Probabilistic Models of Verb-Argument Structure

Probabilistic Models of Verb-Argument Structure Daniel Gildea Dept. of Computer and Information Science University of Pennsylvania dgildea@cis.upenn.edu Abstract We evaluate probabilistic models of verb argument structure trained on a corpus of verbs and their syn- tactic arguments. Models designed to represent pat- terns of verb alternation behavior are compared with generic clustering models in terms of the perplexity assigned to held-out test data. While the special- ized models of alternation do not perform as well, closer examination reveals alternation behavior rep- resented implicitly in the generic models. 1 Introduction Recent research into verb-argument structure has has attempted to acquire the syntactic alternation behavior of verbs directly from large corpora. Mc- Carthy (2000), Merlo and Stevenson (2001), and Schulte im Walde (2000) have evaluated their sys- tems’ accuracy against human judgments of verb classification, with the comprehensive verb classes of Levin (1993) often serving as a gold standard. Another area of research has focused on automatic clustering algorithms for verbs and their arguments with the goal of finding groups of semantically re- lated words (Pereira et al., 1993; Rooth et al., 1999), without focusing specifically on alternation behav- ior. We aim to bring these strands of research to- gether with a unified probabilistic model of verb ar- gument structure incorporating alternation behavior. Unraveling the mapping between syntactic func- tions such as subject and object and semantic roles such as agent and patient is an important piece of the language understanding problem. Learn- ing the alternation behavior of verbs automatically from unannotated text would significantly reduce the amount of labor needed to create text under- standing systems, whether that labor takes the form of writing lexical entries or of annotating semantic information to train statistical systems. Our use of generative probabilistic models of ar- gument structure also allows for language modeling applications independent of semantic interpretation. Language models based on head-modifier lexical dependencies in syntactic trees have been shown to have lower perplexity than n-gram language models and to reduce word-error rates for speech recogni- tion (Chelba and Jelinek, 1999; Roark, 2001). In- corporating semantic classes and verb alternation behavior could improve such models’ performance. Automatically derived word clusters are used in the statistical parsers of Charniak (1997) and Mager- man (1995). Incorporating alternation behavior into such models might improve parsing results as well. This paper focuses on evaluating probabilistic models of verb-argument structure in terms of how well they model unseen test data, as measured by perplexity. We will examine maximum likelihood bigram and trigram models, clustering models based on those of Rooth et al. (1999), as well as a new probabilistic model designed to capture alternations in verb-argument structure. 2 Capturing Alternation Behavior Automatic clustering of co-occurrences of verbs and their direct objects was first used to induce se- mantically related classes of both verbs and nouns (Pereira et al., 1993). Rooth et al. (1999) used the Expectation Maximization algorithm to perform soft clustering by optimizing the parameters of a fairly simple probability model, which considers the verb and noun …

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