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Add spaCy 101 components
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38
website/docs/usage/_spacy-101/_named-entities.jade
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38
website/docs/usage/_spacy-101/_named-entities.jade
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//- 💫 DOCS > USAGE > SPACY 101 > NAMED ENTITIES
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p
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| A named entity is a "real-world object" that's assigned a name – for
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| example, a person, a country, a product or a book title. spaCy can
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| #[strong recognise] #[+a("/docs/api/annotation#named-entities") various types]
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| of named entities in a document, by asking the model for a
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| #[strong prediction]. Because models are statistical and strongly depend
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| on the examples they were trained on, this doesn't always work
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| #[em perfectly] and might need some tuning later, depending on your use
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| case.
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p
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| Named entities are available as the #[code ents] property of a #[code Doc]:
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+code.
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doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
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for ent in doc.ents:
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print(ent.text, ent.start_char, ent.end_char, ent.label_)
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+aside
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| #[strong Text]: The original entity text.#[br]
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| #[strong Start]: Index of start of entity in the #[code Doc].#[br]
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| #[strong End]: Index of end of entity in the #[code Doc].#[br]
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| #[strong Label]: Entity label, i.e. type.
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+table(["Text", "Start", "End", "Label", "Description"])
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- var style = [0, 1, 1, 1, 0]
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+annotation-row(["Apple", 0, 5, "ORG", "Companies, agencies, institutions."], style)
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+annotation-row(["U.K.", 27, 31, "GPE", "Geopolitical entity, i.e. countries, cities, states."], style)
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+annotation-row(["$1 billion", 44, 54, "MONEY", "Monetary values, including unit."], style)
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p
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| Using spaCy's built-in #[+a("/docs/usage/visualizers") displaCy visualizer],
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| here's what our example sentence and its named entities look like:
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+codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
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62
website/docs/usage/_spacy-101/_pos-deps.jade
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62
website/docs/usage/_spacy-101/_pos-deps.jade
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@ -0,0 +1,62 @@
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//- 💫 DOCS > USAGE > SPACY 101 > POS TAGGING AND DEPENDENCY PARSING
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p
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| After tokenization, spaCy can also #[strong parse] and #[strong tag] a
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| given #[code Doc]. This is where the statistical model comes in, which
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| enables spaCy to #[strong make a prediction] of which tag or label most
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| likely applies in this context. A model consists of binary data and is
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| produced by showing a system enough examples for it to make predictions
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| that generalise across the language – for example, a word following "the"
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| in English is most likely a noun.
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p
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| Linguistic annotations are available as
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| #[+api("token#attributes") #[code Token] attributes]. Like many NLP
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| libraries, spaCy #[strong encodes all strings to integers] to reduce
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| memory usage and improve efficiency. So to get the readable string
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| representation of an attribute, we need to add an underscore #[code _]
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| to its name:
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+code.
