mirror of
https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git
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114 lines
4.8 KiB
Python
Executable File
114 lines
4.8 KiB
Python
Executable File
#!/usr/bin/env python3.7
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# coding=utf-8
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from argparse import ArgumentParser
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from xml.sax.saxutils import escape
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import chardet
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import hashlib
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import os
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import spacy
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import textwrap
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SPACY_MODELS = {'de': 'de_core_news_md',
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'el': 'el_core_news_md',
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'en': 'en_core_web_md',
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'es': 'es_core_news_md',
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'fr': 'fr_core_news_md',
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'it': 'it_core_news_md',
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'nl': 'nl_core_news_md',
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'pt': 'pt_core_news_md'}
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SPACY_MODELS_VERSION = os.environ.get('SPACY_MODELS_VERSION')
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SPACY_VERSION = os.environ.get('SPACY_VERSION')
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# Parse the given arguments
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parser = ArgumentParser(description=('Tag a text file with spaCy and save it '
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'as a verticalized text file.'))
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parser.add_argument('-i', '--input', metavar='txt-sourcefile', required=True)
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parser.add_argument('-o', '--output', metavar='vrt-destfile', required=True)
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parser.add_argument('-l', '--language', choices=SPACY_MODELS.keys(), required=True) # noqa
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parser.add_argument('--check-encoding', action='store_true')
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args = parser.parse_args()
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# If requested: Check the encoding of the text contents from the input file
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# Else: Use utf-8
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if args.check_encoding:
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with open(args.input, "rb") as input_file:
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bytes = input_file.read()
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encoding = chardet.detect(bytes)['encoding']
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else:
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encoding = 'utf-8'
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# hashing in chunks to avoid full RAM with huge files.
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with open(args.input, 'rb') as input_file:
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source_md5 = hashlib.md5()
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for chunk in iter(lambda: input_file.read(128 * source_md5.block_size), b''):
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source_md5.update(chunk)
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source_md5 = source_md5.hexdigest()
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# Load the text contents from the input file
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with open(args.input, encoding=encoding) as input_file:
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text = input_file.read()
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# spaCys NLP is limited to strings with maximum 1 million characters at
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# once. So we split it into suitable chunks.
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text_chunks = textwrap.wrap(text, 1000000, break_long_words=False)
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# the text variable potentially occupies a lot of system memory and is no
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# longer needed...
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del text
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# Setup the spaCy toolkit by loading the chosen language model
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model = SPACY_MODELS[args.language]
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nlp = spacy.load(model)
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# Create the output file in verticalized text format
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# See: http://cwb.sourceforge.net/files/CWB_Encoding_Tutorial/node3.html
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output_file_original_filename = args.output
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output_file_stand_off_filename = args.output.replace('.vrt', '.stand-off.vrt')
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common_xml = ('<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
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+ '<corpus>\n'
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+ '<text>\n'
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+ '<nlp name="spaCy:{}"\n'.format(SPACY_VERSION)
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+ ' model="{}:{}"\n'.format(model, SPACY_MODELS_VERSION)
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+ ' source-md5="{}" />\n'.format(source_md5))
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with open(output_file_original_filename, 'w+') as output_file_original, \
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open(output_file_stand_off_filename, 'w+') as output_file_stand_off:
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output_file_original.write(common_xml)
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output_file_stand_off.write(common_xml)
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text_offset = 0
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for text_chunk in text_chunks:
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doc = nlp(text_chunk)
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for sent in doc.sents:
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output_file_original.write('<s>\n')
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output_file_stand_off.write('<s>\n')
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space_flag = False
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# Skip whitespace tokens
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sent_no_space = [token for token in sent
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if not token.text.isspace()]
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# No space variant for cwb original .vrt file input.
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for token in sent_no_space:
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output_file_original.write('{}'.format(escape(token.text))
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+ '\t{}'.format(escape(token.lemma_))
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+ '\t{}'.format(token.pos_)
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+ '\t{}'.format(token.tag_)
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+ '\t{}\n'.format(token.ent_type_ or 'NULL'))
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# Stand off variant with spaces.
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for token in sent:
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token_start = token.idx + text_offset
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token_end = token.idx + len(token.text) + text_offset
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output_file_stand_off.write('{}:{}'.format(token_start,
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token_end)
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+ '\t{}'.format(escape(token.lemma_))
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+ '\t{}'.format(token.pos_)
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+ '\t{}'.format(token.tag_)
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+ '\t{}\n'.format(token.ent_type_ or 'NULL'))
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output_file_original.write('</s>\n')
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output_file_stand_off.write('</s>\n')
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text_offset = token_end + 1
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output_file_original.write('</text>\n</corpus>')
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output_file_stand_off.write('</text>\n</corpus>')
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