nlp/spacy_nlp
2020-02-12 14:25:08 +01:00

84 lines
2.7 KiB
Python
Executable File

#!/usr/bin/env python3.5
# coding=utf-8
from xml.sax.saxutils import escape
import argparse
import chardet
import spacy
import textwrap
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', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
dest='lang',
required=True)
parser.add_argument('o', metavar='vrt-destfile')
parser.add_argument('--check-encoding',
default=False,
action='store_true',
dest='check_encoding'
)
args = parser.parse_args()
SPACY_MODELS = {'de': 'de_core_news_sm',
'el': 'el_core_news_sm',
'en': 'en_core_web_sm',
'es': 'es_core_news_sm',
'fr': 'fr_core_news_sm',
'it': 'it_core_news_sm',
'nl': 'nl_core_news_sm',
'pt': 'pt_core_news_sm'}
# Set the language model for spacy
nlp = spacy.load(SPACY_MODELS[args.lang])
# Try to determine the encoding of the text in the input file
if args.check_encoding:
with open(args.i, "rb") as input_file:
bytes = input_file.read()
encoding = chardet.detect(bytes)['encoding']
else:
encoding = 'utf-8'
# 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.i, encoding=encoding) 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.o, 'w+')
output_file.write('<?xml version="1.0" encoding="UTF-8"?>\n'
'<corpus>\n'
'<text>\n')
for text in texts:
# Run spacy nlp over the text (partial string if above 1 million chars)
doc = nlp(text)
for sent in doc.sents:
output_file.write('<s>\n')
for token in sent:
# Skip whitespace tokens like "\n" or "\t"
if token.text.isspace():
continue
# Write all information in .vrt style to the output file
# text, lemma, simple_pos, pos, ner
output_file.write(
'{}\t{}\t{}\t{}\t{}\n'.format(
escape(token.text),
escape(token.lemma_),
token.pos_,
token.tag_,
token.ent_type_ if token.ent_type_ != '' else 'NULL'
)
)
output_file.write('</s>\n')
output_file.write('</text>\n'
'</corpus>')
output_file.close()