PP-2002-19: A Data-Oriented Parsing Model for Lexical-Functional Grammar

PP-2002-19: Bod, Rens and Kaplan, Ronald (2002) A Data-Oriented Parsing Model for Lexical-Functional Grammar. [Report]

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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 deep 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.

Item Type: Report
Report Nr: PP-2002-19
Series Name: Prepublication (PP) Series
Year: 2002
Date Deposited: 12 Oct 2016 14:36
Last Modified: 12 Oct 2016 14:36
URI: https://eprints.illc.uva.nl/id/eprint/83

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