This commit is contained in:
Patrick Jentsch
2019-05-20 11:28:51 +02:00
parent ed26d24776
commit 5b7bc2a840
3 changed files with 128 additions and 104 deletions

View File

@ -1,48 +1,53 @@
#!/usr/bin/env python3
# coding=utf-8
import argparse
import os
import spacy
import textwrap
parser = argparse.ArgumentParser(description="Tag a .txt file with spaCy and \
save it in .vrt format")
parser.add_argument("-i",
dest="input",
help="Input file.",
required=True)
parser.add_argument("-l",
choices=["de", "en", "es", "fr", "pt"],
dest="lang",
help="Language for tagging",
required=True)
parser.add_argument("-o",
dest="output",
help="Output file.",
required=True)
parser = argparse.ArgumentParser(
description='Tag a text file with spaCy and save it as a verticalized text file.'
)
parser.add_argument(
'i',
metavar='txt-sourcefile',
)
parser.add_argument(
'-l',
choices=['de', 'en', 'es', 'fr', 'pt'],
dest='lang',
required=True
)
parser.add_argument(
'o',
metavar='vrt-destfile',
)
args = parser.parse_args()
SPACY_MODELS = {"de": "de_core_news_sm", "en": "en_core_web_sm",
"es": "es_core_news_sm", "fr": "fr_core_news_sm",
"pt": "pt_core_news_sm"}
SPACY_MODELS = {
'de': 'de_core_news_sm', 'en': 'en_core_web_sm', 'es': 'es_core_news_sm',
'fr': 'fr_core_news_sm', 'pt': 'pt_core_news_sm'
}
# Set the language model for spacy
nlp = spacy.load(SPACY_MODELS[args.lang])
# Read text from the input file and if neccessary split it into parts with a
# length of less than 1 million characters.
with open(args.input) as input_file:
with open(args.i) as input_file:
text = input_file.read()
texts = textwrap.wrap(text, 1000000, break_long_words=False)
text = None
# Create and open the output file
output_file = open(args.output, "w+")
output_file.write('<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="' + os.path.basename(args.input).rsplit(".", 1)[0] + '">\n')
output_file = open(args.o, 'w+')
output_file.write(
'<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
os.path.basename(args.i).rsplit(".", 1)[0]
)
)
for text in texts:
# Run spacy nlp over the text (partial string if above 1 million chars)
doc = nlp(text)
@ -54,9 +59,12 @@ for text in texts:
continue
# Write all information in .vrt style to the output file
# text, lemma, simple_pos, pos, ner
output_file.write(token.text + "\t" + token.lemma_ + "\t"
+ token.pos_ + "\t" + token.tag_ + "\t"
+ (token.ent_type_ if token.ent_type_ != "" else "NULL") + "\n")
output_file.write(
token.text + '\t' + token.lemma_ + '\t'
+ token.pos_ + '\t' + token.tag_ + '\t'
+ (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
)
output_file.write('</s>\n')
output_file.write('</text>\n</corpus>')
output_file.close()