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Modelling Attachment Decisions with a Statistical Parser

Probabilistic models of human sentence processing have proved widely successful in modelling human attachment decisions. This Thesis describes a two-stage probabilistic model of human incremental parsing of German PP-Attachment. German verb final sentences provide a new opportunity for the study of PP-Attachment because the PP is processed in the absence of the sentence head. In this situation, it is preferentially attached to the (existing) NP site. The model consists of two modules: A syntactic module based on a standard stochastic parser and a shallow semantic module that makes final attachment decisions on the basis of Web counts. Conflicts between decisions made by the modules are interpreted as predicting longer reading times. The model’s predictions are compared to average reading times from an eyetracking study (Konieczny et al., 1997). The model correctly accounts for attachment preferences in verb second sentences. This is a replication of results for English. It fails to account for preferences in verb final sentences. We argue that these preferences cannot be modelled by a probabilistic CFG at all. They probably belong to a range of phenomena that have been explained by either the initial influence of global structural frequencies or by the existence of general parsing strategies. Purely probabilistic CFGs cannot accomodate either of these explanations.