How to get dependency tags/tree in a CoNLL-format?

[redstar12]

How to get dependency tags/tree in a CoNLL-format like this: 1 Bob NOUN NNP 2 nsubj 2 brought VERB VBD 0 ROOT 3 the DET DT 4 det 4 pizza NOUN NN 2 dobj 5 to ADP IN 2 prep 6 Alice NOUN NNP 5 pobj ?

[honnibal]

import spacy

nlp = spacy.load('en', vectors=False)
doc = nlp(u'Bob bought the pizza to Alice')
for sent in doc:
    for i, word in enumerate(sent):
        if word.head is word:
            head_idx = 0
        else:
             head_idx = word.i-sent[0].i+1
        print(
            i+1, # There's a word.i attr that's position in *doc*
            word.pos_, # Coarse-grained tag
            word.tag_, # Fine-grained tag
            head_idx,
            word.dep_, # Relation
            '_', '_')

Should have had this snippet up from the start --- thanks.

[evanmiltenburg]

For common formats, I feel like this should be a method to the doc-object returned by nlp(). So then your snippet would be shortened to:

import spacy

nlp = spacy.load('en', vectors=False)
doc = nlp(u'Bob bought the pizza to Alice')
doc.save_conll('bob_and_alice.conll')

And others could implement methods like save_CoreNLP (Stanford parser XML) and save_conllu (universal dependencies). Another option would be to have the method work like doc.save(bob_and_alice.conll, format='conll'). Either way, the function should at least have the following keywords to include/exclude particular layers (suggested default values in parentheses):

• pos (default True)
• tag (default True)
• deps (default True if available, there should be a check)
• entities (default False)

[evanmiltenburg]

@redstar12 messaged me to say that @honnibal's code is not working for her on Python 2.7. Reproducing it on my machine, the problem seems to be this part:

>>> nlp = spacy.load('en', vectors=False)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Applications/anaconda/envs/python2/lib/python2.7/site-packages/spacy/__init__.py", line 16, in load
    vectors_package = get_package_by_name(vectors, via=via)
  File "/Applications/anaconda/envs/python2/lib/python2.7/site-packages/spacy/util.py", line 41, in get_package_by_name
    lang = get_lang_class(name)
  File "/Applications/anaconda/envs/python2/lib/python2.7/site-packages/spacy/util.py", line 26, in get_lang_class
    lang = re.split('[^a-zA-Z0-9_]', name, 1)[0]
  File "/Applications/anaconda/envs/python2/lib/python2.7/re.py", line 171, in split
    return _compile(pattern, flags).split(string, maxsplit)
TypeError: expected string or buffer

This is easily solved by using the old approach:

from spacy.en import English
nlp = English()
doc = nlp(u'Bob bought the pizza to Alice')
for sent in doc.sents:
    for i, word in enumerate(sent):
        if word.head is word:
            head_idx = 0
        else:
             head_idx = word.i-sent[0].i+1
        print(
            i+1, # There's a word.i attr that's position in *doc*
            word.pos_, # Coarse-grained tag
            word.tag_, # Fine-grained tag
            head_idx,
            word.dep_, # Relation
            '_', '_')

I don't know whether this has been fixed in the meantime (I'm using an older version of SpaCy). If not, then there should be a new GitHub issue to address this.

[redstar12]

I did upgrade and tested all these snippets and I'm getting the error: TypeError: 'spacy.tokens.token.Token' object is not iterable (AttributeError: 'spacy.tokens.doc.Doc' object has no attribute 'save_conll')

[evanmiltenburg]

Updated the code (apparently we forgot to write doc.sents instead of doc). The AttributeError makes sense because it was a feature request rather than a reference to an existing method.

[redstar12]

Thank you! It works. But now I am getting: 1 PUNCT "" 0 ROOT 1 PUNCT "" 0 ROOT 1 PUNCT "" 0 ROOT 1 PUNCT "" 0 ROOT 1 PUNCT "" 0 ROOT 1 PUNCT "" 0 ROOT

[evanmiltenburg]

I got this (in Python 2.7), using the example code as given above.

(1, u'PROPN', u'NNP', 1, u'nsubj', '_', '_')
(2, u'VERB', u'VBD', 0, u'ROOT', '_', '_')
(3, u'DET', u'DT', 3, u'det', '_', '_')
(4, u'NOUN', u'NN', 4, u'dobj', '_', '_')
(5, u'ADP', u'IN', 5, u'prep', '_', '_')
(6, u'PROPN', u'NNP', 6, u'pobj', '_', '_')

So the code is definitely working. Did you change anything? (At some point you should start figuring this out yourself, though. It's your problem..)

[redstar12]

OK! Thank you very much!

