A Data-Oriented Parsing Model for Lexical-Functional Grammar

Data-Oriented Parsing (DOP) models of natural language propose that human language processing works with representations of concrete past language experiences rather than with abstract linguistic rules. These models operate by decomposing the given representations into fragments and recomposing those pieces to analyze new utterances. A probability model is used to select from all possible analyses of an utterance the most likely one. Previous DOP models were based on simple tree representations that neglect grammatical functions and syntactic features (Tree-DOP). In this paper, we present a new DOP model based on the more articulated representations of Lexical-Functional Grammar theory (LFG-DOP). LFG-DOP triggers a new, corpus-based notion of grammaticality, and an interestingly different class of probability models. An empirical evaluation of the model shows that larger as well as richer fragments improve performance. Finally, we go into some of the conceptual implications of our approach.