Giter Club home page Giter Club logo

ardakdemir / joint-ner-and-md-tagger Goto Github PK

View Code? Open in Web Editor NEW

This project forked from onurgu/joint-ner-and-md-tagger

0.0 2.0 0.0 4.81 MB

This repo contains the software that was used to conduct the experiments reported in our article titled "Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags" [1] to be presented at COLING 2018.

License: MIT License

Dockerfile 0.39% sed 47.44% Python 38.34% Perl 2.39% Shell 8.75% Awk 0.16% Jupyter Notebook 1.54% CSS 0.02% HTML 0.33% JavaScript 0.64%

joint-ner-and-md-tagger's Introduction

Neural Tagger for MD and NER

This repo contains the software that was used to conduct the experiments reported in our article titled "Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags" [1] to be presented at COLING 2018.

Training and testing

We recommend using the helper scripts for conducting experiments. The scripts named helper-script-* run the experiments in the paper with given hyper parameters. Beware that the option -j6 will make the script run six experiment configurations at once.

bash ./scripts/default/helper-script-to-run-the-experiment-set-small-sizes.sh campaing_name | parallel -j6

For the reporting part to work, you should set up a working sacred environment, which is very easy if you choose a filesystem based storage. You can find an example of this in the helper script found in ./scripts/TRUBA folder.

Tag sentences

This project do not have a designated tagger script for now but you can obtain the output in eval_dir. You should provide the text in tokenized form in CoNLL format. The script will tag both the development and testing files and produce files in ./evaluation/temp/eval_logs/. If you need this and want to contribute by coding and sharing it with the project, you are welcome.

Replication of the experiments

To reproduce the experiments reported with our model, you can use Docker and build a replica of our experimentation environment.

To build:

docker build -t yourimagename:yourversion .

To run:

docker run -ti -v `pwd`/dataset:/opt/ner-tagger-dynet/dataset -v `pwd`/models:/opt/ner-tagger-dynet/models yourimagename:yourversion python train.py --train dataset/gungor.ner.train.small --dev dataset/gungor.ner.dev.small --test dataset/gungor.ner.test.small --word_dim 300 --word_lstm_dim 200 --word_bidirect 1 --cap_dim 100 --crf 1 --lr_method=adam --maximum-epochs 50 --char_dim 200 --char_lstm_dim 200 --char_bidirect 1 --overwrite-mappings 1 --batch-size 1

You should create or set permissions accordingly for `pwd`/dataset and `pwd`/models.

References

[1] Gungor, O., Uskudarli, S., Gungor, T., Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags, 2018, COLING 2018, 19-25 August, (to appear).

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.