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63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
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After tokenization, spaCy can **parse** and **tag** a given `Doc`. This is where
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the statistical model comes in, which enables spaCy to **make a prediction** of
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which tag or label most likely applies in this context. A model consists of
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binary data and is produced by showing a system enough examples for it to make
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predictions that generalize across the language – for example, a word following
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"the" in English is most likely a noun.
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Linguistic annotations are available as
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[`Token` attributes](/api/token#attributes). Like many NLP libraries, spaCy
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**encodes all strings to hash values** to reduce memory usage and improve
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efficiency. So to get the readable string representation of an attribute, we
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need to add an underscore `_` to its name:
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```python
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### {executable="true"}
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import spacy
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nlp = spacy.load('en_core_web_sm')
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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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```
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> - **Text:** The original word text.
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> - **Lemma:** The base form of the word.
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> - **POS:** The simple part-of-speech tag.
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> - **Tag:** The detailed part-of-speech tag.
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> - **Dep:** Syntactic dependency, i.e. the relation between tokens.
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> - **Shape:** The word shape – capitalization, punctuation, digits.
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> - **is alpha:** Is the token an alpha character?
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> - **is stop:** Is the token part of a stop list, i.e. the most common words of
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> the language?
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| Text | Lemma | POS | Tag | Dep | Shape | alpha | stop |
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| ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
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| Apple | apple | `PROPN` | `NNP` | `nsubj` | `Xxxxx` | `True` | `False` |
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| is | be | `VERB` | `VBZ` | `aux` | `xx` | `True` | `True` |
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| looking | look | `VERB` | `VBG` | `ROOT` | `xxxx` | `True` | `False` |
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| at | at | `ADP` | `IN` | `prep` | `xx` | `True` | `True` |
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| buying | buy | `VERB` | `VBG` | `pcomp` | `xxxx` | `True` | `False` |
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| U.K. | u.k. | `PROPN` | `NNP` | `compound` | `X.X.` | `False` | `False` |
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| startup | startup | `NOUN` | `NN` | `dobj` | `xxxx` | `True` | `False` |
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| for | for | `ADP` | `IN` | `prep` | `xxx` | `True` | `True` |
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| \$ | \$ | `SYM` | `$` | `quantmod` | `$` | `False` | `False` |
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| 1 | 1 | `NUM` | `CD` | `compound` | `d` | `False` | `False` |
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| billion | billion | `NUM` | `CD` | `probj` | `xxxx` | `True` | `False` |
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> #### Tip: Understanding tags and labels
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>
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> Most of the tags and labels look pretty abstract, and they vary between
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> languages. `spacy.explain` will show you a short description – for example,
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> `spacy.explain("VBZ")` returns "verb, 3rd person singular present".
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Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
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our example sentence and its dependencies look like:
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import DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from
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'components/embed'
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<Iframe title="displaCy visualization of dependencies and entities" html={DisplaCyLongHtml} height={450} />
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