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<!--- Provide a general summary of your changes in the title. --> ## Description * tidy up and adjust Cython code to code style * improve docstrings and make calling `help()` nicer * add URLs to new docs pages to docstrings wherever possible, mostly to user-facing objects * fix various typos and inconsistencies in docs ### Types of change enhancement, docs ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
1212 lines
51 KiB
Markdown
1212 lines
51 KiB
Markdown
---
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title: Rule-based matching
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teaser: Find phrases and tokens, and match entities
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menu:
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- ['Token Matcher', 'matcher']
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- ['Phrase Matcher', 'phrasematcher']
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- ['Entity Ruler', 'entityruler']
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- ['Models & Rules', 'models-rules']
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---
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Compared to using regular expressions on raw text, spaCy's rule-based matcher
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engines and components not only let you find you the words and phrases you're
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looking for – they also give you access to the tokens within the document and
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their relationships. This means you can easily access and analyze the
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surrounding tokens, merge spans into single tokens or add entries to the named
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entities in `doc.ents`.
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<Accordion title="Should I use rules or train a model?">
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For complex tasks, it's usually better to train a statistical entity recognition
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model. However, statistical models require training data, so for many
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situations, rule-based approaches are more practical. This is especially true at
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the start of a project: you can use a rule-based approach as part of a data
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collection process, to help you "bootstrap" a statistical model.
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Training a model is useful if you have some examples and you want your system to
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be able to **generalize** based on those examples. It works especially well if
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there are clues in the _local context_. For instance, if you're trying to detect
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person or company names, your application may benefit from a statistical named
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entity recognition model.
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Rule-based systems are a good choice if there's a more or less **finite number**
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of examples that you want to find in the data, or if there's a very **clear,
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structured pattern** you can express with token rules or regular expressions.
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For instance, country names, IP addresses or URLs are things you might be able
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to handle well with a purely rule-based approach.
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You can also combine both approaches and improve a statistical model with rules
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to handle very specific cases and boost accuracy. For details, see the section
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on [rule-based entity recognition](#entityruler).
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</Accordion>
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<Accordion title="When should I use the token matcher vs. the phrase matcher?">
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The `PhraseMatcher` is useful if you already have a large terminology list or
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gazetteer consisting of single or multi-token phrases that you want to find
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exact instances of in your data. As of spaCy v2.1.0, you can also match on the
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`LOWER` attribute for fast and case-insensitive matching.
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The `Matcher` isn't as blazing fast as the `PhraseMatcher`, since it compares
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across individual token attributes. However, it allows you to write very
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abstract representations of the tokens you're looking for, using lexical
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attributes, linguistic features predicted by the model, operators, set
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membership and rich comparison. For example, you can find a noun, followed by a
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verb with the lemma "love" or "like", followed by an optional determiner and
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another token that's at least ten characters long.
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</Accordion>
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## Token-based matching {#matcher}
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spaCy features a rule-matching engine, the [`Matcher`](/api/matcher), that
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operates over tokens, similar to regular expressions. The rules can refer to
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token annotations (e.g. the token `text` or `tag_`, and flags (e.g. `IS_PUNCT`).
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The rule matcher also lets you pass in a custom callback to act on matches – for
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example, to merge entities and apply custom labels. You can also associate
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patterns with entity IDs, to allow some basic entity linking or disambiguation.
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To match large terminology lists, you can use the
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[`PhraseMatcher`](/api/phrasematcher), which accepts `Doc` objects as match
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patterns.
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### Adding patterns {#adding-patterns}
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Let's say we want to enable spaCy to find a combination of three tokens:
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1. A token whose **lowercase form matches "hello"**, e.g. "Hello" or "HELLO".
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2. A token whose **`is_punct` flag is set to `True`**, i.e. any punctuation.
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3. A token whose **lowercase form matches "world"**, e.g. "World" or "WORLD".
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```python
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[{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
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```
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<Infobox title="Important note" variant="danger">
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When writing patterns, keep in mind that **each dictionary** represents **one
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token**. If spaCy's tokenization doesn't match the tokens defined in a pattern,
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the pattern is not going to produce any results. When developing complex
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patterns, make sure to check examples against spaCy's tokenization:
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```python
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doc = nlp(u"A complex-example,!")
