This module allows you to parse text into 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 pipeline component to a spaCy, spacy-stanza, or spacy-udpipe pipeline. It
also provides an easy-to-use function to quickly initialize a parser as well as a ConllParser class with built-in
functionality to parse files or text.
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-stanza, which is a spaCy
interface using stanza and its models behind the scenes. Those models use the Universal Dependencies formalism and
yield state-of-the-art performance. stanza is a new and improved version of stanfordnlp. As an alternative to the
Stanford models, you can use the spaCy wrapper for UDPipe, spacy-udpipe,
which is slightly less accurate than stanza but much faster.
By default, this package automatically installs only spaCy as
dependency. Because spaCy's models are not necessarily trained on Universal
Dependencies conventions, their output labels are not UD either. By using spacy-stanza or spacy-udpipe, we get
the easy-to-use interface of spaCy as a wrapper around stanza and UDPipe respectively, including their models that
are trained on UD data.
NOTE: spacy-stanza and spacy-udpipe are not installed automatically 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 their functionality, you have to install
them manually or use one of the available options as described below.
If you want to retrieve CoNLL info as a pandas DataFrame, this library will automatically export it if it detects
that pandas is installed. See the Usage section for more.
To install the library, simply use pip.
# only includes spacy by default
pip install spacy_conllA number of options are available to make installation of additional dependencies easier:
# include spacy-stanza and spacy-udpipe
pip install spacy_conll[parsers]
# include pandas
pip install spacy_conll[pd]
# include pandas, spacy-stanza and spacy-udpipe
pip install spacy_conll[all]
# include pandas, spacy-stanza and spacy-udpipe and additional libaries for testing and formatting
pip install spacy_conll[dev]When the ConllFormatter is added to a spaCy pipeline, it adds CoNLL properties for Token, sentence Span and Doc.
Note that arbitrary Span's are not included and do not receive these properties.
On all three of these levels, two custom properties are exposed by default, ._.conll and its string
representation ._.conll_str. However, if you have pandas installed, then ._.conll_pd will
be added automatically, too!
-
._.conll: raw CoNLL format- in Token: a dictionary containing all the expected CoNLL fields as keys and the parsed properties as values.
- in sentence Span: a list of its tokens'
._.conlldictionaries (list of dictionaries). - in a Doc: a list of its sentences'
._.conlllists (list of list of dictionaries).
-
._.conll_str: string representation of the CoNLL format- in Token: tab-separated representation of the contents of the CoNLL fields ending with a newline.
- in sentence Span: the expected CoNLL format where each row represents a token. When
ConllFormatter(include_headers=True)is used, two header lines are included as well, as per the CoNLL format. - in Doc: all its sentences'
._.conll_strcombined and separated by new lines.
-
._.conll_pd:pandasrepresentation of the CoNLL format- in Token: a Series representation of this token's CoNLL properties.
- in sentence Span: a DataFrame representation of this sentence, with the CoNLL names as column headers.
- in Doc: a concatenation of its sentences' DataFrame's, leading to a new a DataFrame whose index is reset.
You can use spacy_conll in your own Python code as a custom pipeline component, or you can use the built-in
command-line script which offers typically needed functionality. See the following section for more.
This library offers the ConllFormatter class which serves as a custom spaCy pipeline component. It can be instantiated
as follows. It is important that you import spacy_conll before adding the pipe!
import spacy
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("conll_formatter", last=True)Because this library supports different spaCy wrappers (spacy, stanza, and udpipe), a convenience function is
available as well. With utils.init_parser you can easily instantiate a parser with a single line. You can
find the function's signature below. Have a look at the source code to read more about all the
possible arguments or try out the examples.
