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, ...)
# assumes that a lexicon of known words and their most frequent tag is available,
# along with some rules for morphology (suffixes, e.g., -ly = adverb)
# and context (surrounding words) for unknown words.
# If a language model is also available, it overrides these (simpler) rules.
# For English, this can raise accuracy from about 94% up to about 97%,
# and makes the parses about 3x faster.
print("loading model...")
f = os.path.join(os.path.dirname(__file__), "en-model.slp")
lexicon.model = Model.load(lexicon, f)
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,
# along with some rules for morphology (suffixes, e.g., -ly = adverb)
# and context (surrounding words) for unknown words.
# If a language model is also available, it overrides these (simpler) rules.
# For English, this can raise accuracy from about 94% up to about 97%,
# and makes the parses about 3x faster.
print "loading model..."
lexicon.model = Model.load(lexicon, "en-model.slp")
# To test the accuracy of the language 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, ...)
# assumes that a lexicon of known words and their most frequent tag is available,
# along with some rules for morphology (suffixes, e.g., -ly = adverb)
# and context (surrounding words) for unknown words.
# If a language model is also available, it overrides these (simpler) rules.
# For English, this can raise accuracy from about 94% up to about 97%,
# and makes the parses about 3x faster.
print("loading model...")
f = os.path.join(os.path.dirname(__file__), "en-model.slp")
lexicon.model = Model.load(f, lexicon)
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,
# along with some rules for morphology (suffixes, e.g., -ly = adverb)
# and context (surrounding words) for unknown words.
# If a language model is also available, it overrides these (simpler) rules.
# For English, this can raise accuracy from about 94% up to about 97%,
# and makes the parses about 3x faster.
print "loading model..."
lexicon.model = Model.load(lexicon, "en-model.slp")
# To test the accuracy of the language model,
