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A custom pipeline component for spaCy that can convert any parsed Doc and its sentences into CoNLL-U format. Also provides a command line entry point.

Project description

Parsing to CoNLL with spaCy or spacy-stanfordnlp

This module allows you to parse a text to CoNLL-U format. You can use it as a command line tool, or embed it in your own scripts by adding it as a custom component to a spaCy or spacy-stanfordnlp pipeline.

Note that the module simply takes a parser’s output and puts it in a formatted string adhering to the linked ConLL-U format. The output tags depend on the spaCy model used. If you want Universal Depencies tags as output, I advise you to use this library in combination with spacy_stanfordnlp, which is a spaCy interface using stanfordnlp and its models behind the scenes. Those models use the Universal Dependencies formalism. See the remainder README for more information and usage guidelines.

Installation

Requires spaCy and an installed spaCy language model. When using the module from the command line, you also need the packaging package. See section spaCy for usage.

Because spaCy’s models are not necessarily trained on Universal Dependencies conventions, their output labels are not UD either. By using spacy-stanfordnlp, we get the easy-to-use interface of spaCy as a wrapper around stanfordnlp and its models that are trained on UD data. If you want to use the Stanford NLP models, you also need spacy-stanfordnlp and a corresponding model. See the section spacy-stanfordnlp for usage.

NOTE: spacy-stanfordnlp is not automatically installed as a dependency for this library, because it might be too much overhead for those who don’t need UD. If you wish to use its functionality, you have to install it manually. By default, only spacy and packaging are installed as dependencies.

To install the library, simply use pip.

pip install spacy_conll

Usage

Command line

> python -m spacy_conll -h
usage: [-h] [-f INPUT_FILE] [-a INPUT_ENCODING] [-b INPUT_STR]
                   [-o OUTPUT_FILE] [-c OUTPUT_ENCODING] [-m MODEL_OR_LANG]
                   [-s] [-t] [-d] [-e] [-j N_PROCESS] [-u] [-v]

Parse an input string or input file to CoNLL-U format.

optional arguments:
  -h, --help            show this help message and exit
  -f INPUT_FILE, --input_file INPUT_FILE
                        Path to file with sentences to parse. Has precedence
                        over 'input_str'. (default: None)
  -a INPUT_ENCODING, --input_encoding INPUT_ENCODING
                        Encoding of the input file. Default value is system
                        default.
  -b INPUT_STR, --input_str INPUT_STR
                        Input string to parse. (default: None)
  -o OUTPUT_FILE, --output_file OUTPUT_FILE
                        Path to output file. If not specified, the output will
                        be printed on standard output. (default: None)
  -c OUTPUT_ENCODING, --output_encoding OUTPUT_ENCODING
                        Encoding of the output file. Default value is system
                        default.
  -m MODEL_OR_LANG, --model_or_lang MODEL_OR_LANG
                        spaCy or stanfordnlp model or language to use (must be
                        installed). (default: None)
  -s, --disable_sbd     Disables spaCy automatic sentence boundary detection.
                        In practice, disabling means that every line will be
                        parsed as one sentence, regardless of its actual
                        content. Only works when using spaCy. (default: False)
  -t, --is_tokenized    Indicates whether your text has already been tokenized
                        (space-seperated). When used in conjunction with
                        spacy-stanfordnlp, it will also be assumed that the
                        text is sentence split by newline. (default: False)
  -d, --include_headers
                        To include headers before the output of every
                        sentence. These headers include the sentence text and
                        the sentence ID. (default: False)
  -e, --no_force_counting
                        To disable force counting the 'sent_id', starting from
                        1 and increasing for each sentence. Instead, 'sent_id'
                        will depend on how spaCy returns the sentences. Must
                        have 'include_headers' enabled. (default: False)
  -j N_PROCESS, --n_process N_PROCESS
                        Number of processes to use in nlp.pipe(). -1 will use
                        as many cores as available. Requires spaCy v2.2.2.
                        (default: 1)
  -u, --use_stanfordnlp
                        Use stanfordnlp models rather than spaCy models.
                        Requires spacy-stanfordnlp. (default: False)
  -v, --verbose         To print the output to stdout, regardless of
                        'output_file'. (default: False)

