https://github.com/vrann/discourse-parser-docker
Dockerfile for the RST-style Discourse Parser
https://github.com/vrann/discourse-parser-docker
Last synced: 3 months ago
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Dockerfile for the RST-style Discourse Parser
- Host: GitHub
- URL: https://github.com/vrann/discourse-parser-docker
- Owner: vrann
- Created: 2016-03-27T22:44:00.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2016-03-29T03:47:18.000Z (about 9 years ago)
- Last Synced: 2025-01-08T01:52:53.890Z (5 months ago)
- Size: 1.95 KB
- Stars: 3
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Dockerfile for Discourse Parser
=====
Dockerfile for the RST-style Discourse Parser
DEVELOPERS
-----
* Vanessa Wei Feng
Department of Computer Science
University of Toronto
Canada
mailto:[email protected]
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 INFOMRATION
-----
* 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.
USAGE
-----
```
docker pull vrann/discourse-parser-docker
docker run -v /Path/to/text/files:/samples -i -t $container-id
cd gCRF_dist/src
python parse.py /samples/F0001.txt
```