def process_spacy_and_convert_to_naf(self,
nlp,
dct, # in a next iteration, we can make this a class attribute
layers,
output_path=None):
"""
process with spacy and convert to NAF
:param nlp: spacy language model
:param datetime.datetime dct: document creation time
:param set layers: layers to convert to NAF, e.g., {'raw', 'text', 'terms'}
:param output_path: if provided, NAF is saved to that file
:return: the root of the NAF XML object
"""
root = spacy_to_naf.text_to_NAF(text=self.content,
nlp=nlp,
dct=dct,
layers=layers,
title=self.name,
uri=self.uri,
language=self.language)
if output_path is not None:
with open(output_path, 'w') as outfile:
outfile.write(spacy_to_naf.NAF_to_string(NAF=root))
return root
def text_to_naf(wiki_title,
target_languages,
text,
wiki_uri,
annotations,
prefix,
language,
nlp,
dct,
output_folder=None,
wiki_langlinks={},
verbose=0):
assert language in target_languages, f'{language} not part of supported languages: {" ".join(target_languages)}'
# parse with spaCy
add_mw = False
if language in {'en', 'nl'}:
add_mw = True
try:
naf = spacy_to_naf.text_to_NAF(text=text,
nlp=nlp,
dct=dct,
layers={'raw', 'text', 'terms', 'deps'},
naf_version='v3.1',
title=wiki_title,
uri=wiki_uri,
language=language,
add_mws=add_mw)
assert naf.find('raw').text == text, f'mismatch between raw text JSON and NAF file'
except:
return
# add hyperlinks as entity elements
add_hyperlinks(naf,
annotations,
prefix,
language,
dct,
wiki_langlinks=wiki_langlinks)
# if wanted, write output to disk
if output_folder is not None:
if not os.path.exists(output_folder):
os.mkdir(output_folder)
lang_dir = os.path.join(output_folder, language)
if not os.path.exists(lang_dir):
os.mkdir(lang_dir)
output_path = os.path.join(lang_dir, f'{wiki_title}.naf')
spacy_to_naf.NAF_to_file(naf, output_path)
if verbose >= 3:
print(f'saved to {output_path}')
return naf
def fileread():
for filename in os.listdir(path + "rawTest"):
if filename.endswith(".txt"):
orig = open(path + "rawTest/" + filename, "r", encoding="utf8")
f = open(filename.replace(".txt", ".naf"), "w", encoding="utf8")
text = ""
for line in orig.read():
text += line
#datetime.datetime.now() may just be datetime.now() depending on python version
NAF = spacy_to_naf.text_to_NAF(
text,
nlp,
dct=datetime.datetime.now(),
layers={'raw', 'text', 'terms', 'entities', 'deps', 'chunks'})
f.write(spacy_to_naf.NAF_to_string(NAF))
f.close()
def process_first_x_files(path_signalmedia_json,
path_newsreader_nafs='',
start=None,
end=None):
"""
create generator of json objects (representing signalmedia articles)
:param str path_signalmedia_json: path to all signalmedia article in jsonl
(originally called signalmedia-1m.jsonl
:param str path_newsreader_nafs: path to where signalmedia processed
with pipeline is stored in NAF
:param int start: start line
:param int end: end line
:rtype: generator
:return: generator of json objects
"""
if end:
line_range = range(start, end + 1)
news_item = namedtuple('news_item', ['signalmedia_json', 'preprocessing'])
path_template = '{path_newsreader_nafs}/{identifier}.in.naf'
with open(path_signalmedia_json) as infile:
for counter, line in enumerate(infile, 1):
if end:
if counter not in line_range:
continue
if counter > end:
break
article = json.loads(line)
identifier = article['id']
spacy_naf = spacy_to_naf.text_to_NAF(article['content'], nlp)
the_preprocessing = {('spacy', spacy_naf)}
if path_newsreader_nafs:
path_newsreader_naf = path_template.format_map(locals())
if os.path.exists(path_newsreader_naf):
newsreader_naf = etree.parse(path_newsreader_naf)
the_preprocessing.add(('newsreader', newsreader_naf))
a_news_item = news_item(signalmedia_json=article,
preprocessing=the_preprocessing)
yield a_news_item
def text_to_naf(wiki_title,
text,
wiki_uri,
annotations,
prefix,
language,
nlp,
dct,
output_folder=None):
assert language in {
'nl', 'en', 'it'
}, f'{language} not part of supported languages: nl it en'
# parse with spaCy
naf = spacy_to_naf.text_to_NAF(text=text,
nlp=nlp,
dct=dct,
layers={'raw', 'text', 'terms'},
title=wiki_title,
uri=wiki_uri,
language=language)
assert naf.find(
'raw').text == text, f'mismatch between raw text JSON and NAF file'
# add hyperlinks as entity elements
add_hyperlinks(naf, annotations, prefix)
# if wanted, write output to disk
if output_folder is not None:
if not os.path.exists(output_folder):
os.mkdir(output_folder)
lang_dir = os.path.join(output_folder, language)
if not os.path.exists(lang_dir):
os.mkdir(lang_dir)
output_path = os.path.join(lang_dir, f'{wiki_title}.naf')
with open(output_path, 'w') as outfile:
naf_string = spacy_to_naf.NAF_to_string(naf)
outfile.write(naf_string)
return naf
import sys
sys.path.append('..')