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doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
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for token in doc:
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print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
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token.shape_, token.is_alpha, token.is_stop)
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+aside
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| #[strong Text:] The original word text.#[br]
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| #[strong Lemma:] The base form of the word.#[br]
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| #[strong POS:] The simple part-of-speech tag.#[br]
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| #[strong Tag:] ...#[br]
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| #[strong Dep:] Syntactic dependency, i.e. the relation between tokens.#[br]
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| #[strong Shape:] The word shape – capitalisation, punctuation, digits.#[br]
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| #[strong is alpha:] Is the token an alpha character?#[br]
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| #[strong is stop:] Is the token part of a stop list, i.e. the most common
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| words of the language?#[br]
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+table(["Text", "Lemma", "POS", "Tag", "Dep", "Shape", "alpha", "stop"])
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- var style = [0, 0, 1, 1, 1, 1, 1, 1]
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+annotation-row(["Apple", "apple", "PROPN", "NNP", "nsubj", "Xxxxx", true, false], style)
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+annotation-row(["is", "be", "VERB", "VBZ", "aux", "xx", true, true], style)
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+annotation-row(["looking", "look", "VERB", "VBG", "ROOT", "xxxx", true, false], style)
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+annotation-row(["at", "at", "ADP", "IN", "prep", "xx", true, true], style)
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+annotation-row(["buying", "buy", "VERB", "VBG", "pcomp", "xxxx", true, false], style)
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+annotation-row(["U.K.", "u.k.", "PROPN", "NNP", "compound", "X.X.", false, false], style)
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+annotation-row(["startup", "startup", "NOUN", "NN", "dobj", "xxxx", true, false], style)
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+annotation-row(["for", "for", "ADP", "IN", "prep", "xxx", true, true], style)
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+annotation-row(["$", "$", "SYM", "$", "quantmod", "$", false, false], style)
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+annotation-row(["1", "1", "NUM", "CD", "compound", "d", false, false], style)
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+annotation-row(["billion", "billion", "NUM", "CD", "pobj", "xxxx", true, false], style)
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+aside("Tip: Understanding tags and labels")
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| Most of the tags and labels look pretty abstract, and they vary between
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| languages. #[code spacy.explain()] will show you a short description –
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| for example, #[code spacy.explain("VBZ")] returns "verb, 3rd person
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| singular present".
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p
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| Using spaCy's built-in #[+a("/docs/usage/visualizers") displaCy visualizer],
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| here's what our example sentence and its dependencies look like:
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+codepen("030d1e4dfa6256cad8fdd59e6aefecbe", 460)
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44
website/docs/usage/_spacy-101/_similarity.jade
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44
website/docs/usage/_spacy-101/_similarity.jade
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@ -0,0 +1,44 @@
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//- 💫 DOCS > USAGE > SPACY 101 > SIMILARITY
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p
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| spaCy is able to compare two objects, and make a prediction of
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| #[strong how similar they are]. Predicting similarity is useful for
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| building recommendation systems or flagging duplicates. For example, you
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| can suggest a user content that's similar to what they're currently
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| looking at, or label a support ticket as a duplicate, if it's very
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| similar to an already existing one.
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p
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| Each #[code Doc], #[code Span] and #[code Token] comes with a
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| #[+api("token#similarity") #[code .similarity()]] method that lets you
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| compare it with another object, and determine the similarity. Of course
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| similarity is always subjective – whether "dog" and "cat" are similar
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| really depends on how you're looking at it. spaCy's similarity model
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| usually assumes a pretty general-purpose definition of similarity.
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+code.
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tokens = nlp(u'dog cat banana')
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for token1 in tokens:
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for token2 in tokens:
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print(token1.similarity(token2))
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+aside
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| #[strong #[+procon("neutral", 16)] similarity:] identical#[br]
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| #[strong #[+procon("pro", 16)] similarity:] similar (higher is more similar) #[br]
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| #[strong #[+procon("con", 16)] similarity:] dissimilar (lower is less similar)
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+table(["", "dog", "cat", "banana"])
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each cells, label in {"dog": [1.00, 0.80, 0.24], "cat": [0.80, 1.00, 0.28], "banana": [0.24, 0.28, 1.00]}
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+row
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+cell.u-text-label.u-color-theme=label
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for cell in cells
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+cell #[code=cell.toFixed(2)]
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| #[+procon(cell < 0.5 ? "con" : cell != 1 ? "pro" : "neutral")]
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p
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| In this case, the model's predictions are pretty on point. A dog is very
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| similar to a cat, whereas a banana is not very similar to either of them.
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| Identical tokens are obviously 100% similar to each other (just not always
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| exactly #[code 1.0], because of vector math and floating point
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| imprecisions).
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18
website/docs/usage/_spacy-101/_tokenization.jade
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18
website/docs/usage/_spacy-101/_tokenization.jade
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@ -0,0 +1,18 @@
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//- 💫 DOCS > USAGE > SPACY 101 > TOKENIZATION
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p
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| During processing, spaCy first #[strong tokenizes] the text, i.e.