NOTE: is_tokenized does not work for spacy-udpipe. Using is_tokenized for spacy-stanza also affects sentence
segmentation, effectively only splitting on new lines. With spacy, is_tokenized disables sentence splitting completely.
def init_parser(
model_or_lang: str,
parser: str,
*,
is_tokenized: bool = False,
disable_sbd: bool = False,
exclude_spacy_components: Optional[List[str]] = None,
parser_opts: Optional[Dict] = None,
**kwargs,
)For instance, if you want to load a Dutch stanza model in silent mode with the CoNLL formatter already attached, you
can simply use the following snippet. parser_opts is passed to the stanza pipeline initialisation automatically.
Any other keyword arguments (kwargs), on the other hand, are passed to the ConllFormatter initialisation.
from spacy_conll import init_parser
nlp = init_parser("nl", "stanza", parser_opts={"verbose": False})The ConllFormatter allows you to customize the extension names, and you can also specify conversion maps for the
output properties.
To illustrate, here is an advanced example, showing the more complex options:
ext_names: changes the attribute names to a custom key by using a dictionary.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 alternative0.field_names: allows you to change the default CoNLL-U field names to your own custom names. Similar to the conversion map above, you should use any of the default field names as keys and add your own key as value. Possible keys are : "ID", "FORM", "LEMMA", "UPOS", "XPOS", "FEATS", "HEAD", "DEPREL", "DEPS", "MISC".
The example below
- shows how to manually add the component;
- changes the custom attribute
conll_pdto pandas (conll_pdonly availabe ifpandasis installed); - converts any
nsubjdeprel tag tosubj.
import spacy
nlp = spacy.load("en_core_web_sm")
config = {"ext_names": {"conll_pd": "pandas"},
"conversion_maps": {"deprel": {"nsubj": "subj"}}}
nlp.add_pipe("conll_formatter", config=config, last=True)
doc = nlp("I like cookies.")
print(doc._.pandas)This is the same as:
from spacy_conll import init_parser
nlp = init_parser("en_core_web_sm",
"spacy",
ext_names={"conll_pd": "pandas"},
conversion_maps={"deprel": {"nsubj": "subj"}})
doc = nlp("I like cookies.")
print(doc._.pandas)The snippets above will output a pandas DataFrame by using ._.pandas rather than the standard
._.conll_pd, and all occurrences of nsubj in the deprel field are replaced by subj.
ID FORM LEMMA UPOS XPOS FEATS HEAD DEPREL DEPS MISC
0 1 I I PRON PRP Case=Nom|Number=Sing|Person=1|PronType=Prs 2 subj _ _
1 2 like like VERB VBP Tense=Pres|VerbForm=Fin 0 ROOT _ _
2 3 cookies cookie NOUN NNS Number=Plur 2 dobj _ SpaceAfter=No
3 4 . . PUNCT . PunctType=Peri 2 punct _ SpaceAfter=No
Another initialization example that would replace the column names "UPOS" with "upostag" amd "XPOS" with "xpostag":
import spacy
nlp = spacy.load("en_core_web_sm")
config = {"field_names": {"UPOS": "upostag", "XPOS": "xpostag"}}
nlp.add_pipe("conll_formatter", config=config, last=True)It is possible to read a CoNLL string or text file and parse it as a spaCy object. This can be useful if you have raw CoNLL data that you wish to process in different ways. The process is straightforward.
from spacy_conll import init_parser
from spacy_conll.parser import ConllParser
nlp = ConllParser(init_parser("en_core_web_sm", "spacy"))
doc = nlp.parse_conll_file_as_spacy("path/to/your/conll-sample.txt")
'''
or straight from raw text:
conllstr = """
# text = From the AP comes this story :
1 From from ADP IN _ 3 case 3:case _
2 the the DET DT Definite=Def|PronType=Art 3 det 3:det _
3 AP AP PROPN NNP Number=Sing 4 obl 4:obl:from _
4 comes come VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 0 root 0:root _
5 this this DET DT Number=Sing|PronType=Dem 6 det 6:det _
6 story story NOUN NN Number=Sing 4 nsubj 4:nsubj _
"""
doc = nlp.parse_conll_text_as_spacy(conllstr)
'''
# Multiple CoNLL entries (separated by two newlines) will be included as different sentences in the resulting Doc
for sent in doc.sents:
for token in sent:
print(token.text, token.dep_, token.pos_)Upon installation, a command-line script is added under tha alias parse-as-conll. You can use it to parse a
string or file into CoNLL format given a number of options.
