Article Structure
Abstract
We present a Bayesian nonparametric model for estimating tree insertion grammars (TIG), building upon recent work in Bayesian inference of tree substitution grammars (TSG) via Dirichlet processes.
Introduction
There is a deep tension in statistical modeling of grammatical structure between providing good expressivity — to allow accurate modeling of the data with sparse grammars — and low complexity —making induction of the grammars and parsing of novel sentences computationally practical.
Probabilistic Model
In the basic nonparametric TSG model, there is an independent DP for every grammar category (such as c = NP), each of which uses a base distribution P0 that generates an initial tree by making stepwise decisions.
Inference
Given this model, our inference task is to explore optimal derivations underlying the data.
Evaluation Results
We use the standard Penn treebank methodology of training on sections 2—21 and testing on section 23.
Conclusion
We described a nonparametric Bayesian inference scheme for estimating TIG grammars and showed the power of TIG formalism over TSG for returning rich, generalizable, yet compact representations of data.
Topics
parse tree
- Recent work that incorporated Dirichlet process (DP) nonparametric models into TSGs has provided an efficient solution to the problem of segmenting training data trees into elementary parse tree fragments to form the grammar (Cohn et al., 2009; Cohn and Blunsom, 2010; Post and Gildea, 2009).Page 1, “Introduction”
- Figure 2: TIG-to-TSG transform: (a) and (b) illustrate transformed TSG derivations for two different TIG derivations of the same parse tree structure.Page 2, “Introduction”
- Following previous work, we design a blocked Metropolis-Hastings sampler that samples derivations per entire parse trees all at once in a joint fashion (Cohn and Blunsom, 2010; Shindo et al., 2011).Page 3, “Inference”
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treebank
- We use the Penn treebank for our experiments and find that our proposal Bayesian TIG model not only has competitive parsing performance but also finds compact yet linguistically rich TIG representations of the data.Page 1, “Abstract”
- We use the standard Penn treebank methodology of training on sections 2—21 and testing on section 23.Page 4, “Evaluation Results”
- carried out a small treebank experiment where we train on Section 2, and a large one where we train on the full training set.Page 4, “Evaluation Results”
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