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fix all references to BILUO annotation format (#3797)
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@ -532,7 +532,7 @@ cdef class GoldParse:
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self.labels[i] = deps[i2j_multi[i]]
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self.labels[i] = deps[i2j_multi[i]]
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# Now set NER...This is annoying because if we've split
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# Now set NER...This is annoying because if we've split
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# got an entity word split into two, we need to adjust the
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# got an entity word split into two, we need to adjust the
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# BILOU tags. We can't have BB or LL etc.
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# BILUO tags. We can't have BB or LL etc.
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# Case 1: O -- easy.
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# Case 1: O -- easy.
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ner_tag = entities[i2j_multi[i]]
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ner_tag = entities[i2j_multi[i]]
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if ner_tag == "O":
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if ner_tag == "O":
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@ -127,7 +127,7 @@ cdef class PhraseMatcher:
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and self.attr not in (DEP, POS, TAG, LEMMA):
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and self.attr not in (DEP, POS, TAG, LEMMA):
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string_attr = self.vocab.strings[self.attr]
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string_attr = self.vocab.strings[self.attr]
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user_warning(Warnings.W012.format(key=key, attr=string_attr))
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user_warning(Warnings.W012.format(key=key, attr=string_attr))
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tags = get_bilou(length)
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tags = get_biluo(length)
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phrase_key = <attr_t*>mem.alloc(length, sizeof(attr_t))
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phrase_key = <attr_t*>mem.alloc(length, sizeof(attr_t))
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for i, tag in enumerate(tags):
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for i, tag in enumerate(tags):
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attr_value = self.get_lex_value(doc, i)
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attr_value = self.get_lex_value(doc, i)
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@ -230,7 +230,7 @@ cdef class PhraseMatcher:
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return "matcher:{}-{}".format(string_attr_name, string_attr_value)
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return "matcher:{}-{}".format(string_attr_name, string_attr_value)
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def get_bilou(length):
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def get_biluo(length):
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if length == 0:
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if length == 0:
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raise ValueError(Errors.E127)
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raise ValueError(Errors.E127)
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elif length == 1:
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elif length == 1:
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@ -510,7 +510,7 @@ described in any single publication. The model is a greedy transition-based
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parser guided by a linear model whose weights are learned using the averaged
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parser guided by a linear model whose weights are learned using the averaged
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perceptron loss, via the
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perceptron loss, via the
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[dynamic oracle](http://www.aclweb.org/anthology/C12-1059) imitation learning
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[dynamic oracle](http://www.aclweb.org/anthology/C12-1059) imitation learning
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strategy. The transition system is equivalent to the BILOU tagging scheme.
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strategy. The transition system is equivalent to the BILUO tagging scheme.
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## Models and training data {#training}
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## Models and training data {#training}
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