parse-as-conll -h
usage: parse-as-conll [-h] [-f INPUT_FILE] [-a INPUT_ENCODING] [-b INPUT_STR] [-o OUTPUT_FILE]
[-c OUTPUT_ENCODING] [-s] [-t] [-d] [-e] [-j N_PROCESS] [-v]
[--ignore_pipe_errors] [--no_split_on_newline]
model_or_lang {spacy,stanza,udpipe}
Parse an input string or input file to CoNLL-U format using a spaCy-wrapped parser. The output
can be written to stdout or a file, or both.
positional arguments:
model_or_lang Model or language to use. SpaCy models must be pre-installed, stanza
and udpipe models will be downloaded automatically
{spacy,stanza,udpipe}
Which parser to use. Parsers other than 'spacy' need to be installed
separately. For 'stanza' you need 'spacy-stanza', and for 'udpipe' the
'spacy-udpipe' library is required.
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. (default:
cp1252)
-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. (default:
cp1252)
-s, --disable_sbd Whether to disable spaCy automatic sentence boundary detection. In
practice, disabling means that every line will be parsed as one
sentence, regardless of its actual content. When 'is_tokenized' is
enabled, 'disable_sbd' is enabled automatically (see 'is_tokenized').
Only works when using 'spacy' as 'parser'. (default: False)
-t, --is_tokenized Whether your text has already been tokenized (space-seperated). Setting
this option has as an important consequence that no sentence splitting
at all will be done except splitting on new lines. So if your input is
a file, and you want to use pretokenised text, make sure that each line
contains exactly one sentence. (default: False)
-d, --include_headers
Whether to include headers before the output of every sentence. These
headers include the sentence text and the sentence ID as per the CoNLL
format. (default: False)
-e, --no_force_counting
Whether 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. Might not work for a 'parser' other than 'spacy' depending
on your environment. (default: 1)
-v, --verbose Whether to always print the output to stdout, regardless of
'output_file'. (default: False)
--ignore_pipe_errors Whether to ignore a priori errors concerning 'n_process' By default we
try to determine whether processing works on your system and stop
execution if we think it doesn't. If you know what you are doing, you
can ignore such pre-emptive errors, though, and run the code as-is,
which will then throw the default Python errors when applicable.
(default: False)
--no_split_on_newline
By default, the input file or string is split on newlines for faster
processing of the split up parts. If you want to disable that behavior,
you can use this flag. (default: False)For example, parsing a single line, multi-sentence string:
parse-as-conll en_core_web_sm spacy --input_str "I like cookies. What about you?" --include_headers
# sent_id = 1
# text = I like cookies.
1 I I PRON PRP Case=Nom|Number=Sing|Person=1|PronType=Prs 2 nsubj _ _
2 like like VERB VBP Tense=Pres|VerbForm=Fin 0 ROOT _ _
3 cookies cookie NOUN NNS Number=Plur 2 dobj _ SpaceAfter=No
4 . . PUNCT . PunctType=Peri 2 punct _ _
# sent_id = 2
# text = What about you?
1 What what PRON WP _ 2 dep _ _
2 about about ADP IN _ 0 ROOT _ _
3 you you PRON PRP Case=Acc|Person=2|PronType=Prs 2 pobj _ SpaceAfter=No
4 ? ? PUNCT . PunctType=Peri 2 punct _ SpaceAfter=NoFor example, parsing a large input file and writing output to a given output file, using four processes:
parse-as-conll en_core_web_sm spacy --input_file large-input.txt --output_file large-conll-output.txt --include_headers --disable_sbd -j 4The first version of this library was inspired by initial work by rgalhama and has evolved a lot since then.