For example, parsing a single line, multi-sentence string:

>  python -m spacy_conll --input_str "I like cookies . What about you ?" --is_tokenized --include_headers
# sent_id = 1
# text = I like cookies .
1       I       -PRON-  PRON    PRP     PronType=prs    2       nsubj   _       _
2       like    like    VERB    VBP     VerbForm=fin|Tense=pres 0       ROOT    _       _
3       cookies cookie  NOUN    NNS     Number=plur     2       dobj    _       _
4       .       .       PUNCT   .       PunctType=peri  2       punct   _       _

# sent_id = 2
# text = What about you ?
1       What    what    NOUN    WP      PronType=int|rel        2       dep     _       _
2       about   about   ADP     IN      _       0       ROOT    _       _
3       you     -PRON-  PRON    PRP     PronType=prs    2       pobj    _       _
4       ?       ?       PUNCT   .       PunctType=peri  2       punct   _       _

For example, parsing a large input file and writing output to output file, using four processes:

> python -m spacy_conll --input_file large-input.txt --output_file large-conll-output.txt --include_headers --disable_sbd -j 4

You can also use Stanford NLP’s models to retrieve UD tags. You can do this by using the -u flag. NOTE: Using Stanford’s models has limited options due to the API of stanfordnlp. It is not possible to disable sentence segmentation and control the tokenisation at the same time. When using the -u flag you can only enable the --is_tokenized flag which behaves different when used with spaCy. With spaCy, it will simply not try to tokenize the text and use spaces as token separators. When using spacy-stanfordnlp, it will also be assumed that the text is sentence split by newline. No further sentence segmentation is done.

In Python

spaCy

spacy_conll is intended to be used a custom pipeline component in spaCy. Three custom extensions are accessible, by default named conll_str, conll_str_headers, and conll.

  • conll_str: returns the string representation of the CoNLL format
  • conll_str_headers: returns the string representation of the CoNLL format including headers. These headers consist of two lines, namely # sent_id = <i>, indicating which sentence it is in the overall document, and # text = <sentence>, which simply shows the original sentence’s text
  • conll: returns the output as (a list of) tuple(s) where each line is a tuple of its column values

When adding the component to the spaCy pipeline, it is important to insert it after the parser, as shown in the example below.

import spacy
from spacy_conll import ConllFormatter

nlp = spacy.load('en')
conllformatter = ConllFormatter(nlp)
nlp.add_pipe(conllformatter, after='parser')
doc = nlp('I like cookies. Do you?')
print(doc._.conll_str_headers)

The snippet above will return (and print) the following string:

# sent_id = 1
# text = I like cookies.
1   I       -PRON-  PRON    PRP     PronType=prs    2       nsubj   _       _
2   like    like    VERB    VBP     VerbForm=fin|Tense=pres 0       ROOT    _       _
3   cookies cookie  NOUN    NNS     Number=plur     2       dobj    _       _
4   .       .       PUNCT   .       PunctType=peri  2       punct   _       _

# sent_id = 2
# text = Do you?
1   Do      do      AUX     VBP     VerbForm=fin|Tense=pres 0       ROOT    _       _
2   you     -PRON-  PRON    PRP     PronType=prs    1       nsubj   _       _
3   ?       ?       PUNCT   .       PunctType=peri  1       punct   _       _

An advanced example, showing the more complex options:

  • ext_names: changes the attribute names to a custom key by using a dictionary. You can change:
    • conll_str: a string representation of the CoNLL format
    • conll_str_headers: the same as conll_str but with leading lines containing sentence index and sentence text
    • conll: a dictionary containing the field names and their values. For a Doc object, this returns a list of dictionaries where each dictionary is a sentence
  • field_names: a dictionary containing a mapping of field names so that you can name them as you wish
  • conversion_maps: a two-level dictionary that looks like {field_name: {tag_name: replacement}}. In other words, you can specify in which field a certain value should be replaced by another. This is especially useful when you are not satisfied with the tagset of a model and wish to change some tags to an alternative

