Overview

A bi-directional GRU implementation for Relation Extraction:

The GRU is loosely based on the approach done in the work of Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (Zhou et. al, 2016):

1. Bidirectional GRU
2. Attention layer
3. L2 Regularization

Installation

This project is implemented in python 3.6 and tensorflow 2.0. Follow these steps to setup your environment:

1. Download and install Conda
2. Create a Conda environment with Python 3.6

conda create -n nlp-hw4 python=3.6

3. Activate the Conda environment.

conda activate nlp-hw4

4. Install the requirements:

pip install -r requirements.txt

5. Download spacy model

python -m spacy download en_core_web_sm

6. Download glove wordvectors:

./download_glove.sh

Data

Training can be found in data/train.txt and validation is in data/val.txt. We are using data from a previous SemEval shared task which in total had 8,000 training examples. The train/validation examples are a 90/10 split from this original 8,000. More details of the data can be found in the overview paper SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals (Hendrickx et. al, 2009) as well as extra PDFs explaining the details of each relation in the dataset directory.

Train and Predict

There are 4 main scripts in the repository train_basic.py, train_advanced.py, predict.py and evaluate.pl.

• Train scripts do as described and saves the model to be used later for prediction. Basic training script trains the basic MyBasicAttentiveBiGRU model implementation (Bi-RNN+attention).

• Predict generates predictions on the test set test.txt and saves your output to a file. You will submit your predictions to be scored against the hidden (labels) test set. Both files are set with reasonable defaults in their arguments but example commands are shown below.

• Evaluation script is the pearl script unlike others. You can use it see detailed report of your predictions file against the gold labels.

Train a model

python train.py --embed-file embeddings/glove.6B.100D.txt --embed-dim 100 --batch-size 10 --num_epochs 5

# stores the model by default at : serialization_dirs/basic/

Predict with model

python predict.py --prediction-file my_predictions.txt --batch-size 10