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Convolutional Neural Network for Relation Extraction

Note: This project is mostly based on https://github.com/yuhaozhang/sentence-convnet


Requirements

To download wikipedia articles (distant_supervision.py)

To visualize the results (visualize.ipynb)

Data

  • data directory includes preprocessed data:

    cnn-re-tf
    ├── ...
    ├── word2vec
    └── data
        ├── er              # binay-classification dataset
        │   ├── source.txt      #   source sentences
        │   └── target.txt      #   target labels
        └── mlmi            # multi-label multi-instance dataset
            ├── source.att      #   attention
            ├── source.left     #   left context
            ├── source.middle   #   middle context
            ├── source.right    #   right context
            ├── source.txt      #   source sentences
            └── target.txt      #   target labels
    

    To reproduce:

    python ./distant_supervision.py
    
  • word2vec directory is empty. Please download the Google News pretrained vector data from this Google Drive link, and unzip it to the directory. It will be a .bin file.

Usage

Preprocess

python ./util.py

It creates vocab.txt, ids.txt and emb.npy files.

Training

  • Binary classification (ER-CNN):

    python ./train.py --sent_len=3 --vocab_size=11208 --num_classes=2 --train_size=15000 \
    --data_dir=./data/er --attention=False --multi_label=False --use_pretrain=False
    
  • Multi-label multi-instance learning (MLMI-CNN):

    python ./train.py --sent_len=255 --vocab_size=36112 --num_classes=23 --train_size=10000 \
    --data_dir=./data/mlmi --attention=True --multi_label=True --use_pretrain=True
    
  • Multi-label multi-instance Context-wise learning (MLMI-CONT):

    python ./train_context.py --sent_len=102 --vocab_size=36112 --num_classes=23 --train_size=10000 \
    --data_dir=./data/mlmi --attention=True --multi_label=True --use_pretrain=True
    

Caution: A wrong value for input-data-dependent options (sent_len, vocab_size and num_class) may cause an error. If you want to train the model on another dataset, please check these values.

Evaluation

python ./eval.py --train_dir=./train/1473898241

Replace the --train_dir with the output from the training.

Run TensorBoard

tensorboard --logdir=./train/1473898241

Architecture

CNN Architecture

Results

P R F AUC init_lr l2_reg
ER-CNN 0.9410 0.8630 0.9003 0.9303 0.005 0.05
MLMI-CNN 0.8205 0.6406 0.7195 0.7424 1e-3 1e-4
MLMI-CONT 0.8819 0.7158 0.7902 0.8156 1e-3 1e-4

F1 AUC Loss PR_Curve ER-CNN Embeddings MLMI-CNN Embeddings MLMI-CONT Left Embeddings MLMI-CONT Right Embeddings

*As you see above, these models somewhat suffer from overfitting ...

References


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