label keyword argument in Span.merge has no effect

I am trying to add custom entities via add_entity API. I’d like to be able to specify their entity types. It does not appear that the label keyword in add_entity is doing anything.

(The following description looks long, but is the same code block repeated with minor changes.)

If I use the snippet provided in issue #523:

def merge_phrase(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
    # Get Span objects
    spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
    for ent_id, label, span in spans:
        span.merge(label=label, tag='NNP' if label else span.root.tag_)

nlp = spacy.load('en')
nlp.matcher.add_entity('MorganStanley', on_match=merge_phrase)
nlp.matcher.add_pattern('MorganStanley', [{'orth': 'Morgan'}, {'orth': 'Stanley'}], label='ORG')
nlp.pipeline = [nlp.tagger, nlp.entity, nlp.matcher, nlp.parser]

It looks promising:

# Okay, now we've got our pipeline set up...
doc = nlp(u'Morgan Stanley fires Vice President')
for word in doc:
    print(word.text, word.tag_, word.dep_, word.head.text, word.ent_type_)

Morgan Stanley NNP amod fires ORG
fires NNS ROOT fires 
Vice NNP compound President 
President NNP appos fires 

However, I’m not sure the label='ORG' is actually doing anything. If I remove it, I get the same output.

nlp = spacy.load('en')
nlp.matcher.add_entity('MorganStanley', on_match=merge_phrase)
nlp.matcher.add_pattern('MorganStanley', [{'orth': 'Morgan'}, {'orth': 'Stanley'}])
nlp.pipeline = [nlp.tagger, nlp.entity, nlp.matcher, nlp.parser]

doc = nlp(u'Morgan Stanley fires Vice President')
for word in doc:
    print(word.text, word.tag_, word.dep_, word.head.text, word.ent_type_)

Morgan Stanley NNP amod fires ORG
fires NNS ROOT fires 
Vice NNP compound President 
President NNP appos fires

In fact, anything of the form ABC [Brothers/Limited/Company/Bank] gets labeled as an ‘ORG’. And I can’t get other patterns to be labeled ‘ORG’. E.g.:

nlp = spacy.load('en')
nlp.matcher.add_entity('MorganStanley', on_match=merge_phrase)
nlp.matcher.add_pattern('MorganStanley', [{'orth': 'State'}, {'orth': 'Street'}], label='ORG')
nlp.pipeline = [nlp.tagger, nlp.entity, nlp.matcher, nlp.parser]

doc = nlp(u'State Street fires Vice President')
for word in doc:
    print(word.text, word.tag_, word.dep_, word.head.text, word.ent_type_)

State Street NNP compound fires 
fires NNS ROOT fires 
Vice NNP compound President 
President NNP appos fires 

The .label_ property of doc.ents is always ''.

How do I set the entity label? And what is the difference between doc.ents[0].label_ and doc[0].ent_type_?

• Operating System: Debian 8
• Python Version Used: 3.5
• spaCy Version Used: 1.6

[dlmiyamoto]

It doesn’t seem that the label kwarg in the span.merge of the callback does anything. span.merge passes **attributes to doc.merge, but doc.merge doesn’t seem to do anything with a label kwarg. Is this a result of outdated documentation?

[lock[bot]]

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