def main(argv):
if len(argv) < 3:
usage(argv)
dic = False
freq = False
own_tag = False
if len(argv) >= 4:
if argv[3] == "-d":
dic = True
if len(argv) >= 4:
if argv[3] == "-f":
freq = True
if len(argv) >= 5:
if argv[4] == "-f":
freq = True
if len(argv) >= 5:
if argv[4] == "-f":
freq = True
if len(argv) >= 4:
if argv[3] == "-t":
own_tag = True
if len(argv) >= 5:
if argv[4] == "-t":
own_tag = True
if len(argv) >= 6:
if argv[5] == "-t":
own_tag = True
ex = Util.read_file(argv[1])
ex = Util.transform_text(ex)
models = ["data/location.txt", "data/person.txt", "data/organisation.txt"]
# Analyse lexicale
lexer = Lexer(ex, own_tag)
lexer.lex()
# Analyse syntaxique
parser = Parser(lexer.get_tokenized_text(), own_tag)
parser.parse()
# Analyse sémantique + reconnaissance des EN
ner = NER(ex, parser.get_parsed_text())
if dic: ner.gen_models(models)
ner.apply()
# Balisage du texte
tagger = Tagger(ner.get_ner(), ex)
if freq:
tagger.freq_tag(argv[2])
else:
tagger.tag(argv[2])
