NER that uses Graph Conv Nets
This is an implementation of a named entity recognizer that uses Graph Convolutional Networks. The reference article is Graph Convolutional Networks for Named Entity Recognition.
This code uses GCNs and POS tagging to boost the entity recognition of a bidirectional LSTM. It scores ~81% on the Ontonotes 5 test dataset, which can be retrieved from the LDC website.
The system currently uses the word vectors that come with spacy's "en_core_web_md" model.
Installation
git clone https://github.com/contextscout/gcn_ner.git
cd gcn_ner
virtualenv --python=/usr/bin/python3 .env
source .env/bin/activate
pip install -r requirements.txt
python -m spacy download en
python -m spacy download en_core_web_mdif you want to install Tensorflow with GPU capabilities please use
pip install -r requirements_gpu.txtTest NER on a text
Execute the file
python test_ner.py < data/random_text.txtTrain NER from a dataset
You will need to put your 'train.conll' into the 'data/' directory, then execute the file
python train.pyTest the dataset F1 score
You will need to put your 'dev.conll' or 'test.conll' into the 'data/' directory, then execute the file
python test_dataset.pyCONLL format
The training/testing conll files must be in the conll format, as in the following example. Only the fourth, fifth, and eleventh columns are used.
source_file_name 1 0 New NNP (TOP(S(NP* - - - Speaker#1 (GPE* * (ARG1* (ARG1* (19
source_file_name 1 1 York NNP *) - - - Speaker#1 *) * *) *) 19)
source_file_name 1 2 was VBD (VP* be 03 - Speaker#1 * (V*) * * -
source_file_name 1 3 developed VBN (VP* develop 02 - Speaker#1 * * (V*) * -
source_file_name 1 4 from IN (PP* - - - Speaker#1 * * (ARG2* * -
source_file_name 1 5 a DT (NP* - - - Speaker#1 * * * * -
source_file_name 1 6 hunting NN * - - - Speaker#1 * * * * -
source_file_name 1 7 harbor NN *)) - - - Speaker#1 * * *) * -
source_file_name 1 8 one CD (ADVP(NP(QP* - - - Speaker#1 (DATE* * (ARGM-TMP* * -
source_file_name 1 9 million CD *) - - - Speaker#1 * * * * -
source_file_name 1 10 years NNS *) - - - Speaker#1 * * * * -
source_file_name 1 11 ago RB *) - - - Speaker#1 *) * *) * -
source_file_name 1 12 to TO (S(VP* - - - Speaker#1 * * (ARGM-PRP* * -
source_file_name 1 13 become VB (VP* become 01 1 Speaker#1 * * * (V*) -
source_file_name 1 14 today NN (NP(NP* - - - Speaker#1 (DATE) * * (ARG2* -
source_file_name 1 15 's POS *) - - - Speaker#1 * * * * -
source_file_name 1 16 international JJ * - - - Speaker#1 * * * * -
source_file_name 1 17 metropolis NNS *)))))) - - - Speaker#1 * * *) *) -
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