Modeling Conventionalization and Predictability in...
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Modeling Conventionalization and Predictability in Multiword Expressions at the Brain Level Shohini Bhattasali 1 Murielle Popa-Fabre 2 John Hale 3 Christophe Pallier 4 1 University of Maryland 2 INRIA – University of Paris 3 University of Georgia 4 CEA Inserm Cognitive Neuroimaging Unit SCiL 2020 Question Which gradient metric is psychologically plausi- ble for processing these noncompositional expres- sions during language comprehension? MWE Examples: break the ice, boa constrictor, safe and sound, see to it, in spite of Key Ideas • Apply computational linguistics metrics in a naturalistic listening fMRI study • r 2 Model Comparison, instead of GLM localization • Dice’s Coefficent is a better predictor of neural activation for processing MWEs Data Collection 51 native English speakers listened to The Little Prince for 1 hr & 38 mins across nine separate sec- tions; 15,388 words in total. Comprehension confirmed through multiple-choice questions (90% accuracy, SD = 3.7%). Multiword Expressions 742 MWEs were identified in the dataset through a transition-based MWE analyzer (Al Saied et al., 2017) trained on Children’s Book Test dataset (Hill et al., 2015). Association Measures calculated using corpus fre- quency counts from COCA: 1 Pointwise Mutual Information (PMI): PMI = log 2 c(w 1 n ) E (w 1 n ) (1) 2 Dice’s Coefficient: Dice = n × c(w 1 n ) Σ n i=1 c(w i ) (2) Group-level Results Figure 1:Z-map showing regions having a significant effect for Dice’s coefficient versus Pointwise Mutual Information after Bonferroni correction with p < 0.05 Figure 2:Graph comparing Pointwise Mutual Information (in blue) with Dice’s Coefficient (in green). fMRI Analysis Preprocessing was carried out with AFNI version 16 and ME-ICA v3.2 (Kundu et al., 2011). GLM Analysis: • MWE predictors convolved with SPM12’s canonical HRF, regressed against observed BOLD signal. • Includes four regressors of non-interest: word offset, frequency, pitch, intensity r 2 Model Comparison: For every subject, we com- pute how much the inclusion of each variable of inter- est (i.e. Dice and PMI) increases the cross-validated r 2 . Conclusion • Dice’s Coefficient, formalizing the degree of predictability, is a better predictor of cerebral activation for processing MWEs. • Indicates that Dice is a more cognitively plausible computational metric. • Previous work has shown gradient nature of MWEs (Bhattasali et al., 2018); results here illustrate how gradient they are. Acknowledgments This material is based upon work supported by the National Science Foundation under Grant No. 1607441. We thank Philip Resnik for valuable feedback.
Transcript of Modeling Conventionalization and Predictability in...
Modeling Conventionalization and Predictability in Multiword Expressions atthe Brain Level
Shohini Bhattasali 1 Murielle Popa-Fabre 2 John Hale 3 Christophe Pallier 4
1University of Maryland 2INRIA – University of Paris 3University of Georgia 4CEA Inserm Cognitive Neuroimaging Unit
SCiL 2020
Question
Which gradient metric is psychologically plausi-ble for processing these noncompositional expres-sions during language comprehension?
MWE Examples: break the ice, boaconstrictor, safe and sound, see to it, inspite of
Key Ideas
•Apply computational linguistics metrics in anaturalistic listening fMRI study
• r2 Model Comparison, instead of GLMlocalization
•Dice’s Coefficent is a better predictor of neuralactivation for processing MWEs
Data Collection
51 native English speakers listened to The LittlePrince for 1 hr & 38 mins across nine separate sec-tions; 15,388 words in total.Comprehension confirmed through multiple-choicequestions (90% accuracy, SD = 3.7%).
Multiword Expressions
742 MWEs were identified in the dataset through atransition-based MWE analyzer (Al Saied et al., 2017)trained on Children’s Book Test dataset (Hill et al.,2015).Association Measures calculated using corpus fre-quency counts from COCA:1 Pointwise Mutual Information (PMI):
PMI = log2c(w1
n)E(w1
n)(1)
2 Dice’s Coefficient:
Dice = n × c(w1n)
Σni=1
c(wi) (2)
Group-level Results
Figure 1:Z-map showing regions having a significant effect for Dice’s coefficient versus Pointwise Mutual Information after Bonferronicorrection with p < 0.05
Figure 2:Graph comparing Pointwise Mutual Information (in blue) with Dice’s Coefficient (in green).
fMRI Analysis
Preprocessing was carried out with AFNI version 16and ME-ICA v3.2 (Kundu et al., 2011).
GLM Analysis:•MWE predictors convolved with SPM12’scanonical HRF, regressed against observed BOLDsignal.
• Includes four regressors of non-interest: wordoffset, frequency, pitch, intensity
r2 Model Comparison: For every subject, we com-pute howmuch the inclusion of each variable of inter-est (i.e. Dice and PMI) increases the cross-validatedr2.
Conclusion•Dice’s Coefficient, formalizing the degree ofpredictability, is a better predictor of cerebralactivation for processing MWEs.
• Indicates that Dice is a more cognitivelyplausible computational metric.
•Previous work has shown gradient nature ofMWEs (Bhattasali et al., 2018); results hereillustrate how gradient they are.
AcknowledgmentsThis material is based upon work supported by the NationalScience Foundation under Grant No. 1607441. We thankPhilip Resnik for valuable feedback.
