Natural Language Understanding (NLP)

https://www.topbots.com/4-different-approaches-natural-language-processing-understanding/

spacy training process

https://course.spacy.io/chapter4


# Start with blank English model nlp = spacy.blank('en') # Create blank entity recognizer and add it to the pipeline ner = nlp.create_pipe('ner') nlp.add_pipe(ner) # Add a new label ner.add_label('GADGET') # Start the training nlp.begin_training() # Train for 10 iterations for itn in range(10): random.shuffle(examples) # Divide examples into batches for batch in spacy.util.minibatch(examples, size=2): texts = [text for text, annotation in batch] annotations = [annotation for text, annotation in batch] # Update the model nlp.update(texts, annotations)