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lstm_crowd
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LICENSE
README
crf.py
hmm.py
pico.py
task1.zip
util.py

README

Source code for our paper:

Aggregating and Predicting Sequence Labels from Crowd Annotations
An Thanh Nguyen, Byron C. Wallace, Junyi Jessy Li, Ani Nenkova and Matthew Lease
Association for Computational Linguistics (ACL)


The crowd NER dataset is from Rodrigues et al. (2014):
http://www.fprodrigues.com/software/crf-ma-sequence-labeling-with-multiple-annotators/

For LSTM-Crowd, our implementation extends Lample et al. (2016): https://github.com/glample/tagger

Data for the Biomedical IE task: https://github.com/yinfeiy/PICO-data

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Requirements: Python 2

Installing required packages using virtualenv (so it does not collide with system dependencies)

$ virtualenv env-python2 --python=python2
$ source env-python2/bin/activate
(env-python2) $ pip install sklearn numpy nltk scipy python-crfsuite matplotlib

To run the experiment on NER Task 1, extract the Rodrigues et al. data in task1.zip then execute: 

python hmm.py -i task1/val/ -o output.txt

to run on the validation set or 

python hmm.py -i task1/test/ -o output.txt

to run on the test set.

Execute python hmm.py --help for more options.

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References:

Rodrigues, Filipe, Francisco Pereira, and Bernardete Ribeiro. "Sequence labeling with multiple annotators." Machine Learning 95.2 (2014): 165-181.

Lample, Guillaume, et al. "Neural architectures for named entity recognition." arXiv preprint arXiv:1603.01360 (2016).