Named-Entity Recognition (NER) is one of the most popular NLP tasks. It’s popular because it produces annotation result that can be used directly (eg. extracting people name from text) or indirectly (eg. extracting feature for classification task).
One of the easiest way to do it is by downloading and using latest Stanford Core NLP suite from https://stanfordnlp.github.io/CoreNLP/ to train a NER model using our own dataset. Here are some steps to do it.
First, prepare necessary files:
- Training dataset. List of word tokens annotated with their Named-Entity class (eg.
PERSfor Person entity). Example: jane-austen-emma-ch1 - Training properties. Training cofiguration properties for a CRF classifier (eg. input file(s), output file, features .etc). Example: jane-austen
- Test dataset. Similar to training dataset but with different list of tokens. Example: jane-austen-emma-ch2
If you have datasets in ENAMEX or Open NLP format, you can use these simple python scripts enamex2stanfordner.py or tag2stanfordner.py to convert them
Enter stanfordnlp unzipped directory and run this command to train model:
java -cp "*" edu.stanford.nlp.ie.crf.CRFClassifier -prop jane-austen.prop
The result will show the output model file name ner-model.ser.gz:
.... [main] INFO edu.stanford.nlp.ie.crf.CRFClassifier - CRFClassifier training ... done [2.1 sec]. [main] INFO edu.stanford.nlp.ie.crf.CRFClassifier - Serializing classifier to ner-model.ser.gz... [main] INFO edu.stanford.nlp.ie.crf.CRFClassifier - done.
To test the model against the test file run this command:
java -cp "*" edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier ner-model.ser.gz -testFile jane-austen-emma-ch2.tsv
The result shows the performance of the model. In this case it achieves 82% precision, 72% recall and 77% F-measure:
... [main] INFO edu.stanford.nlp.ie.AbstractSequenceClassifier - CRFClassifier tagged 1999 words in 1 documents at 6305.99 words per second. [main] INFO edu.stanford.nlp.ie.AbstractSequenceClassifier - Entity P R F1 TP FP FN [main] INFO edu.stanford.nlp.ie.AbstractSequenceClassifier - PERS 0.8205 0.7273 0.7711 32 7 12 [main] INFO edu.stanford.nlp.ie.AbstractSequenceClassifier - Totals 0.8205 0.7273 0.7711 32 7 12
You can also train the classifier using for language other than English as long as you provide proper training/testing dataset.
Cheers! ?
References: https://nlp.stanford.edu/software/crf-faq.html
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