import spacy
from datetime import datetime
from spacy_to_naf import text_to_NAF, NAF_to_string
nlp = spacy.load('nl_core_news_sm')
naf = text_to_NAF(
"Hij nam de kat aan.",
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms', 'deps'},
naf_version='v3.1',
language='nl',
layer_to_attributes_to_ignore={'terms': {'morphofeat', 'type'}},
replace_hidden_characters=True,
dtd_validation=True)
print(NAF_to_string(naf))
import sys
sys.path.append('..')
import spacy
from datetime import datetime
from spacy_to_naf import text_to_NAF, NAF_to_string
nlp = spacy.load('en_core_web_sm')
naf = text_to_NAF('He gave up.',
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms'},
naf_version='v3.1',
layer_to_attributes_to_ignore={'terms' : {'morphofeat', 'type'}},
dtd_validation=True)
print(NAF_to_string(naf))
import sys
sys.path.append('..')
import spacy
from datetime import datetime
from spacy_to_naf import text_to_NAF, NAF_to_string
nlp = spacy.load('en_core_web_sm')
naf = text_to_NAF(
'Tom Cruise is an actor.\n\n\nHe likes to act.',
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms'},
naf_version='v4',
layer_to_attributes_to_ignore={'terms': {'morphofeat', 'type'}},
replace_hidden_characters=True,
map_udpos2naf_pos=True) # map UD pos to NAF pos
print(NAF_to_string(naf))
import sys
sys.path.append('..')
import spacy
from lxml import etree
from spacy_to_naf import text_to_NAF, NAF_to_string
from spacy_to_naf import EntityElement, add_entity_element
from datetime import datetime
nlp = spacy.load('en_core_web_sm')
tree = text_to_NAF('Tom Cruise is an actor.\n\n\nHe likes to act.',
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms'},
replace_hidden_characters=True,
map_udpos2naf_pos=True) # map UD pos to NAF pos
root = tree.getroot()
entities_layer = root.find('entities')
if entities_layer is None:
etree.SubElement(root, "entities")
entities_layer = root.find('entities')
entity_data = EntityElement(eid='1',
entity_type='None',
targets=['t1', 't2'],
text='Tom Cruise',
ext_refs=[{
'reference':
import sys
sys.path.append('..')
