dplp-docker

This docker container allows you to build, install and run the DPLP RST discourse parser (Ji and Eisenstein 2014) in a docker container.

Building / Installing DPLP

DPLP uses CoreNLP to generate parse trees from the input. We will run it as a server, so that the language models only have to be loaded once:

docker run -p 9000:9000 nlpbox/corenlp:3.9.2

You can check that it runs correctly by visiting [http://localhost:9000] in your browser. Now you can install dplp-docker:

git clone https://github.com/NLPbox/dplp-docker
cd dplp-docker
docker build -t dplp .

Running DPLP

To test if parser works, just run docker run --net host dplp. To run the parser on the file /tmp/input.txt on your local machine, run:

docker run --net host -v /tmp:/tmp -ti dplp /tmp/input.txt

If you run CoreNLP on a different host, then you'll need to set the CORENLP_ENDPOINT variable, e.g.

docker run -e CORENLP_ENDPOINT=http://example.com:9000 --net host -v /tmp:/tmp -ti dplp /tmp/input.txt

Example Input

Although they didn't like it, they accepted the offer.

Example Output

0       1       Although        although        IN      mark    3       O        (ROOT (SBAR (IN Although)      1
0       2       they    they    PRP     nsubj   3       O        (S (NP (PRP they))     1
0       3       didn't  didn't  VBP     root    0       O        (VP (VBP didn't)       1
0       4       like    like    IN      case    5       O        (PP (IN like)  1
0       5       it,     it,     NN      nmod    3       O        (NP (NP (NN it,))      1
0       6       they    they    PRP     nsubj   7       O        (SBAR (S (NP (PRP they))       2
0       7       accepted        accept  VBD     acl:relcl       5       O        (VP (VBD accepted)     2
0       8       the     the     DT      det     9       O        (NP (DT the)   2
0       9       offer.  offer.  NN      dobj    7       O        (NN offer.)))))))))))  2

ParentedTree('NS-elaboration', [ParentedTree('EDU', ['1']), ParentedTree('EDU', ['2'])])

Citation

Yangfeng Ji, Jacob Eisenstein (2014). Representation Learning for Text-level Discourse Parsing. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 13-24.