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Rename processing text to production use and remove linear feature scheme
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ines
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@ -27,8 +27,7 @@
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"GoldCorpus": "goldcorpus"
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},
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"Other": {
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"Annotation Specs": "annotation",
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"Feature Scheme": "features"
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"Annotation Specs": "annotation"
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}
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},
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@ -143,9 +142,5 @@
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"annotation": {
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"title": "Annotation Specifications"
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},
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"features": {
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"title": "Linear Model Feature Scheme"
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}
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}
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@ -1,138 +0,0 @@
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//- 💫 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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@ -15,9 +15,9 @@
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"Custom tokenization": "customizing-tokenizer",
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"Rule-based matching": "rule-based-matching",
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"Adding languages": "adding-languages",
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"Processing text": "processing-text",
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"NLP pipelines": "language-processing-pipeline",
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"Deep learning": "deep-learning",
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"Production use": "production-use",
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"Training": "training",
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"Training NER": "training-ner",
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"Saving & loading": "saving-loading",
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@ -99,11 +99,6 @@
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"next": "training"
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},
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"processing-text": {
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"title": "Processing text",
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"next": "language-processing-pipeline"
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},
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"language-processing-pipeline": {
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"title": "Language processing pipelines",
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"next": "deep-learning"
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@ -111,9 +106,15 @@
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"deep-learning": {
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"title": "Hooking a deep learning model into spaCy",
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"next": "production use"
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},
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"production-use": {
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"title": "Production use",
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"next": "training"
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},
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"training": {
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"title": "Training spaCy's statistical models",
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"next": "saving-loading"
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@ -6,69 +6,6 @@ p
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| Once you have loaded the #[code nlp] object, you can call it as though
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| it were a function. This allows you to process a single unicode string.
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+code.
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doc = nlp(u'Hello, world! A three sentence document.\nWith new lines...')
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p
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| The library should perform equally well with #[strong short or long documents].
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| All algorithms are linear-time in the length of the string, and once the
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| data is loaded, there's no significant start-up cost to consider. This
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| means that you don't have to strategically merge or split your text —
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| you should feel free to feed in either single tweets or whole novels.
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p
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| If you run #[+api("spacy#load") #[code spacy.load('en')]], spaCy will
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| load the #[+a("/docs/usage/models") model] associated with the name
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| #[code 'en']. Each model is a Python package containing an
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| #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py"))__init__.py]
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the #[code nlp] object will
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| be an instance of #[code spacy.en.English]. This means that when you run
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| #[code doc = nlp(text)], you're executing
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| #[code spacy.en.English.__call__], which is implemented on its parent
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| class, #[+api("language") #[code Language]].
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+code.
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doc = nlp.make_doc(text)
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for proc in nlp.pipeline:
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proc(doc)
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p
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| I've tried to make sure that the #[code Language.__call__] function
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| doesn't do any "heavy lifting", so that you won't have complicated logic
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| to replicate if you need to make your own pipeline class. This is all it
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| does.
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p
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| The #[code .make_doc()] method and #[code .pipeline] attribute make it
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| easier to customise spaCy's behaviour. If you're using the default
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| pipeline, we can desugar one more time.
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+code.
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doc = nlp.tokenizer(text)
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nlp.tagger(doc)
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nlp.parser(doc)
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nlp.entity(doc)
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p Finally, here's where you can find out about each of those components:
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+table(["Name", "Source"])
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+row
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+cell #[code tokenizer]
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+cell #[+src(gh("spacy", "spacy/tokenizer.pyx")) spacy.tokenizer.Tokenizer]
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+row
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+cell #[code tagger]
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+cell #[+src(gh("spacy", "spacy/tagger.pyx")) spacy.pipeline.Tagger]
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+row
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+cell #[code parser]
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+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.DependencyParser]
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+row
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+cell #[code entity]
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+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.EntityRecognizer]
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+h(2, "multithreading") Multi-threading with #[code .pipe()]
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p
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