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
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,
