A Probabilistic Corpus-Driven Model for Lexical-Functional Analysis

1. I n t r o d u c t i o n Data-Oriented Parsing (DOP) models of natural language embody the assumpt ion 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 ut terances arc analyzed by combin ing (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 r e p r e s e n tation f o r u t terance attalyses, • a set of d e c o m p o s i t i o n o p e r a t i o n s that divide a given utterance analysis into a set of fragments, • a set of c o m p o s i t i o n o p e r a t i o n s by which such fragments may bc rccombined to derive an analysis of a new utterance, and • a definition of a probab i l i o ' mode l 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). T ree -DOP uses two decomt)osition operations that produce connected subtrees of utterance representations: (1) the R o o t 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 F r o n t i e r 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 TreeI)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 treerepresentations 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 Tree-DOP derivation D = <tj, t2 . tk> is produced by a stochastic branching process, it starts by randomly choosing a fragment t~ labeled with the initial category (e.g. St. At each subsequent step, a next fragment is chosen at random from among the set of competitors for composition into the current subtree. The process stops when a tree results with no nonterminal leaves, l,et C P ( t l C S ) denote the probability of choosing a tree t from a contpetilion set CS containing t. Then the probability of a derivation is P(<tl, t2 . tk>) = [ l i C P ( t i I CSi) where the competition probability CP(t I CS) is given by CP(t I CS) = P(t) / Zt, e CS P(t') Here, P(t) is the fragment probability for t in a given corpus. Let Ti_ 1 = tl ° t2 ° . o t i . l be the subanalysis just before the ith step of the process, let LNC(Ti_I ) denote the category of the leftmost nonterminal of 7"/-1, and let r(t) denote the root category of a fragment t. Then the competition set at the /tit step is CSi = { t: r ( t )=l ,NC(Ti . 1) } That is, the competit ion sets for Tree-DOP are determined by the ca tegory of the lef tmost nontermina[ of the current subanalysis. This is not the only possible definition of competi t ion set. As Manning and Carpenter (1997) have shown, the competit ion sets can be made dependent on the composition operation. Their left-corner language model would also apply to Tree-DOP, yielding a different definition for the competition sets. But the properties of such Tree-DOP models have not been investigated. Experiments with Tree-DOP on the Penn Treebank and the OVIS corpus show a consistent increase in parse accuracy when larger and more complex subtrees are taken into account (cf. Bod 1993, 95, 98; Bonnema et al. 1997; Sekine & Grishman 1995; Sima'an 1995). However, "Free-DOP is limited in that it cannot account for underlying syntactic (and semantic) dependencies that are not