CODEWITHSUNDEEP
pythonpython-3.xnlpspacy
Bram Vanroy
Bram Vanroy

Reputation: 28437

Tag an already tokenised string with spaCy

To make a comparable study, I am working with data that has already been tokenised (not with spacy). I need to use these tokens as input to ensure that I work with the same data across the board. I wish to feed these tokens into spaCy's tagger, but the following fails:

import spacy

nlp = spacy.load('en', disable=['tokenizer', 'parser', 'ner', 'textcat'])
sent = ['I', 'like', 'yellow', 'bananas']

doc = nlp(sent)

for i in doc:
    print(i)

with the following trace

Traceback (most recent call last):
  File "C:/Users/bmvroy/.PyCharm2018.2/config/scratches/scratch_6.py", line 6, in <module>
    doc = nlp(sent)
  File "C:\Users\bmvroy\venv\lib\site-packages\spacy\language.py", line 346, in __call__
    doc = self.make_doc(text)
  File "C:\Users\bmvroy\venv\lib\site-packages\spacy\language.py", line 378, in make_doc
    return self.tokenizer(text)
TypeError: Argument 'string' has incorrect type (expected str, got list)

First of all, I'm not sure why spaCy tries to tokenize the input as I disabled the tokenizer in the load() statement. Second, evidently this is not the way to go.

I am looking for a way to feed the tagger a list of tokens. Is that possible with spaCy?

I tried the solution provided by @aab combined with info from the documentation but to no avail:

from spacy.tokens import Doc
from spacy.lang.en import English
from spacy.pipeline import Tagger

nlp = English()
tagger = Tagger(nlp.vocab)

words = ['Listen', 'up', '.']
spaces = [True, False, False]

doc = Doc(nlp.vocab, words=words, spaces=spaces)
processed = tagger(doc)
print(processed)

This code didn't run, and gave the following error:

    processed = tagger(doc)
  File "pipeline.pyx", line 426, in spacy.pipeline.Tagger.__call__
  File "pipeline.pyx", line 438, in spacy.pipeline.Tagger.predict
AttributeError: 'bool' object has no attribute 'tok2vec'

Upvotes: 2

Views: 1474

Answers (1)

aab
aab

Reputation: 11474

You need to use the alternate way of constructing a document directly using the Doc class. Here's the example from their docs (https://spacy.io/api/doc):

from spacy.tokens import Doc
doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'], spaces=[True, False, False])

The spaces argument (whether each token is followed by a space) is optional.

Then you can run the components you need, so the whole thing would look like:

import spacy
from spacy.tokens import Doc

nlp = spacy.load('en')
doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'], spaces=[True, False, False])

nlp.tagger(doc)
nlp.parser(doc)

for t in doc:
    print(t.text, t.pos_, t.tag_, t.dep_, t.head)

Upvotes: 3

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