Lemmatisation of contraction 've in English fails

[chrisjbryant]

Small bug, but 've is currently not getting lemmatised as have in spacy 3.6. Other contractions seem unaffected.

>>> nlp = spacy.load("en_core_web_sm")
>>> a = "I can't believe they've not been in touch."
>>> b = nlp(a)
>>> for tok in b:
...     print(tok.text, tok.lemma_)
... 
I I
ca can
n't not
believe believe
they they
've 've
not not
been be
in in
touch touch

[svlandeg]

Hi, thanks for the report! That does look like a bug.

In the more recent trained pipelines, the attribute_ruler takes care of these particular exceptions. You can have a look into them by printing nlp.get_pipe("attribute_ruler").patterns if you're interested.

For instance, for 're, the pipeline does have this correct:

{'patterns': [[{'TAG': 'VBP', 'LOWER': {'IN': ['are', "'re"]}}]], 'attrs': {'LEMMA': 'be', 'POS': 'AUX', 'MORPH': 'Mood=Ind|Tense=Pres|VerbForm=Fin'}, 'index': 0}

But for 've, the LEMMA is missing:

{'patterns': [[{'TAG': 'VBP', 'LOWER': {'IN': ['have', "'ve"]}}]], 'attrs': {'POS': 'AUX', 'MORPH': 'Mood=Ind|Tense=Pres|VerbForm=Fin'}, 'index': 0}

The good news is, that you can fix this in your pipeline by writing to the attribute_ruler's patterns directly, e.g.

nlp = spacy.load("en_core_web_lg")
ruler = nlp.get_pipe("attribute_ruler")

pattern = [{'TAG': 'VBP', 'LOWER': {'IN': ['have', "'ve"]}}]
attrs = {'POS': 'AUX', 'MORPH': 'Mood=Ind|Tense=Pres|VerbForm=Fin', 'LEMMA': 'have'}
ruler.add(patterns=[pattern], attrs=attrs, index=0)

Now, any time 've is tagged as VBP in a sentence, its lemma should be have, as in your example sentence:

I I
ca can
n't not
believe believe
they they
've have
not not
been be
in in
touch touch

We'll also have a look at updating this for the next version of our models!

[adrianeboyd]

This should be fixed in the v3.7.x models.

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