WLP-Parser

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This repository contains a collection of neural network models that we used to demonstrate the utility of our dataset.

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# WLP-Parser: A wet lab protocol sequence tagger along with relation extraction.

This repository contains a collection of neural network models that we used to 
demostrate the utility of our dataset. These networks were trained using [Pytorch](http://pytorch.org/).

A more detailed description of the wet lab protocol corpus can be found in this paper:

[**An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols**](https://arxiv.org/pdf/1805.00195.pdf)  
Chaitanya Kulkarni, Wei Xu, Alan Ritter, Raghu Machiraju  
*In Proceedings of 16th Annual Conference of the North American Chapter 
of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT, 2018)*

Additional information regarding the action, entities and relations can be found [here](https://arxiv.org/pdf/1805.00195.pdf).

Also check out a working [demo](http://bionlp.osu.edu:5000/protocols) for sequence tagging and relation extraction using the methods in this repository.

## Usage
[TO BE POPULATED]

## Additional References

For Maximum Entropy Model used to label all the actions, entities and relations

[**A maximum entropy approach to named entity 
recognition.**](https://dl.acm.org/citation.cfm?id=930095)  
Marek Rei and Helen Yannakoudakis  
*In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-2016)*

For LSTM + CRF Model used for labelling actions and entities

[**Neural Architectures for Named Entity 
Recognition**](https://arxiv.org/pdf/1603.01360.pdf)
Guillaume Lample, Miguel Ballesteros,
Sandeep Subramanian, Kazuya Kawakami, Chris Dyer
*In Proceedings of 14th Annual Conference of the North American Chapter 
of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT, 2016)*

## License

Licensed under the [MIT License](https://opensource.org/licenses/MIT).

WLP-Parser

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