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| segments it into words, punctuation and so on. For example, punctuation
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| at the end of a sentence should be split off – whereas "U.K." should
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| remain one token. This is done by applying rules specific to each
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| language. Each #[code Doc] consists of individual tokens, and we can
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| simply iterate over them:
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+code.
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for token in doc:
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print(token.text)
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+table([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).u-text-center
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+row
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for cell in ["Apple", "is", "looking", "at", "buying", "U.K.", "startup", "for", "$", "1", "billion"]
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+cell=cell
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152
website/docs/usage/_spacy-101/_word-vectors.jade
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152
website/docs/usage/_spacy-101/_word-vectors.jade
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@ -0,0 +1,152 @@
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//- 💫 DOCS > USAGE > SPACY 101 > WORD VECTORS
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p
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| Similarity is determined by comparing #[strong word vectors] or "word
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| embeddings", multi-dimensional meaning representations of a word. Word
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| vectors can be generated using an algorithm like
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| #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
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| #[+a("/docs/usage/models") default models] come with
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| #[strong 300-dimensional vectors], that look like this:
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+code("banana.vector", false, false, 250).
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array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
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3.28450017e-02, -4.19569999e-01, 7.20689967e-02,
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-3.74760002e-01, 5.74599989e-02, -1.24009997e-02,
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5.29489994e-01, -5.23800015e-01, -1.97710007e-01,
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-3.41470003e-01, 5.33169985e-01, -2.53309999e-02,
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1.73800007e-01, 1.67720005e-01, 8.39839995e-01,
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5.51070012e-02, 1.05470002e-01, 3.78719985e-01,
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2.42750004e-01, 1.47449998e-02, 5.59509993e-01,
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1.25210002e-01, -6.75960004e-01, 3.58420014e-01,
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-4.00279984e-02, 9.59490016e-02, -5.06900012e-01,
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-8.53179991e-02, 1.79800004e-01, 3.38669986e-01,
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1.32300004e-01, 3.10209990e-01, 2.18779996e-01,
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1.68530002e-01, 1.98740005e-01, -5.73849976e-01,
|
||||
-1.06490001e-01, 2.66689986e-01, 1.28380001e-01,
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-1.28030002e-01, -1.32839993e-01, 1.26570001e-01,
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8.67229998e-01, 9.67210010e-02, 4.83060002e-01,
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2.12709993e-01, -5.49900010e-02, -8.24249983e-02,
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2.24079996e-01, 2.39749998e-01, -6.22599982e-02,
