A Finite-State Model of Human Sentence Processing

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 49–56, Sydney, July 2006. c⃝2006 Association for Computational Linguistics A Finite-State Model of Human Sentence Processing Jihyun Park and Chris Brew Department of Linguisitcs The Ohio State University Columbus, OH, USA {park|cbrew}@ling.ohio-state.edu Abstract It has previously been assumed in the psycholinguistic literature that finite-state models of language are crucially limited in their explanatory power by the local- ity of the probability distribution and the narrow scope of information used by the model. We show that a simple computa- tional model (a bigram part-of-speech tag- ger based on the design used by Corley and Crocker (2000)) makes correct predic- tions on processing difficulty observed in a wide range of empirical sentence process- ing data. We use two modes of evaluation: one that relies on comparison with a con- trol sentence, paralleling practice in hu- man studies; another that measures prob- ability drop in the disambiguating region of the sentence. Both are surprisingly good indicators of the processing difficulty of garden-path sentences. The sentences tested are drawn from published sources and systematically explore five different types of ambiguity: previous studies have been narrower in scope and smaller in scale. We do not deny the limitations of finite-state models, but argue that our re- sults show that their usefulness has been underestimated. 1 Introduction The main purpose of the current study is to inves- tigate the extent to which a probabilistic part-of- speech (POS) tagger can correctly model human sentence processing data. Syntactically ambigu- ous sentences have been studied in great depth in psycholinguistics because the pattern of ambigu- ity resolution provides a window onto the human sentence processing mechanism (HSPM). Prima facie it seems unlikely that such a tagger will be adequate, because almost all previous researchers have assumed, following standard linguistic the- ory, that a formally adequate account of recur- sive syntactic structure is an essential component of any model of the behaviour. In this study, we tested a bigram POS tagger on different types of structural ambiguities and (as a sanity check) to the well-known asymmetry of subject and object relative clause processing. Theoretically, the garden-path effect is defined as processing difficulty caused by reanalysis. Em- pirically, it is attested as comparatively slower reading time or longer eye fixation at a disam- biguating region in an ambiguous sentence com- pared to its control sentences (Frazier and Rayner, 1982; Trueswell, 1996). That is, the garden-path effect detected in many human studies, in fact, is measured through a “comparative” method. This characteristic of the sentence processing research design is reconstructed in the current study using a probabilistic POS tagging system. Under the assumption that larger probability de- crease indicates slower reading time, the test re- sults suggest that the probabilistic POS tagging system can predict reading time penalties at the disambiguating region of garden-path sentences compared to that of non-garden-path sentences (i.e. control sentences). 2 Previous Work Corley and Crocker (2000) present a probabilistic model …

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