import spacy
from lxml import etree
from spacy_to_naf import text_to_NAF, NAF_to_string
from spacy_to_naf import EntityElement, add_entity_element
from datetime import datetime
nlp = spacy.load('en_core_web_sm')
NAF = text_to_NAF(
'The man saw the bird. The woman gave the gift to the person.',
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms', 'deps'},
replace_hidden_characters=True,
map_udpos2naf_pos=True) # map UD pos to NAF pos
print(NAF_to_string(NAF))
def run_spacy_on_wiki_text_and_add_hyperlinks(wiki_title,
prefix,
language,
nlp,
wiki_folder,
wiki_uri2relative_path,
dct,
output_folder=None,
verbose=0):
"""
:param str wiki_title: Wikipedia article title, e.g., "President van Frankrijk"
:param str language: supported: 'nl' | 'en' | 'it'
:param nlp: loaded spaCy model, i.e., results of calling spacy.load('MODELNAME')
:param str wiki_folder: path to where extracted Wikipedia output is stored, e.g, the folder "wiki",
with subfolders for the output per language
:param datetime.datetime dct: document creation time, date of crawling for Wikipedia
:param output_folder: if provided, the NAF file will be written to
output_folder/LANGUAGE/WIKI_TITLE.naf
:rtype: tuple
:return: (succes, reason, naf)
"""
succes = True
reason = 'succes'
naf = None
assert language in {'nl', 'en', 'it'}, f'{language} not part of supported languages: nl it en'
# try to retrieve JSON of Wikipedia article
wiki_uri = f'{prefix}{wiki_title.replace(" ", "_")}'
wiki_uri_encoded = urlencode_wikititle(wiki_title, prefix=prefix)
if verbose >= 2:
print(wiki_uri)
if wiki_uri_encoded not in wiki_uri2relative_path:
reason = 'page not extracted'
succes = False
else:
relative_path, line_number = wiki_uri2relative_path[wiki_uri_encoded]
path = os.path.join(wiki_folder, relative_path)
# load wiki_page
wiki_page = {}
with bz2.BZ2File(path, "r") as infile:
for index, line in enumerate(infile):
if index == line_number:
wiki_page = json.loads(line)
break
assert wiki_page, f'index is wrong for {language} {wiki_title}'
# parse with spaCy
naf = spacy_to_naf.text_to_NAF(text=wiki_page['text'],
nlp=nlp,
dct=dct,
layers={'raw', 'text', 'terms'},
title=wiki_title,
uri=wiki_uri,
language=language)
assert naf.find('raw').text == wiki_page['text'], f'mismatch between raw text JSON and NAF file'
# add hyperlinks as entity elements
add_hyperlinks(naf,
wiki_page['annotations'],
prefix,
verbose=verbose)
# if wanted, write output to disk
if output_folder is not None:
if not os.path.exists(output_folder):
os.mkdir(output_folder)
lang_dir = os.path.join(output_folder, language)
if not os.path.exists(lang_dir):
os.mkdir(lang_dir)
output_path = os.path.join(lang_dir, f'{wiki_title}.naf')
with open(output_path, 'w') as outfile:
naf_string = spacy_to_naf.NAF_to_string(naf)
outfile.write(naf_string)
if verbose >= 2:
print(f'written {wiki_title} ({language}) to {output_path}')
message = f'succes:{succes} with reason: {reason} for {wiki_title} ({language})'
if verbose >= 3:
print(message)
if all([verbose == 2,
not succes]):
print(message)
# return message whether it was succesful
return succes, reason, naf
import spacy
from lxml import etree
from spacy_to_naf import text_to_NAF, NAF_to_string
from spacy_to_naf import EntityElement, add_entity_element
from spacy_to_naf import time_in_correct_format
from spacy_to_naf import add_linguisticProcessors_el
from datetime import datetime
nlp = spacy.load('en_core_web_sm')
naf_version = 'v3.1'
now = datetime.now()
tree = text_to_NAF('Tom Cruise is an actor.\n\n\nHe likes to act.',
nlp,
dct=now,
naf_version=naf_version,
layers={'raw', 'text', 'terms'},
replace_hidden_characters=True,
map_udpos2naf_pos=False,
dtd_validation=True) # map UD pos to NAF pos
root = tree.getroot()
naf_header = root.find('nafHeader')
time_as_string = time_in_correct_format(now)
modelname = 'Wikipedia hyperlinks'
add_linguisticProcessors_el(naf_header,
layer='entities',
start_time=time_as_string,
end_time=time_as_string,
modelname=modelname)
import sys
sys.path.append('..')
import spacy
from datetime import datetime
from spacy_to_naf import text_to_NAF, NAF_to_string
nlp = spacy.load('it_core_news_sm')
naf = text_to_NAF(
'Tom Cruise is an actor.\n\n\nHe likes to act.',
nlp,
dct=datetime.now(),
layers={'raw', 'text', 'terms'},
naf_version='v3.1',
layer_to_attributes_to_ignore={'terms': {'morphofeat', 'type'}},
dtd_validation=True) # map UD pos to NAF pos