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6.21940017e-01, -5.98999977e-01, 4.32009995e-01,
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2.81430006e-01, 3.38420011e-02, -4.88150001e-01,
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||||
-2.13589996e-01, 2.74010003e-01, 2.40950003e-01,
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4.59500015e-01, -1.86049998e-01, -1.04970002e+00,
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-9.73049998e-02, -1.89080000e-01, -7.09290028e-01,
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4.01950002e-01, -1.87680006e-01, 5.16870022e-01,
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1.25200003e-01, 8.41499984e-01, 1.20970003e-01,
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8.82389992e-02, -2.91959997e-02, 1.21510006e-03,
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5.68250008e-02, -2.74210006e-01, 2.55640000e-01,
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6.97930008e-02, -2.22580001e-01, -3.60060006e-01,
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-2.24020004e-01, -5.36990017e-02, 1.20220006e+00,
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5.45350015e-01, -5.79980016e-01, 1.09049998e-01,
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4.21669990e-01, 2.06619993e-01, 1.29360005e-01,
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-4.14570011e-02, -6.67770028e-01, 4.04670000e-01,
|
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-1.52179999e-02, -2.76400000e-01, -1.56110004e-01,
|
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-7.91980028e-02, 4.00369987e-02, -1.29439995e-01,
|
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-2.40900001e-04, -2.67850012e-01, -3.81150007e-01,
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-9.72450018e-01, 3.17259997e-01, -4.39509988e-01,
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4.19340014e-01, 1.83530003e-01, -1.52600005e-01,
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-1.08080000e-01, -1.03579998e+00, 7.62170032e-02,
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1.65189996e-01, 2.65259994e-04, 1.66160002e-01,
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-1.52810007e-01, 1.81229994e-01, 7.02740014e-01,
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5.79559989e-03, 5.16639985e-02, -5.97449988e-02,
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-2.75510013e-01, -3.90489995e-01, 6.11319989e-02,
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5.54300010e-01, -8.79969969e-02, -4.16810006e-01,
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3.28260005e-01, -5.25489986e-01, -4.42880005e-01,
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8.21829960e-03, 2.44859993e-01, -2.29819998e-01,
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-3.49810004e-01, 2.68940002e-01, 3.91660005e-01,
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-4.19039994e-01, 1.61909997e-01, -2.62630010e+00,
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6.41340017e-01, 3.97430003e-01, -1.28680006e-01,
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||||
-3.19460005e-01, -2.56330013e-01, -1.22199997e-01,
|
||||
3.22750002e-01, -7.99330026e-02, -1.53479993e-01,
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||||
3.15050006e-01, 3.05909991e-01, 2.60120004e-01,
|
||||
1.85530007e-01, -2.40429997e-01, 4.28860001e-02,
|
||||
4.06219989e-01, -2.42559999e-01, 6.38700008e-01,
|
||||
6.99829996e-01, -1.40430003e-01, 2.52090007e-01,
|
||||
4.89840001e-01, -6.10670000e-02, -3.67659986e-01,
|
||||
-5.50890028e-01, -3.82649988e-01, -2.08430007e-01,
|
||||
2.28320003e-01, 5.12179971e-01, 2.78679997e-01,
|
||||
4.76520002e-01, 4.79510017e-02, -3.40079993e-01,
|
||||
-3.28729987e-01, -4.19669986e-01, -7.54989982e-02,
|
||||
-3.89539987e-01, -2.96219997e-02, -3.40700001e-01,
|
||||
2.21699998e-01, -6.28560036e-02, -5.19029975e-01,
|
||||
-3.77739996e-01, -4.34770016e-03, -5.83010018e-01,
|
||||
-8.75459984e-02, -2.39289999e-01, -2.47109994e-01,
|
||||
-2.58870006e-01, -2.98940003e-01, 1.37150005e-01,
|
||||
