Generalizing Case Frames Using a Thesaurus and the MDL Principle

Abstract

We address the problem of automatically acquiring case-frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the efficiency, our method makes use of an existing thesaurus and restricts its attention on those partitions that are present as `cuts' in the thesaurus tree, thus reducing the generalization problem to that of estimating the `tree cut models' of the thesaurus. We then give an efficient algorithm which provably obtains the optimal tree cut model for the given frequency data, in the sense of MDL. We have used the case-frame patterns obtained using our method to resolve pp-attachment ambiguity.Our experimental results indicate that our method improves upon or is at least as effective as existing methods.

11 pages, uuencoded compressed postscript, a revised version

References in corpus (5)

• Inducing Probabilistic Grammars by Bayesian Model Merging
• Training and Scaling Preference Functions for Disambiguation
• Learning Word Association Norms Using Tree Cut Pair Models
• Learning Dependencies between Case Frame Slots
• Segmenting speech without a lexicon: The roles of phonotactics and speech source

Cited by in corpus (6)

• Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
• Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
• A Probabilistic Disambiguation Method Based on Psycholinguistic Principles
• Representing Verbs as Argument Concepts
• Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics
• Learning Dependencies between Case Frame Slots