few brain regions.

READ FULL TEXT

page 7

page 14

page 15

page 16

page 19

page 20

page 21

research
05/03/2022

Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?

Several popular Transformer based language models have been found to be ...
research
08/23/2018

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e....
research
11/11/2020

Multilingual Irony Detection with Dependency Syntax and Neural Models

This paper presents an in-depth investigation of the effectiveness of de...
research
01/21/2016

Syntax-Semantics Interaction Parsing Strategies. Inside SYNTAGMA

This paper discusses SYNTAGMA, a rule based NLP system addressing the tr...
research
08/01/2016

Left-corner Methods for Syntactic Modeling with Universal Structural Constraints

The primary goal in this thesis is to identify better syntactic constrai...
research
04/30/2020

Exploring Contextualized Neural Language Models for Temporal Dependency Parsing

Extracting temporal relations between events and time expressions has ma...
research
05/31/2017

Analysis of the Effect of Dependency Information on Predicate-Argument Structure Analysis and Zero Anaphora Resolution

This paper investigates and analyzes the effect of dependency informatio...

Please sign up or login with your details

Forgot password? Click here to reset