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72 lines
2.0 KiB
Python
Executable File
72 lines
2.0 KiB
Python
Executable File
#!/usr/bin/env python3.5
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# coding=utf-8
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import argparse
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import os
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import spacy
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import textwrap
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parser = argparse.ArgumentParser(
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description='Tag a text file with spaCy and save it as a verticalized text file.'
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)
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parser.add_argument(
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'i',
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metavar='txt-sourcefile',
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)
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parser.add_argument(
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'-l',
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choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
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dest='lang',
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required=True
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)
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parser.add_argument(
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'o',
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metavar='vrt-destfile',
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)
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args = parser.parse_args()
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SPACY_MODELS = {
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'de': 'de_core_news_sm', 'el': 'el_core_news_sm', 'en': 'en_core_web_sm',
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'es': 'es_core_news_sm', 'fr': 'fr_core_news_sm', 'it': 'it_core_news_sm',
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'nl': 'nl_core_news_sm', 'pt': 'pt_core_news_sm'
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}
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# Set the language model for spacy
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nlp = spacy.load(SPACY_MODELS[args.lang])
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# Read text from the input file and if neccessary split it into parts with a
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# length of less than 1 million characters.
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with open(args.i) as input_file:
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text = input_file.read()
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texts = textwrap.wrap(text, 1000000, break_long_words=False)
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text = None
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# Create and open the output file
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output_file = open(args.o, 'w+')
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output_file.write(
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'<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
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os.path.basename(args.i).rsplit(".", 1)[0]
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)
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)
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for text in texts:
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# Run spacy nlp over the text (partial string if above 1 million chars)
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doc = nlp(text)
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for sent in doc.sents:
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output_file.write('<s>\n')
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for token in sent:
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# Skip whitespace tokens like "\n" or "\t"
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if token.text.isspace():
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continue
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# Write all information in .vrt style to the output file
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# text, lemma, simple_pos, pos, ner
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output_file.write(
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token.text + '\t' + token.lemma_ + '\t'
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+ token.pos_ + '\t' + token.tag_ + '\t'
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+ (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
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)
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output_file.write('</s>\n')
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output_file.write('</text>\n</corpus>')
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output_file.close()
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