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Spacy Matcher not working on long word combination

See original GitHub issue

I had text

text = "What factors is AdvisorShares STAR Global Buy-Write ETF exposed with?"
doc = nlp(text)
matcher(doc)

and add matcher by

matcher.add(entity_key='company-1', label='COMPANY', attrs={}, specs=spec, on_match=merge_phrases)

#[[{65: ‘AdvisorShares’}, {65: ‘STAR’}, {65: ‘Global’}, {65: ‘Buy-Write’}, {65: ‘ETF’}], [{65: ‘Advisorshares’}, {65: ‘Star’}, {65: ‘Global’}, {65: ‘Buy-Write’}, {65: ‘Etf’}]]

But system can’t recognize AdvisorShares STAR Global Buy-Write ETF as COMPANY type of entity

Issue Analytics

State:
Created 6 years ago
Comments:6 (2 by maintainers)

Top GitHub Comments

1reaction

Gregory-Howardcommented, Apr 11, 2017

Hi, see https://spacy.io/docs/usage/customizing-tokenizer for having some special cases for tokenization. Not tested but something like this can do the job :

nlp.tokenizer.add_special_case(u'Buy-Write',
    [
        {
            ORTH: u'Buy-Write',
            LEMMA: u'buy-write',
            POS: u'NOUN'}
       ])

0reactions

lock[bot]commented, May 8, 2018

This thread has been automatically locked since there has not been any recent activity after it was closed. Please open a new issue for related bugs.