2.98919994e-02, 3.65439989e-02, -4.96650010e-01,
|
||||
-1.81600004e-01, 5.29389977e-01, 2.19919994e-01,
|
||||
-4.45140004e-01, 3.77979994e-01, -5.70620000e-01,
|
||||
-4.69460003e-02, 8.18059966e-02, 1.92789994e-02,
|
||||
3.32459986e-01, -1.46200001e-01, 1.71560004e-01,
|
||||
3.99809986e-01, 3.62170011e-01, 1.28160000e-01,
|
||||
3.16439986e-01, 3.75690013e-01, -7.46899992e-02,
|
||||
-4.84800003e-02, -3.14009994e-01, -1.92860007e-01,
|
||||
-3.12940001e-01, -1.75529998e-02, -1.75139993e-01,
|
||||
-2.75870003e-02, -1.00000000e+00, 1.83870003e-01,
|
||||
8.14339995e-01, -1.89129993e-01, 5.09989977e-01,
|
||||
-9.19600017e-03, -1.92950002e-03, 2.81890005e-01,
|
||||
2.72470005e-02, 4.34089988e-01, -5.49669981e-01,
|
||||
-9.74259973e-02, -2.45399997e-01, -1.72030002e-01,
|
||||
-8.86500031e-02, -3.02980006e-01, -1.35910004e-01,
|
||||
-2.77649999e-01, 3.12860007e-03, 2.05559999e-01,
|
||||
-1.57720000e-01, -5.23079991e-01, -6.47010028e-01,
|
||||
-3.70139986e-01, 6.93930015e-02, 1.14009999e-01,
|
||||
2.75940001e-01, -1.38750002e-01, -2.72680014e-01,
|
||||
6.68910027e-01, -5.64539991e-02, 2.40170002e-01,
|
||||
-2.67300010e-01, 2.98599988e-01, 1.00830004e-01,
|
||||
5.55920005e-01, 3.28489989e-01, 7.68579990e-02,
|
||||
1.55279994e-01, 2.56359994e-01, -1.07720003e-01,
|
||||
-1.23590000e-01, 1.18270002e-01, -9.90289971e-02,
|
||||
-3.43279988e-01, 1.15019999e-01, -3.78080010e-01,
|
||||
-3.90120000e-02, -3.45930010e-01, -1.94040000e-01,
|
||||
-3.35799992e-01, -6.23340011e-02, 2.89189994e-01,
|
||||
2.80319989e-01, -5.37410021e-01, 6.27939999e-01,
|
||||
5.69549985e-02, 6.21469975e-01, -2.52819985e-01,
|
||||
4.16700006e-01, -1.01079997e-02, -2.54339993e-01,
|
||||
4.00029987e-01, 4.24320012e-01, 2.26720005e-01,
|
||||
1.75530002e-01, 2.30489999e-01, 2.83230007e-01,
|
||||
1.38820007e-01, 3.12180002e-03, 1.70570001e-01,
|
||||
3.66849989e-01, 2.52470002e-03, -6.40089989e-01,
|
||||
-2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
|
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-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
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p
|
||||
| The #[code .vector] attribute will return an object's vector.
|
||||
| #[+api("doc#vector") #[code Doc.vector]] and
|
||||
| #[+api("span#vector") #[code Span.vector]] will default to an average
|
||||
| of their token vectors. You can also check if a token has a vector
|
||||
| assigned, and get the L2 norm, which can be used to normalise
|
||||
| vectors.
|
||||
|
||||
+code.
|
||||
tokens = nlp(u'dog cat banana sasquatch')
|
||||
|
||||
for token in tokens:
|
||||
print(token.text, token.has_vector, token.vector_norm, token.is_oov)
|
||||
|
||||
+aside
|
||||
| #[strong Text]: The original token text.#[br]
|
||||
| #[strong has vector]: Does the token have a vector representation?#[br]
|
||||
| #[strong Vector norm]: The L2 norm of the token's vector (the square root
|
||||
| of the sum of the values squared)#[br]
|
||||
| #[strong is OOV]: Is the word out-of-vocabulary?
|
||||
|
||||
+table(["Text", "Has vector", "Vector norm", "OOV"])
|
||||
- var style = [0, 1, 1, 1]
|
||||
+annotation-row(["dog", true, 7.033672992262838, false], style)
|
||||
+annotation-row(["cat", true, 6.68081871208896, false], style)
|
||||
+annotation-row(["banana", true, 6.700014292148571, false], style)
|
||||
+annotation-row(["sasquatch", false, 0, true], style)
|
||||
|
||||
p
|
||||
| The words "dog", "cat" and "banana" are all pretty common in English, so
|
||||
| they're part of the model's vocabulary, and come with a vector. The word
|
||||
| "sasquatch" on the other hand is a lot less common and out-of-vocabulary
|
||||
| – so its vector representation consists of 300 dimensions of #[code 0],
|
||||
| which means it's practically nonexistent.
|
||||
|
||||
p
|
||||
| If your application will benefit from a large vocabulary with more
|
||||
| vectors, you should consider using one of the
|
||||
| #[+a("/docs/usage/models#available") larger models] instead of the default,
|
||||
| smaller ones, which usually come with a clipped vocabulary.
|
||||
Loading…
Reference in New Issue
Block a user