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Verb subcategorization frequencies: American English corpus data, methodological studies, and cross-corpus
Susanne Gahl1, Dan Jurafsky, Douglas Roland
1University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. gahl@icsi.berkeley.edu
Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|January 12, 2005
Summary
This study provides new verb bias data for psycholinguistic research, addressing limitations in existing resources. It offers norming data for 281 verbs and analyzes how coding choices impact transitivity bias estimates.
Area of Science:
- Psycholinguistics
- Computational Linguistics
- Corpus Linguistics
Background:
- Verb subcategorization frequencies, or verb biases, are crucial for understanding human sentence processing.
- Existing resources for verb biases have limitations in coverage, ecological validity, and coding consistency.
- Previous estimates of verb transitivity show significant variation based on corpus size, coverage, and coding criteria.
Purpose of the Study:
- To provide norming data for 281 verbs relevant to psycholinguistic research.
- To develop a detailed coding manual for verb bias analysis.
- To investigate the impact of different coding decisions and computation methods on transitivity bias estimates.
Main Methods:
- Analysis of a corpus of American English.
- Collection of norming data for 281 target verbs.
- Examination of coding decisions and their effect on verb bias computation.
Main Results:
- The study presents a comprehensive dataset for verb biases.
- It highlights the variability in transitivity bias estimates due to methodological choices.
- Provides insights into the influence of coding criteria on verb bias findings.
Conclusions:
- The generated norming data offers a valuable resource for psycholinguistic research.
- Understanding methodological influences is key to reliable verb bias research.
- This work contributes to more robust and ecologically valid studies of sentence processing.
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