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print([token.text for token in doc])
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```
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</Infobox>
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First, we initialize the `Matcher` with a vocab. The matcher must always share
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the same vocab with the documents it will operate on. We can now call
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[`matcher.add()`](/api/matcher#add) with an ID and our custom pattern. The
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second argument lets you pass in an optional callback function to invoke on a
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successful match. For now, we set it to `None`.
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```python
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### {executable="true"}
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import spacy
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from spacy.matcher import Matcher
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nlp = spacy.load("en_core_web_sm")
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matcher = Matcher(nlp.vocab)
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# Add match ID "HelloWorld" with no callback and one pattern
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pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
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matcher.add("HelloWorld", None, pattern)
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doc = nlp(u"Hello, world! Hello world!")
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matches = matcher(doc)
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for match_id, start, end in matches:
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string_id = nlp.vocab.strings[match_id] # Get string representation
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span = doc[start:end] # The matched span
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print(match_id, string_id, start, end, span.text)
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```
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The matcher returns a list of `(match_id, start, end)` tuples – in this case,
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`[('15578876784678163569', 0, 2)]`, which maps to the span `doc[0:2]` of our
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original document. The `match_id` is the [hash value](/usage/spacy-101#vocab) of
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the string ID "HelloWorld". To get the string value, you can look up the ID in
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the [`StringStore`](/api/stringstore).
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```python
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for match_id, start, end in matches:
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string_id = nlp.vocab.strings[match_id] # 'HelloWorld'
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span = doc[start:end] # The matched span
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```
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Optionally, we could also choose to add more than one pattern, for example to
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also match sequences without punctuation between "hello" and "world":
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```python
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matcher.add("HelloWorld", None,
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[{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}],
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[{"LOWER": "hello"}, {"LOWER": "world"}])
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```
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By default, the matcher will only return the matches and **not do anything
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else**, like merge entities or assign labels. This is all up to you and can be
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defined individually for each pattern, by passing in a callback function as the
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`on_match` argument on `add()`. This is useful, because it lets you write
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entirely custom and **pattern-specific logic**. For example, you might want to
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merge _some_ patterns into one token, while adding entity labels for other
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pattern types. You shouldn't have to create different matchers for each of those
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processes.
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#### Available token attributes {#adding-patterns-attributes}
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The available token pattern keys are uppercase versions of the
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[`Token` attributes](/api/token#attributes). The most relevant ones for
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rule-based matching are:
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| Attribute | Type | Description |
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| -------------------------------------- | ------- | ------------------------------------------------------------------------------------------------ |
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| `ORTH` | unicode | The exact verbatim text of a token. |
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| `TEXT` <Tag variant="new">2.1</Tag> | unicode | The exact verbatim text of a token. |
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| `LOWER` | unicode | The lowercase form of the token text. |
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| `LENGTH` | int | The length of the token text. |
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| `IS_ALPHA`, `IS_ASCII`, `IS_DIGIT` | bool | Token text consists of alphanumeric characters, ASCII characters, digits. |
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| `IS_LOWER`, `IS_UPPER`, `IS_TITLE` | bool | Token text is in lowercase, uppercase, titlecase. |
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| `IS_PUNCT`, `IS_SPACE`, `IS_STOP` | bool | Token is punctuation, whitespace, stop word. |
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| `LIKE_NUM`, `LIKE_URL`, `LIKE_EMAIL` | bool | Token text resembles a number, URL, email. |
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| `POS`, `TAG`, `DEP`, `LEMMA`, `SHAPE` | unicode | The token's simple and extended part-of-speech tag, dependency label, lemma, shape. |
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| `ENT_TYPE` | unicode | The token's entity label. |
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| `_` <Tag variant="new">2.1</Tag> | dict | Properties in [custom extension attributes](/processing-pipelines#custom-components-attributes). |
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<Infobox title="Tip: Try the interactive matcher explorer">
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[](https://explosion.ai/demos/matcher)
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The [Matcher Explorer](https://explosion.ai/demos/matcher) lets you test the
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rule-based `Matcher` by creating token patterns interactively and running them
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over your text. Each token can set multiple attributes like text value,
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part-of-speech tag or boolean flags. The token-based view lets you explore how
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spaCy processes your text – and why your pattern matches, or why it doesn't.
