CRF, bi-LSTM-CRF for Named Entity Recognition

this is a proof of concept for using various CRF solutions for named entity recognition. the demos here use all-lower-cased text in order to simulate NER on text where case information is not available (e.g. automatic speech recognition output)

June 08 2018 update:

• now train/test split is uniform across models
• use the pycrfsuite report for both models
• added MIT licence for the pycrfsuite code
• removed unneeded/unattributed code, trimmed requirements
• expanded comments
• added results

requirements

gensim
keras
keras-contrib
tensorflow
numpy
pandas
python-crfsuite

to run feature-engineered CRFsuite CRF:

1. run data-preprocessing.ipynb to generate formatted model data
2. run pycrfsuite-training.ipynb to fit model
3. see results/pyCRF-sample.csv for sample output

to run bi-LSTM-CRF

1. run data-preprocessing.ipynb to generate formatted model data
2. run keras_training.ipynb to train and save model
3. run keras-decoding.ipynb to load saved model and decode test sentences
4. see results/keras-biLSTM-CRF_sample.csv for sample output

data

trained on the ConLL-2002 English NER dataset:

https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus

NB: convert to utf-8 first, converted csv is in repository

preprocessing

see: preprocessing.ipynb

1. csv is read
2. word, POS-tag and named entity lists are created by sentence
3. a vocabulary for each input/output type is created
4. sentence words, POS-tags and NE's are integer-indexed as lists
5. data is filtered for only sentences with at least one NE tag
6. data is split into train and test sets
7. all necessary information is saved as numpy binaries

models and training

see: pycrfsuite-training.ipynb

model inputs: word and pos-tag hand-engineered features

model output: named entity tag sequences

see: keras_training.ipynb

model inputs: word and pos-tag integer-indexed sequences (padded)

model output: named entity tag integer-indexed sequences (padded)

decoding

see: keras-decoding.ipynb for code, results/XXXX-sample.csv for sample decode

this file decodes test set results into human-readable format.

adjust the number of outputs to see in the following line:

for sent_idx in range(len(X_test_sents[:500])): << adjust 500 up or down

performance

per-tag results on the withheld test set

py-crfsuite

             precision    recall  f1-score   support

      B-art       0.31      0.06      0.10        69
      I-art       0.00      0.00      0.00        54
      B-eve       0.52      0.35      0.42        46
      I-eve       0.35      0.22      0.27        36
      B-geo       0.85      0.90      0.87      5629
      I-geo       0.81      0.74      0.77      1120
      B-gpe       0.94      0.92      0.93      2316
      I-gpe       0.89      0.65      0.76        26
      B-nat       0.73      0.46      0.56        24
      I-nat       0.60      0.60      0.60         5
      B-org       0.78      0.69      0.73      2984
      I-org       0.77      0.76      0.76      2377
      B-per       0.81      0.81      0.81      2424
      I-per       0.81      0.90      0.85      2493
      B-tim       0.92      0.83      0.87      2989
      I-tim       0.82      0.70      0.75      1017

avg / total       0.83      0.82      0.82     23609

keras biLSTM-CRF

             precision    recall  f1-score   support

      B-art       0.26      0.14      0.18        66
      I-art       0.17      0.07      0.10        54
      B-eve       0.34      0.25      0.29        44
      I-eve       0.20      0.21      0.20        34
      B-geo       0.87      0.90      0.89      5436
      I-geo       0.79      0.83      0.81      1065
      B-gpe       0.96      0.95      0.95      2284
      I-gpe       0.71      0.60      0.65        25
      B-nat       0.58      0.65      0.61        23
      I-nat       1.00      0.40      0.57         5
      B-org       0.80      0.75      0.77      2897
      I-org       0.84      0.77      0.81      2286
      B-per       0.84      0.85      0.84      2396
      I-per       0.84      0.90      0.87      2449
      B-tim       0.90      0.89      0.90      2891
      I-tim       0.84      0.75      0.80       957

avg / total       0.85      0.85      0.85     22912