trainer.ABBREV = 0.3 """cut-off value whether a 'token' is an abbreviation""" trainer.IGNORE_ABBREV_PENALTY = False """allows the disabling of the abbreviation penalty heuristic, which exponentially disadvantages words that are found at times without a final period.""" trainer.ABBREV_BACKOFF = 5 """upper cut-off for Mikheev's(2002) abbreviation detection algorithm""" trainer.COLLOCATION = 7.88 """minimal log-likelihood value that two tokens need to be considered as a collocation""" trainer.SENT_STARTER = 30 """minimal log-likelihood value that a token requires to be considered as a frequent sentence starter""" trainer.INCLUDE_ALL_COLLOCS = False """this includes as potential collocations all word pairs where the first word ends in a period. It may be useful in corpora where there is a lot of variation that makes abbreviations like Mr difficult to identify.""" trainer.INCLUDE_ABBREV_COLLOCS = False """this includes as potential collocations all word pairs where the first word is an abbreviation. Such collocations override the orthographic heuristic, but not the sentence starter heuristic. This is overridden by INCLUDE_ALL_COLLOCS, and if both are false, only collocations with initials and ordinals are considered.""" """"""
trainer.ABBREV = 0.3 """cut-off value whether a 'token' is an abbreviation""" trainer.IGNORE_ABBREV_PENALTY = False """allows the disabling of the abbreviation penalty heuristic, which exponentially disadvantages words that are found at times without a final period.""" trainer.ABBREV_BACKOFF = 5 """upper cut-off for Mikheev's(2002) abbreviation detection algorithm""" trainer.COLLOCATION = 7.88 """minimal log-likelihood value that two tokens need to be considered as a collocation""" trainer.SENT_STARTER = 30 """minimal log-likelihood value that a token requires to be considered as a frequent sentence starter""" trainer.INCLUDE_ALL_COLLOCS = False """this includes as potential collocations all word pairs where the first word ends in a period. It may be useful in corpora where there is a lot of variation that makes abbreviations like Mr difficult to identify.""" trainer.INCLUDE_ABBREV_COLLOCS = False """this includes as potential collocations all word pairs where the first word is an abbreviation. Such collocations override the orthographic heuristic, but not the sentence starter heuristic. This is overridden by INCLUDE_ALL_COLLOCS, and if both are false, only collocations with initials and ordinals are considered.""" """"""
from os.path import basename from nltk.tokenize.punkt import PunktTrainer __author__ = 'Florian Leitner' __version__ = '1.0' if len(sys.argv) == 2 and sys.argv[1] in ('-h', '--help'): print('usage: {} < TEXT > MODEL'.format(basename(sys.argv[0]))) sys.exit(1) trainer = PunktTrainer() # configuration trainer.ABBREV = 0.3 # cut-off value whether a ‘token’ is an abbreviation trainer.ABBREV_CUTOFF = 5 # upper cut-off for Mikheev’s (2002) abbreviation detection algorithm trainer.COLLOCATION = 7.88 # minimal log-likelihood value that two tokens need to be considered as a collocation trainer.IGNORE_ABBREV_PENALTY = False # disables the abbreviation penalty heuristic, which exponentially disadvantages words that are found at times without a final period trainer.INCLUDE_ABBREV_COLLOCS = True # include as potential collocations all word pairs where the first word is an abbreviation - such collocations override the orthographic heuristic, but not the sentence starter heuristic trainer.INCLUDE_ALL_COLLOCS = False # this includes as potential collocations all word pairs where the first word ends in a period - it may be useful in corpora where there is a lot of variation that makes abbreviations like Mr difficult to identify trainer.MIN_COLLOC_FREQ = 3 # minimum bound on the number of times a bigram needs to appear before it can be considered a collocation - useful when INCLUDE_*_COLLOCS are used trainer.SENT_STARTER = 30 # minimal log-likelihood value that a token requires to be considered as a frequent sentence starter for line in fileinput.input(): trainer.train(line) #print(line) #trainer.freq_threshold() trainer.finalize_training() params = trainer.get_params() pickle.dump(params, sys.stdout.buffer)