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 @pare.xerox.corn Abstract Wc develop a l)ata-Oricntcd Parsing (DOP) model based on the syntactic representations of Lexical- f;unctional 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 pr~duction 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 maintaiIi hu'gc corpora of linguistic representations of previously occurring utterances. New utterances arc analyzed by combining (arbitrarily large) fragments from the corpus; the occurrence-frequencies of the fragments are used to determine wbich 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 |~afatneters'. ,, a formal definition of a well-formed represen- tation for utterance attalyses, • 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 bc rccombined to derive an analysis of a new utterance, and • a definition of a probabilio' model that indicates how the probability of a new utterance analysis is computed on tim basis of the probabilities of the fragments that combine to make it up. ['revious instantiations of the DOP architecture were based on utterance-analyses represented as surface phrase-structure trees ("Tree-DOP", e.g. Bod 1993; 1.(ajman 1995; Sinta'an 1995; Goodman 1996; l{onncma et al. 1997). Tree-DOP uses two decomt)osition 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 subtrce 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- I)OP is a node-substitution operation that replaces the left-most nonterminal frontier node in a subtree with a fragment whose root category lnatches the category of tile 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) = Yq) derives R P(D) A …

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