Article Structure
Abstract
We present a model for inducing sentential argument structure, which distinguishes arguments from optional modifiers.
Introduction
A fundamental challenge facing the language learner is to determine the content and structure of the stored units in the lexicon.
Approach
We adopt an approach where the lexicon consists of an inventory of stored tree fragments.
Model
Our model extends earlier work on induction of Bayesian TSGs (Post and Gildea, 2009; O’Donnell, 2011; Cohn et al., 2010).
Results
We evaluate our model in two ways.
Summary
We have investigated the role of the argument/modifier distinction in learning.
Topics
Penn Treebank
- We trained our model on sections 2—21 of the WSJ part of the Penn Treebank (Marcus et al., 1999).Page 3, “Results”
- Unfortunately, marking for argument/modifiers in the Penn Treebank is incomplete, and is limited to certain adverbials, e.g.Page 4, “Results”
- This corpus adds annotations indicating, for each node in the Penn Treebank , whether that node is a modifier.Page 4, “Results”
See all papers in Proc. ACL 2013 that mention Penn Treebank.
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Treebank
- We trained our model on sections 2—21 of the WSJ part of the Penn Treebank (Marcus et al., 1999).Page 3, “Results”
- Unfortunately, marking for argument/modifiers in the Penn Treebank is incomplete, and is limited to certain adverbials, e.g.Page 4, “Results”
- This corpus adds annotations indicating, for each node in the Penn Treebank , whether that node is a modifier.Page 4, “Results”
See all papers in Proc. ACL 2013 that mention Treebank.
See all papers in Proc. ACL that mention Treebank.
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