---
title: "Statistical Parsing with Probabilistic Symbol-Refined Tree Substitution Grammars"
authors:
  - "Hiroyuki Shindo"
  - "Yusuke Miyao"
  - "Akinori Fujino"
  - "Masaaki Nagata"
doi: ""
url: "https://ijcai.org/proceedings/2013/461"
pdf: "https://ijcai.org/proceedings/2013/0461.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "3082-3085"
track: ""
keywords:
---

# Statistical Parsing with Probabilistic Symbol-Refined Tree Substitution Grammars

Hiroyuki Shindo, Yusuke Miyao, Akinori Fujino, Masaaki Nagata

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 3082-3085.

## Abstract

We present probabilistic Symbol-Refined Tree Substitution Grammars (SR-TSG) for statistical parsing of natural language sentences. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. Our probabilistic model is consistent based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, thus all grammar rules can be learned from training data in a fully automatic fashion. Our SR-TSG parser achieves the state-of-the-art performance on the Wall Street Journal (WSJ) English Penn Treebank data.