A Probabilistic Corpus-Driven Model for Lexical-Functional Analysis

A Probabilistic Corpus-Driven Model for Lexical-Functional Analysis Rens Bod Department of Computational Linguistics University of Amsterdam Spuistraat 134, NL- 1012 VB Amsterdam rens.bod@let.uva.nl Ronald Kaplan Xerox Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 kaplan@parc.xerox.com Abstract We develop a Data-Oriented Parsing (DOP) model based on the syntactic representations of Lexical- Functional Grammar (LFG). We start by sum- marizing the original DOP model for tree represen- tations and then show how it can be extended with corresponding functional structures. The resulting LFG-DOP model triggers a new, corpus-based notion of grammaticality, and its probability models exhibit interesting behavior with respect to specificity and the interpretation of ill-formed strings. 1. Introduction Data-Oriented Parsing (DOP) models of natural language embody the assumption that human language perception and production works with representations of past language experiences, rather than with abstract grammar rules (cf. Bod 1992, 95; Scha 1992; Sima'an 1995; Rajman 1995). DOP models therefore maintain large corpora of linguistic representations of previously occurring utterances. New utterances are analyzed by combining (arbitrarily large) fragments from the corpus; the occurrence-frequencies of the fragments are used to determine which analysis is the most probable one. In accordance with the general DOP architecture outlined by Bod (1995), a particular DOP model is described by specifying settings for the following four parameters: • a formal definition of a well-formed represen- tation for utterance analyses, • a set of decomposition operations that divide a given utterance analysis into a set of fragments, • a set of composition operations by which such fragments may be recombined to derive an analysis of a new utterance, and • a definition of a probability model that indicates how the probability of a new utterance analysis is computed on the basis of the probabilities of the fragments that combine to make it up. Previous instantiations of the DOP architecture were based on utterance-analyses represented as surface phrase-structure trees CTree-DOP", e.g. Bod 1993; Rajman 1995; Sima'an 1995; Goodman 1996; Bonnema et al. 1997). Tree-DOP uses two decomposition operations that produce connected subtrees of utterance representations: (1) the Root operation selects any node of a tree to be the root of the new subtree and erases all nodes except the selected node and the nodes it dominates; (2) the Frontier operation then chooses a set (possibly empty) of nodes in the new subtree different from its root and erases all subtrees dominated by the chosen nodes. The only composition operation used by Tree- DOP is a node-substitution operation that replaces the left-most nonterminal frontier node in a subtree with a fragment whose root category matches the category of the frontier node. Thus Tree-DOP provides tree- representations for new utterances by combining fragments from a corpus of phrase structure trees. A Tree-DOP representation R can typically be derived in many different ways. If each derivation D has a probability P(D), then the probability of deriving R is the sum of the individual derivation probabilities: P(R) = ~D derives R P(D) A Tree-DOP derivation D …

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