• A PyTorch implementation of a common NER model: BiLSTM-CRF with Character-Word-Capital-Affix-Level Feature.
• Tips: Some code are referred from ZhixiuYe/NER-pytorch.
• Paper: Bidirectional LSTM-CRF Models for Sequence Tagging - arXiv
• Paper: Deep Affix Features Improve Neural Named Entity Recognizers - ACL

• NER dataset: CoNLL-2003-English, it can be downloaded from release page.
• Pretrained word embedding: glove.6B.100d.txt, it can be downloaded from here.

• First, download eng.train, eng.testa, eng.testb, glove.6B.100d.txt and save to ./data/ folder.
• To train, execute the following script. You can edit some parameters in train.py.

Tips: The evaluate script will ask you to have a perl interpreter, commonly it has been installed on Linux systems.

python3 train.py \
    --output_mapping ./output/mapping.pkl \
    --output_affix_list ./output/affix_list.json \
    --use_crf 1 \
    --add_cap_feature 1 \
    --add_affix_feature 1 \
    --use_gpu 1 \
    --model_path ./model

• vikas95/Pref_Suff_Span_NN
• ZhixiuYe/NER-pytorch