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139 lines
3.9 KiB
Plaintext
139 lines
3.9 KiB
Plaintext
//- 💫 DOCS > API > LINEAR MOEL FEATURES
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include ../../_includes/_mixins
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p
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| There are two popular strategies for putting together machine learning
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| models for NLP: sparse linear models, and neural networks. To solve NLP
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| problems with linear models, feature templates need to be assembled that
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| combine multiple atomic predictors. This page documents the atomic
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| predictors used in the spaCy 1.0 #[+api("parser") #[code Parser]],
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| #[+api("tagger") #[code Tagger]] and
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| #[+api("entityrecognizer") #[code EntityRecognizer]].
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p
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| To understand the scheme, recall that spaCy's #[code Parser] and
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| #[code EntityRecognizer] are implemented as push-down automata. They
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| maintain a "stack" that holds the current entity, and a "buffer"
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| consisting of the words to be processed.
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p
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| Each state consists of the words on the stack (if any), which consistute
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| the current entity being constructed. We also have the current word, and
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| the two subsequent words. Finally, we also have the entities previously
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| built.
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p
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| This gives us a number of tokens to ask questions about, to make the
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| features. About each of these tokens, we can ask about a number of
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| different properties. Each feature identifier asks about a specific
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| property of a specific token of the context.
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+h(2, "tokens") Context tokens
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+table([ "ID", "Description" ])
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+row
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+cell #[code S0]
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+cell
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| The first word on the stack, i.e. the token most recently added
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| to the current entity.
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+row
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+cell #[code S1]
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+cell The second word on the stack, i.e. the second most recently added.
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+row
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+cell #[code S2]
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+cell The third word on the stack, i.e. the third most recently added.
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+row
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+cell #[code N0]
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+cell The first word of the buffer, i.e. the current word being tagged.
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+row
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+cell #[code N1]
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+cell The second word of the buffer.
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+row
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+cell #[code N2]
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+cell The third word of the buffer.
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+row
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+cell #[code P1]
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+cell The word immediately before #[code N0].
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+row
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+cell #[code P2]
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+cell The second word before #[code N0].
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+row
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+cell #[code E0]
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+cell The first word of the previously constructed entity.
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+row
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+cell #[code E1]
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+cell The first word of the second previously constructed entity.
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p About each of these tokens, we can ask:
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+table([ "ID", "Attribute", "Description" ])
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+row
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+cell #[code N0w]
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+cell #[code token.orth]
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+cell The word form.
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+row
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+cell #[code N0W]
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+cell #[code token.lemma]
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+cell The word's lemma.
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+row
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+cell #[code N0p]
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+cell #[code token.tag]
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+cell The word's (full) POS tag.
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+row
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+cell #[code N0c]
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+cell #[code token.cluster]
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+cell The word's (full) Brown cluster.
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+row
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+cell #[code N0c4]
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+cell -
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+cell First four digit prefix of the word's Brown cluster.
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+row
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+cell #[code N0c6]
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+cell -
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+cell First six digit prefix of the word's Brown cluster.
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+row
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+cell #[code N0L]
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+cell -
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+cell The word's dependency label. Not used as a feature in the NER.
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+row
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+cell #[code N0_prefix]
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+cell #[code token.prefix]
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+cell The first three characters of the word.
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+row
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+cell #[code N0_suffix]
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+cell #[code token.suffix]
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+cell The last three characters of the word.
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+row
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+cell #[code N0_shape]
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+cell #[code token.shape]
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+cell The word's shape, i.e. is it alphabetic, numeric, etc.
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+row
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+cell #[code N0_ne_iob]
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+cell #[code token.ent_iob]
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+cell The Inside/Outside/Begin code of the word's NER tag.
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+row
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+cell #[code N0_ne_type]
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+cell #[code token.ent_type]
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+cell The word's NER type.
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