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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,109 @@ | ||
| import re | ||
| import json | ||
| import gzip | ||
| import argparse | ||
| from collections import OrderedDict | ||
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| from logzero import logger | ||
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| from tokenization import RegExpTokenizer, NLTKTokenizer, MeCabTokenizer | ||
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| regex_spaces = re.compile(r'\s+') | ||
| regex_hyperlink = re.compile(r'\[\[([^:]+?)\]\]') | ||
| regex_entity = re.compile(r'##[^#]+?##') | ||
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| def main(args): | ||
| logger.info('initializing a tokenizer') | ||
| if args.tokenizer == 'regexp': | ||
| tokenizer = RegExpTokenizer(do_lower_case=args.do_lower_case, | ||
| preserved_pattern=regex_entity) | ||
| elif args.tokenizer == 'nltk': | ||
| tokenizer = NLTKTokenizer(do_lower_case=args.do_lower_case, | ||
| preserved_pattern=regex_entity) | ||
| elif args.tokenizer == 'mecab': | ||
| tokenizer = MeCabTokenizer(mecab_option=args.tokenizer_option, | ||
| do_lower_case=args.do_lower_case, | ||
| preserved_pattern=regex_entity) | ||
| else: | ||
| raise RuntimeError(f'Invalid tokenizer: {args.tokenizer}') | ||
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| logger.info('generating corpus for training') | ||
| n_processed = 0 | ||
| with gzip.open(args.cirrus_file, 'rt') as fi, \ | ||
| open(args.output_file, 'wt') as fo: | ||
| for line in fi: | ||
| json_item = json.loads(line) | ||
| if 'title' not in json_item: | ||
| continue | ||
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| title = json_item['title'] | ||
| text = regex_spaces.sub(' ', json_item['text']) | ||
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| hyperlinks = dict() | ||
| hyperlinks[title] = title | ||
| for match in regex_hyperlink.finditer(json_item['source_text']): | ||
| if '|' in match.group(1): | ||
| (entity, anchor) = match.group(1).split('|', maxsplit=1) | ||
| else: | ||
| entity = anchor = match.group(1) | ||
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| if '#' in entity: | ||
| entity = entity[:entity.find('#')] | ||
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| anchor = anchor.strip() | ||
| entity = entity.strip() | ||
| if len(anchor) > 0 and len(entity) > 0: | ||
| hyperlinks.setdefault(anchor, entity) | ||
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| hyperlinks_sorted = OrderedDict(sorted( | ||
| hyperlinks.items(), key=lambda t: len(t[0]), reverse=True)) | ||
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| replacement_flags = [0] * len(text) | ||
| for (anchor, entity) in hyperlinks_sorted.items(): | ||
| cursor = 0 | ||
| while cursor < len(text) and anchor in text[cursor:]: | ||
| start = text.index(anchor, cursor) | ||
| end = start + len(anchor) | ||
| if not any(replacement_flags[start:end]): | ||
| entity_token = f'##{entity}##'.replace(' ', '_') | ||
| text = text[:start] + entity_token + text[end:] | ||
| replacement_flags = replacement_flags[:start] \ | ||
| + [1] * len(entity_token) + replacement_flags[end:] | ||
| assert len(text) == len(replacement_flags) | ||
| cursor = start + len(entity_token) | ||
| else: | ||
| cursor = end | ||
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| text = ' '.join(tokenizer.tokenize(text)) | ||
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| print(text, file=fo) | ||
| n_processed += 1 | ||
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| if n_processed <= 10: | ||
| logger.info('*** Example ***') | ||
| example_text = text[:400] + '...' if len(text) > 400 else text | ||
| logger.info(example_text) | ||
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| if n_processed % 10000 == 0: | ||
| logger.info(f'processed: {n_processed}') | ||
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| if n_processed % 10000 != 0: | ||
| logger.info(f'processed: {n_processed}') | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--cirrus_file', type=str, required=True, | ||
| help='Wikipedia Cirrussearch content dump file (.json.gz)') | ||
| parser.add_argument('--output_file', type=str, required=True, | ||
| help='output corpus file (.txt)') | ||
| parser.add_argument('--tokenizer', default='regexp', | ||
| help='tokenizer type [regexp]') | ||
| parser.add_argument('--do_lower_case', action='store_true', | ||
| help='lowercase words (not applied to NEs)') | ||
| parser.add_argument('--tokenizer_option', type=str, default='', | ||
| help='option string passed to the tokenizer') | ||
| args = parser.parse_args() | ||
| main(args) |
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