mirror of https://github.com/explosion/spaCy.git
44 lines
1.8 KiB
Plaintext
44 lines
1.8 KiB
Plaintext
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//- 💫 DOCS > API > ANNOTATION > BILUO
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+table([ "Tag", "Description" ])
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+row
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+cell #[code #[span.u-color-theme B] EGIN]
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+cell The first token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme I] N]
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+cell An inner token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme L] AST]
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+cell The final token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme U] NIT]
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+cell A single-token entity.
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+row
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+cell #[code #[span.u-color-theme O] UT]
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+cell A non-entity token.
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+aside("Why BILUO, not IOB?")
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| There are several coding schemes for encoding entity annotations as
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| token tags. These coding schemes are equally expressive, but not
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| necessarily equally learnable.
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| #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
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| showed that the minimal #[strong Begin], #[strong In], #[strong Out]
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| scheme was more difficult to learn than the #[strong BILUO] scheme that
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| we use, which explicitly marks boundary tokens.
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p
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| spaCy translates the character offsets into this scheme, in order to
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| decide the cost of each action given the current state of the entity
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| recogniser. The costs are then used to calculate the gradient of the
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| loss, to train the model. The exact algorithm is a pastiche of
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| well-known methods, and is not currently described in any single
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| publication. The model is a greedy transition-based parser guided by a
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| linear model whose weights are learned using the averaged perceptron
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| loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
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| imitation learning strategy. The transition system is equivalent to the
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| BILOU tagging scheme.
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