The example below

  • changes the custom attribute conll to connl_for_pd;
  • changes the lemma field to word_lemma;
  • converts any -PRON- to PRON;
  • as a bonus: uses the output dictionary to create a pandas DataFrame.
import pandas as pd
import spacy
from spacy_conll import ConllFormatter


nlp = spacy.load('en')
conllformatter = ConllFormatter(nlp,
                                ext_names={'conll': 'connl_for_pd'},
                                field_names={'lemma': 'word_lemma'},
                                conversion_maps={'word_lemma': {'-PRON-': 'PRON'}})
nlp.add_pipe(conllformatter, after='parser')
doc = nlp('I like cookies.')
df = pd.DataFrame.from_dict(doc._.connl_for_pd[0])
print(df)

The snippet above will output a pandas DataFrame:

   id     form word_lemma upostag  ... head deprel  deps misc
0   1        I       PRON    PRON  ...    2  nsubj     _    _
1   2     like       like    VERB  ...    0   ROOT     _    _
2   3  cookies     cookie    NOUN  ...    2   dobj     _    _
3   4        .          .   PUNCT  ...    2  punct     _    _

[4 rows x 10 columns]

spacy-stanfordnlp

Using spacy_conll in conjunction with spacy-stanfordnlp is similar to using it with spacy: in practice we are still simply adding a custom component pipeline to the existing pipeline, but this time that pipeline is a Stanford NLP pipeline that is wrapped in spaCy’s API.

from spacy_stanfordnlp import StanfordNLPLanguage
import stanfordnlp

from spacy_conll import ConllFormatter


snlp = stanfordnlp.Pipeline(lang='en')
nlp = StanfordNLPLanguage(snlp)
conllformatter = ConllFormatter(nlp)
nlp.add_pipe(conllformatter, last=True)

s = 'A cookie is a baked or cooked food that is typically small, flat and sweet.'

doc = nlp(s)
print(doc._.conll_str)

Output:

1   A       a       DET     DT      _       2       det     _       _
2   cookie  cookie  NOUN    NN      Number=sing     8       nsubj   _       _
3   is      be      AUX     VBZ     VerbForm=fin|Tense=pres|Number=sing|Person=three        8       cop     _       _
4   a       a       DET     DT      _       8       det     _       _
5   baked   bake    VERB    VBN     VerbForm=part|Tense=past|Aspect=perf    8       amod    _       _
6   or      or      CCONJ   CC      ConjType=comp   7       cc      _       _
7   cooked  cook    VERB    VBN     VerbForm=part|Tense=past|Aspect=perf    5       conj    _       _
8   food    food    NOUN    NN      Number=sing     0       root    _       _
9   that    that    PRON    WDT     _       12      nsubj   _       _
10  is      be      AUX     VBZ     VerbForm=fin|Tense=pres|Number=sing|Person=three        12      cop     _       _
11  typically       typically       ADV     RB      Degree=pos      12      advmod  _       _
12  small   small   ADJ     JJ      Degree=pos      8       acl:relcl       _       _
13  ,       ,       PUNCT   ,       PunctType=comm  14      punct   _       _
14  flat    flat    ADJ     JJ      Degree=pos      12      conj    _       _
15  and     and     CCONJ   CC      ConjType=comp   16      cc      _       _
16  sweet   sweet   ADJ     JJ      Degree=pos      12      conj    _       _
17  .       .       PUNCT   .       PunctType=peri  8       punct   _       _

DEPRECATED: Spacy2ConllParser

There are two main methods, parse() and parseprint(). The latter is a convenience method for printing the output of parse() to stdout (default) or a file.

from spacy_conll import Spacy2ConllParser
spacyconll = Spacy2ConllParser()

# `parse` returns a generator of the parsed sentences
for parsed_sent in spacyconll.parse(input_str="I like cookies.\nWhat about you?\nI don't like 'em!"):
    do_something_(parsed_sent)

# `parseprint` prints output to stdout (default) or a file (use `output_file` parameter)
# This method is called when using the command line
spacyconll.parseprint(input_str='I like cookies.')

Credits

Based on the initial work by rgalhama.

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