Dockerfile for the Feng 2014 RST Parser

About the Dockerfile

This Dockerfile provides a full environment for running the Feng 2014 RST Parser on bare text. It includes all dependencies and a fix for a bug in the crf wrapper blocking the parser pipeline in some Ubuntu environments (thanks Wladimir).

Usage

docker build -t feng-2014-rstparser .
docker run -v /Path/to/text/files:/samples -i -t feng-2014-rstparser
cd gCRF_dist/src
python parse.py /samples/F0001.txt

About the RST Parser

See http://www.cs.toronto.edu/~weifeng/software.html

Developer

• Vanessa Wei Feng Department of Computer Science University of Toronto Canada mailto:weifeng@cs.toronto.edu

References

• Vanessa Wei Feng and Graeme Hirst, 2014. Two-pass Discourse Segmentation with Pairing and Global Features. arXiv:1407.8215v1. http://arxiv.org/abs/1407.8215
• Vanessa Wei Feng and Graeme Hirst, 2014. A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing. In Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-2014), Baltimore, USA. http://aclweb.org/anthology/P14-1048

General information

• This RST-style discourse parser produces discourse tree structure on full-text level, given a raw text. No prior sentence splitting or any sort of preprocessing is expected. The program runs on Linux systems.
• The overall software work flow is similar to the one described in our paper (ACL 2014). However, to guarantee further efficiency, we remove the post-editing component from the workflow, and remove the set of entity-based transaction features from our feature set. Moreover, both structure and relation classification models are now implemented using CRFSuite.