[evanmiltenburg]

1. I already told you: doc.save_conll doesn't work because it's not implemented yet. It was just a suggestion to create this function in the future. This will not work for you.
2. I'm not sure about your indentation. But let's assume that's not the problem.
3. I checked it again, in Python 2, and the code really works:

Python 2.7.11 |Continuum Analytics, Inc.| (default, Jun 15 2016, 16:09:16)
[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> from spacy.en import English
>>> nlp = English()
>>> doc = nlp(u'Bob bought the pizza to Alice')
>>> for sent in doc.sents:
...     for i, word in enumerate(sent):
...         if word.head is word:
...             head_idx = 0
...         else:
...              head_idx = word.i-sent[0].i+1
...         print(
...             i+1, # There's a word.i attr that's position in *doc*
...             word.pos_, # Coarse-grained tag
...             word.tag_, # Fine-grained tag
...             head_idx,
...             word.dep_, # Relation
...             '_', '_')
...
(1, u'PROPN', u'NNP', 1, u'nsubj', '_', '_')
(2, u'VERB', u'VBD', 0, u'ROOT', '_', '_')
(3, u'DET', u'DT', 3, u'det', '_', '_')
(4, u'NOUN', u'NN', 4, u'dobj', '_', '_')
(5, u'ADP', u'IN', 5, u'prep', '_', '_')
(6, u'PROPN', u'NNP', 6, u'pobj', '_', '_')
>>>

But now I'm wondering: did you download and install the SpaCy data? If not, do this on the command line: python -m spacy.en.download. Then run the code. Make sure that the indentations are indeed correct.

If that doesn't work, then please try to investigate where the code breaks down. Just coming here and saying "it doesn't work" is not good enough. Try to see if other things do work, e.g. tagging:

for token in doc:
    print('\t'.join(token.orth_, token.pos_, token.tag_))

[redstar12]

Thank you very much for your reply. I don't know why but we had a problem with parsing after upgrade. We reinstalled Spacy and now the code works. BUT! As you can see, the HEAD value is wrong. It is the same as ID value:

(1, u'PROPN', u'NNP', 1, u'nsubj', '', '') (2, u'VERB', u'VBD', 0, u'ROOT', '', '') (3, u'DET', u'DT', 3, u'det', '', '') (4, u'NOUN', u'NN', 4, u'dobj', '', '') (5, u'ADP', u'IN', 5, u'prep', '', '') (6, u'PROPN', u'NNP', 6, u'pobj', '', '')

And it has to be like this:

(1, u'PROPN', u'NNP', 2, u'nsubj', '', '') (2, u'VERB', u'VBD', 0, u'ROOT', '', '') (3, u'DET', u'DT', 4, u'det', '', '') (4, u'NOUN', u'NN', 2, u'dobj', '', '') (5, u'ADP', u'IN', 2, u'prep', '', '') (6, u'PROPN', u'NNP', 5, u'pobj', '', '')

[evanmiltenburg]

Ok, then there was a small mistake in the code @honnibal wrote. I don't have time to fix it. All I wanted to say in this thread was that it'd be nice to have a method for the parsed document to save it in CONLL format.

Hints I can give you to fix the code:

• Each token has a token.i attribute that gives you the index.
• Each token has a token.head attribute that gives you the head token (which also has the i attribute).

[redstar12]

Thank you very much for your hints. I fixed the code. It works!!! Thank you!

[mosynaq]

Here's the code that works for me:

doc = nlp(u'Bob bought the pizza to Alice')
for sent in doc.sents:
     for i, word in enumerate(sent):
         if word.head is word:
             head_idx = 0
         else:
            head_idx = doc[i].head.i+1

         print("%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s"%(
             i+1, # There's a word.i attr that's position in *doc*
             word,
             '_',
             word.pos_, # Coarse-grained tag
             word.tag_, # Fine-grained tag
             '_',
             head_idx,
             word.dep_, # Relation
             '_', '_'))

And the output:

1   Bob _   PROPN   NNP _   2   nsubj   _   _
2   bought  _   VERB    VBD _   0   ROOT    _   _
3   the _   DET DT  _   4   det _   _
4   pizza   _   NOUN    NN  _   2   dobj    _   _
5   to  _   ADP IN  _   2   prep    _   _
6   Alice   _   PROPN   NNP _   5   pobj    _   _

You can test the output here (online) or use this (offline, .Net-based).

Sample output visualization from the latter:

[GruffPrys]

@mosynaq #1215 might be of interest to you for testing output in displaCy :)

[Nou2017]

is it possible to get the same result using MaltParser and Python?

[flackbash]

The computation of the head id is not entirely correct in either one of the code snippets. This is how it should be:

if word.head is word:
    head_idx = 0
else:
    # this is the corrected line:
    head_idx = word.head.i - sent[0].i + 1

Aside from that the comments here were really helpful, thanks!

[Nou2017]

It works with French sentences?

[Imane0]

@flackbash Could you please explain why you add "- sent[0].i + 1" ? Why isn't word.head.i enough ?

[lock[bot]]

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.