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,