scholarly journals Learning verb argument structure from minimally annotated corpora

2002 ◽  
Author(s):  
Anoop Sarkar ◽  
Woottiporn Tripasai
Keyword(s):  
Argument Structure ◽  
Verb Argument Structure ◽  
Annotated Corpora

scholarly journals Frequency Sensitivity of Neural Responses to English Verb Argument Structure Violations

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Jona Sassenhagen ◽  
Ryan Blything ◽  
Elena V. M. Lieven ◽  
Ben Ambridge
Keyword(s):  
Statistical Learning ◽  
High Frequency ◽  
Argument Structure ◽  
Low Frequency ◽  
Event Related Potentials ◽  
Neural Responses ◽  
Online Processing ◽  
Verb Argument Structure ◽  
Related Potentials ◽  
Intransitive Verbs

How are verb-argument structure preferences acquired? Children typically receive very little negative evidence, raising the question of how they come to understand the restrictions on grammatical constructions. Statistical learning theories propose stochastic patterns in the input contain sufficient clues. For example, if a verb is very common, but never observed in transitive constructions, this would indicate that transitive usage of that verb is illegal. Ambridge et al. (2008) have shown that in offline grammaticality judgements of intransitive verbs used in transitive constructions, low-frequency verbs elicit higher acceptability ratings than high-frequency verbs, as predicted if relative frequency is a cue during statistical learning. Here, we investigate if the same pattern also emerges in on-line processing of English sentences. EEG was recorded while healthy adults listened to sentences featuring transitive uses of semantically matched verb pairs of differing frequencies. We replicate the finding of higher acceptabilities of transitive uses of low- vs. high-frequency intransitive verbs. Event-Related Potentials indicate a similar result: early electrophysiological signals distinguish between misuse of high- vs low-frequency verbs. This indicates online processing shows a similar sensitivity to frequency as off-line judgements, consistent with a parser that reflects an original acquisition of grammatical constructions via statistical cues. However, the nature of the observed neural responses was not of the expected, or an easily interpretable, form, motivating further work into neural correlates of online processing of syntactic constructions.

scholarly journals Preemption versus Entrenchment: Towards a Construction-General Solution to the Problem of the Retreat from Verb Argument Structure Overgeneralization

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123723 ◽  
Author(s):  
Ben Ambridge ◽  
Amy Bidgood ◽  
Katherine E. Twomey ◽  
Julian M. Pine ◽  
Caroline F. Rowland ◽  
...  
Keyword(s):  
General Solution ◽  
Argument Structure ◽  
Verb Argument Structure

scholarly journals The role of performance limitations in the acquisition of verb-argument structure: an alternative account

2001 ◽  
Vol 28 (1) ◽  
pp. 127-152 ◽  
Author(s):  
ANNA L. THEAKSTON ◽  
ELENA V. M. LIEVEN ◽  
JULIAN M. PINE ◽  
CAROLINE F. ROWLAND
Keyword(s):  
Argument Structure ◽  
Alternative Account ◽  
Verb Argument Structure ◽  
Performance Limitations

Verb-Argument Structure Processing in Complex Sentences in Broca′s and Wernicke′s Aphasia

1993 ◽  
Vol 45 (3) ◽  
pp. 423-447 ◽  
Author(s):  
L.P. Shapiro ◽  
B. Gordon ◽  
N. Hack ◽  
J. Killackey
Keyword(s):  
Argument Structure ◽  
Complex Sentences ◽  
Verb Argument Structure

Word category and verb–argument structure information in the dynamics of parsing

Cognition ◽  
2004 ◽  
Vol 91 (3) ◽  
pp. 191-219 ◽  
Author(s):  
Stefan Frisch ◽  
Anja Hahne ◽  
Angela D Friederici
Keyword(s):  
Argument Structure ◽  
Structure Information ◽  
Word Category ◽  
Verb Argument Structure

scholarly journals When and how do children develop knowledge of verb argument structure? Evidence from verb bias effects in a structural priming task

2015 ◽  
Vol 81 ◽  
pp. 1-15 ◽  
Author(s):  
Michelle Peter ◽  
Franklin Chang ◽  
Julian M. Pine ◽  
Ryan Blything ◽  
Caroline F. Rowland
Keyword(s):  
Argument Structure ◽  
Structural Priming ◽  
Priming Task ◽  
Verb Bias ◽  
Verb Argument Structure

scholarly journals Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis

2018 ◽  
Vol 61 (2) ◽  
pp. 373-385 ◽  
Author(s):  
Chien-Ju Hsu ◽  
Cynthia K. Thompson
Keyword(s):  
Narrative Analysis ◽  
Argument Structure ◽  
Coding Systems ◽  
Language Analysis ◽  
Sentence Level ◽  
Agrammatic Aphasia ◽  
Verb Argument Structure ◽  
Assessment And Treatment ◽  
Healthy Control ◽  
Narrative Language

Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia (a) for reliability purposes to ascertain whether they yield similar results and (b) to evaluate CLAN for its ability to automatically identify language variables important for detailing agrammatic production patterns. Method The same set of Cinderella narrative samples from 8 participants with a clinical diagnosis of agrammatic aphasia and 10 cognitively healthy control participants were transcribed and coded using NNLA and CLAN. Both coding systems were utilized to quantify and characterize speech production patterns across several microsyntactic levels: utterance, sentence, lexical, morphological, and verb argument structure levels. Agreement between the 2 coding systems was computed for variables coded by both. Results Comparison of the 2 systems revealed high agreement for most, but not all, lexical-level and morphological-level variables. However, NNLA elucidated utterance-level, sentence-level, and verb argument structure–level impairments, important for assessment and treatment of agrammatism, which are not automatically coded by CLAN. Conclusions CLAN automatically and reliably codes most lexical and morphological variables but does not automatically quantify variables important for detailing production deficits in agrammatic aphasia, although conventions for manually coding some of these variables in Codes for the Human Analysis of Transcripts are possible. Suggestions for combining automated programs and manual coding to capture these variables or revising CLAN to automate coding of these variables are discussed.

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