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63 lines
2.2 KiB
Python
Executable File
63 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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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(description="Tag a .txt file with spaCy and \
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save it in .vrt format")
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parser.add_argument("-i",
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dest="input",
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help="Input file.",
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required=True)
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parser.add_argument("-l",
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choices=["de", "en", "es", "fr", "pt"],
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dest="lang",
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help="Language for tagging",
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required=True)
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parser.add_argument("-o",
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dest="output",
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help="Output file.",
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required=True)
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args = parser.parse_args()
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SPACY_MODELS = {"de": "de_core_news_sm", "en": "en_core_web_sm",
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"es": "es_core_news_sm", "fr": "fr_core_news_sm",
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"pt": "pt_core_news_sm"}
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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.input) 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.output, "w+")
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output_file.write('<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="' + os.path.basename(args.input).rsplit(".", 1)[0] + '">\n')
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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(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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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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