This repository contains my fork of the RST parser published by Vanessa Wei Feng and Graeme Hirst. I updated some of its dependencies, dockerized the application, added some end-to-end tests and changed its output format to make it simpler to parse (e.g. by discoursegraphs or the rst-converter-service).
If you want to run the parser as a web service, have a look at nlpbox/feng-hirst-service.
The original source code
is still part of this repository. (The version published in Feng and Hirst (2012)
is tagged 1.01, Feng and Hirst (2014) is tagged 2.01—both are
in the master branch. The original README is kept in the file
README-original.txt.)
docker build -t feng-hirst .
If your input text is in /tmp/input.txt:
cat /tmp/input.txt
Although they didn't like it, they accepted the offer.
you can map the /tmp directory into the container and parse the text like
this:
docker run -v /tmp:/tmp -ti feng-hirst /tmp/input.txt
ParseTree('Contrast[S][N]', ["Although they did n't like it ,", 'they accepted the offer .'])
NOTE: For my convenience as a developer, this fork does not use the original output, which looks like this:
(Contrast[S][N]
_!Although they did n't like it ,!_
_!they accepted the offer . <P>!_)
but the one seen above (based on / parsed by the Python nltk Tree implementation).
If you use the Feng/Hirst RST parser in your academic work, please cite the following paper:
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.
For more technical details, see:
Vanessa Wei Feng and Graeme Hirst, 2014.
Two-pass Discourse Segmentation with Pairing and Global Features.
arXiv:1407.8215v1.