Statistical Parsing With an Automatically Extracted Tree Adjoining Grammar (2003)
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@MISC{Chiang03statisticalparsing,
author = {David Chiang},
title = {Statistical Parsing With an Automatically Extracted Tree Adjoining Grammar},
year = {2003}
}
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Abstract
Introduction Why use tree adjoining grammars (TAG) for statistical parsing? It might be thought that its added formal power makes parameter estimation unnecessarily di#cult; or that whatever benefits it provides---the ability to model unbounded cross-serial dependencies, for example--- are inconsequential for statistical parsing, which is concerned with the probable rather than the possible. But just as TAG is not by itself a complete linguistic theory, but a formalism for specifying linguistic theories, it should not be viewed as a statistical model but a formalism for specifying statistical models. The advantage that TAG has over CFG is that it assigns richer structural descriptions to sentences; specifically, in addition to parse trees, it assigns derivation trees (defined below) on which features of a parsing model can be defined. In this chapter we explore the use of TAG for statistical parsing. We start by examining PCFG-based parsers which use head-lexicalization to capture
