Probabilistic Parsing and Psychological Plausibility

Author: Thorsten Brants and Matthew Crocker

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Given the recent evidence for probabilistic mechanisms in models of human ambiguity resolution, this paper investigates the plausibility of exploiting current wide-coverage, probabilistic parsing techniques to model human linguistic performance. In particular, we investigate the performance of standard stochastic parsers when they are revised to operate incrementally, and with reduced memory resources. We present techniques for ranking and filtering analyses, together with experimental results. Our results confirm that stochastic parsers which adhere to these psychologically motivated constraints achieve good performance. Memory can be reduced down to 1% (compared to exhausitve search) without reducing recall and precision. Additionally, these models exhibit substantially faster performance. Finally, we argue that this general result is likely to hold for more sophisticated, and psycholinguistically plausible, probabilistic parsing models.