2
votes

I need to train Spacy NER to be able to recognize 2 new classes for named entity recognition, all I have are files with list of items that are supposed to be in new classes.

For example: Rolling Stones, Muse, Arctic Monkeys - artists Any idea how this can be done?

1

1 Answers

6
votes

This seems like a perfect use case for Matcher or PhraseMatcher (if you care about performance).

import spacy

nlp = spacy.load('en')

def merge_phrases(matcher, doc, i, matches):
    '''
    Merge a phrase. We have to be careful here because we'll change the token indices.
    To avoid problems, merge all the phrases once we're called on the last match.
    '''
    if i != len(matches)-1:
        return None
    spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
    for ent_id, label, span in spans:
        span.merge('NNP' if label else span.root.tag_, span.text, nlp.vocab.strings[label])


matcher = spacy.matcher.Matcher(nlp.vocab)
matcher.add(entity_key='1', label='ARTIST', attrs={}, specs=[[{spacy.attrs.ORTH: 'Rolling'}, {spacy.attrs.ORTH: 'Stones'}]], on_match=merge_phrases)
matcher.add(entity_key='2', label='ARTIST', attrs={}, specs=[[{spacy.attrs.ORTH: 'Muse'}]], on_match=merge_phrases)
matcher.add(entity_key='3', label='ARTIST', attrs={}, specs=[[{spacy.attrs.ORTH: 'Arctic'}, {spacy.attrs.ORTH: 'Monkeys'}]], on_match=merge_phrases)
doc = nlp(u'The Rolling Stones are an English rock band formed in London in 1962. The first settled line-up consisted of Brian Jones, Ian Stewart, Mick Jagger, Keith Richards, Bill Wyman and Charlie Watts')
matcher(doc)
for ent in doc.ents:
  print(ent)

See the documentation for more details. From my experience, with 400k entities in a Matcher it would take almost a second to match each document. PhraseMatcher is much much faster but a bit trickier to use. Note that this is "strict" matcher, it won't match any entities it haven't seen before.