def _parse(*args, **kw): # FIXME (workaround) from pattern.text.en import parser if isinstance(parser.model, str): from pattern.text import Model parser.model = Model(path=parser.model) return parsetree(*args, **kw)
# Perceptron is an error-driven classifier. # When given a training example (e.g., tagged word + surrounding words), # it will check if it could correctly predict this example. # If not, it will adjust its weights. # So the accuracy of the perceptron can be improved significantly # by training in multiple iterations, averaging out all weights. # This will take several minutes. # If you want it to run faster for experimentation, # use less iterations or less data in the code below: print("training model...") seed(0) # Lock random list shuffling so we can compare. m = Model(known=known, unknown=unknown, classifier=SLP()) for iteration in range(5): for s in shuffled(data[:20000]): prev = None next = None for i, (w, tag) in enumerate(s): if i < len(s) - 1: next = s[i + 1] m.train(w, tag, prev, next) prev = (w, tag) next = None f = os.path.join(os.path.dirname(__file__), "en-model.slp") m.save(f, final=True) # Each parser in Pattern (pattern.en, pattern.es, pattern.it, ...)
# Perceptron is an error-driven classifier. # When given a training example (e.g., tagged word + surrounding words), # it will check if it could correctly predict this example. # If not, it will adjust its weights. # So the accuracy of the perceptron can be improved significantly # by training in multiple iterations, averaging out all weights. # This will take several minutes. # If you want it to run faster for experimentation, # use less iterations or less data in the code below: print "training model..." seed(0) # Lock random list shuffling so we can compare. m = Model(known=known, unknown=unknown, classifier=SLP()) for iteration in range(5): for s in shuffled(data[:20000]): prev = None next = None for i, (w, tag) in enumerate(s): if i < len(s) - 1: next = s[i+1] m.train(w, tag, prev, next) prev = (w, tag) next = None m.save("en-model.slp", final=True) # Each parser in Pattern (pattern.en, pattern.es, pattern.it, ...) # assumes that a lexicon of known words and their most frequent tag is available,
# Perceptron is an error-driven classifier. # When given a training example (e.g., tagged word + surrounding words), # it will check if it could correctly predict this example. # If not, it will adjust its weights. # So the accuracy of the perceptron can be improved significantly # by training in multiple iterations, averaging out all weights. # This will take several minutes. # If you want it to run faster for experimentation, # use less iterations or less data in the code below: print "training model..." seed(0) # Lock random list shuffling so we can compare. m = Model(known=known, unknown=unknown, classifier=SLP()) for iteration in range(5): for s in shuffled(data[:20000]): prev = None next = None for i, (w, tag) in enumerate(s): if i < len(s) - 1: next = s[i + 1] m.train(w, tag, prev, next) prev = (w, tag) next = None m.save("en-model.slp", final=True) # Each parser in Pattern (pattern.en, pattern.es, pattern.it, ...) # assumes that a lexicon of known words and their most frequent tag is available,