kmullapudi-11 / dlh_finalproject

DLH Final Project Team 82 - [email protected], [email protected]

Citation to the original paper

Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (Zhou et al., ACL 2016)

Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 207–212, Berlin, Germany. Association for Computational Linguistics.

Link to the code repository

We used an open-source implementation of the aforemnetioned paper that can be found at this link.

The original README for this repository can be found at the end of this file.

Dependencies

1. Create a python virtual environment in this directory
2. Install dependencies using:

pip3 install -r requirements.txt

Data download instruction

This repository includes the data used in our experiment in the concept_assertion_relation_training_data folder.

The data can also be obtained from the Harvard Department of Medical Informatics n2c2 research portal.

Preprocessing code + command

The pre-processing code lives in the data_formatting.py file. To pre-process out data, run:

python3 data_formatting.py

Training code + Evaluation code command

Our training and evaluation code lives in the train.py file. The hyperparameters associated with the training can be modified in the get_args.py file. By default, our training model runs for 25 epochs and print the following statistics in the console:

• Loss
• Accuracy
• F-1 scores for each label
• Recall scores for each label
• Precision scores for each label

Training can be run using the following command:

python3 train.py

Table of results

We used a 70-30 split on our dataset for our train-test split. We used a random word embedding for our sentences and used the following hyperparameters:

• embedding dimension - 100
• LSTM combine - 'add'
• embbeding dropout - 0.5

We achieved the following results:

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[Original GitHub README]

(Pytorch) Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

Pytorch implementation of ACL 2016 paper, Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (Zhou et al., 2016)

• Dataset: Relation Extraction Challenge(SemEval-2010 Task #8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals)
• Performance: This code repo approached 71% F1. Please feel free to fork and refine it and get the paper's reported 84%.
• Model Structure: Attention-based BiLSTM

Reference

• Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (ACL 2016), P Zhou et al.
• SeoSangwoo's Tensorflow implementation