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Named Entity Recognition on CoNLL dataset using BiLSTM+CRF implemented with Pytorch

Named Entity Recognition on CoNLL dataset using BiLSTM+CRF implemented with Pytorch

NER_pytorch

Named Entity Recognition on CoNLL dataset using BiLSTM+CRF implemented with Pytorch.

paper

  • Neural Architectures for Named Entity Recognition

  • End-toEnd Sequence labeling via BLSTM-CNN-CRF

code

This code is customized so that i use latest Pytorch version(1.1.0) starting with https://github.com/ZhixiuYe/NER-pytorch

To use jupyter notebook to visualize the result, i transform ~.py into .ipynb

The f1 score performane of test CoNLL data is 91.3%

Conll performance

f1 91.3%

0. prepare data

To get pre-trained word embedding vector Glove

run prepare_data.ipynb

1. train

150 epoch is enough, 24h with oneP100 GPU, 51 epoch has best f1 score, i use visdom

model shape

  1. word embedding with Glove(100d) + charactor embedding with CNN(25d)

  2. BiLSTM 1 layer + Highway

  3. Linear 400d -> 19d with tanh

     BiLSTM_CRF(
               (char_embeds): Embedding(85, 25)
               (char_cnn3): Conv2d(1, 25, kernel_size=(3, 25), stride=(1, 1), padding=(2, 0))
               (word_embeds): Embedding(400176, 100)
               (dropout): Dropout(p=0.5)
               (lstm): LSTM(125, 200, bidirectional=True)
               (hw_trans): Linear(in_features=25, out_features=25, bias=True)
               (hw_gate): Linear(in_features=25, out_features=25, bias=True)
               (h2_h1): Linear(in_features=400, out_features=200, bias=True)
               (tanh): Tanh()
               (hidden2tag): Linear(in_features=400, out_features=19, bias=True)
     )
    

    run 1. train.ipynb

2. evaluation

run 2. evaluation.ipynb

Result

NER_pytorch

data

https://www.clips.uantwerpen.be/conll2003/ner/

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Here is an example:

    word     | POS | Syntatic chunk tag | named entity tag
    U.N.       NNP   I-NP                 I-ORG 
    official   NN    I-NP                 O 
    Ekeus      NNP   I-NP                 I-PER 
    heads      VBZ   I-VP                 O 
    for        IN    I-PP                 O 
    Baghdad    NNP   I-NP                 I-LOC 
    .          .     O                    O 

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