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</Infobox>
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#### Extended pattern syntax and attributes {#adding-patterns-attributes-extended new="2.1"}
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Instead of mapping to a single value, token patterns can also map to a
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**dictionary of properties**. For example, to specify that the value of a lemma
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should be part of a list of values, or to set a minimum character length. The
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following rich comparison attributes are available:
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> #### Example
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>
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> ```python
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> # Matches "love cats" or "likes flowers"
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> pattern1 = [{"LEMMA": {"IN": ["like", "love"]}},
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> {"POS": "NOUN"}]
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>
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> # Matches tokens of length >= 10
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> pattern2 = [{"LENGTH": {">=": 10}}]
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> ```
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| Attribute | Value Type | Description |
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| -------------------------- | ---------- | --------------------------------------------------------------------------------- |
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| `IN` | any | Attribute value is member of a list. |
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| `NOT_IN` | any | Attribute value is _not_ member of a list. |
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| `==`, `>=`, `<=`, `>`, `<` | int, float | Attribute value is equal, greater or equal, smaller or equal, greater or smaller. |
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#### Regular expressions {#regex new="2.1"}
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In some cases, only matching tokens and token attributes isn't enough – for
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example, you might want to match different spellings of a word, without having
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to add a new pattern for each spelling.
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```python
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pattern = [{"TEXT": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}},
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{"LOWER": "president"}]
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```
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`'REGEX'` as an operator (instead of a top-level property that only matches on
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the token's text) allows defining rules for any string value, including custom
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attributes:
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```python
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# Match tokens with fine-grained POS tags starting with 'V'
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pattern = [{"TAG": {"REGEX": "^V"}}]
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# Match custom attribute values with regular expressions
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pattern = [{"_": {"country": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}}}]
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```
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<Infobox title="Regular expressions in older versions" variant="warning">
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Versions before v2.1.0 don't yet support the `REGEX` operator. A simple solution
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is to match a regular expression on the `Doc.text` with `re.finditer` and use
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the [`Doc.char_span`](/api/doc#char_span) method to create a `Span` from the
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character indices of the match.
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You can also use the regular expression by converting it to a **binary token
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flag**. [`Vocab.add_flag`](/api/vocab#add_flag) returns a flag ID which you can
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use as a key of a token match pattern.
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```python
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definitely_flag = lambda text: bool(re.compile(r"deff?in[ia]tely").match(text))
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IS_DEFINITELY = nlp.vocab.add_flag(definitely_flag)
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pattern = [{IS_DEFINITELY: True}]
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```
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</Infobox>
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#### Operators and quantifiers {#quantifiers}
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The matcher also lets you use quantifiers, specified as the `'OP'` key.
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Quantifiers let you define sequences of tokens to be matched, e.g. one or more
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punctuation marks, or specify optional tokens. Note that there are no nested or
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scoped quantifiers – instead, you can build those behaviors with `on_match`
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callbacks.
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| OP | Description |
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| --- | ---------------------------------------------------------------- |
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| `!` | Negate the pattern, by requiring it to match exactly 0 times. |
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| `?` | Make the pattern optional, by allowing it to match 0 or 1 times. |
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| `+` | Require the pattern to match 1 or more times. |
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| `*` | Allow the pattern to match zero or more times. |
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> #### Example
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>
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> ```python
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> pattern = [{"LOWER": "hello"},
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> {"IS_PUNCT": True, "OP": "?"}]
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> ```
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<Infobox title="Note on operator behaviour" variant="warning">
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In versions before v2.1.0, the semantics of the `+` and `*` operators behave
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inconsistently. They were usually interpreted "greedily", i.e. longer matches
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are returned where possible. However, if you specify two `+` and `*` patterns in
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a row and their matches overlap, the first operator will behave non-greedily.
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This quirk in the semantics is corrected in spaCy v2.1.0.
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</Infobox>
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#### Using wildcard token patterns {#adding-patterns-wildcard new="2"}
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While the token attributes offer many options to write highly specific patterns,
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you can also use an empty dictionary, `{}` as a wildcard representing **any
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token**. This is useful if you know the context of what you're trying to match,
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but very little about the specific token and its characters. For example, let's
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say you're trying to extract people's user names from your data. All you know is
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that they are listed as "User name: {username}". The name itself may contain any
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character, but no whitespace – so you'll know it will be handled as one token.
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```python
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[{"ORTH": "User"}, {"ORTH": "name"}, {"ORTH": ":"}, {}]
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```
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### Adding on_match rules {#on_match}
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To move on to a more realistic example, let's say you're working with a large
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corpus of blog articles, and you want to match all mentions of "Google I/O"
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(which spaCy tokenizes as `['Google', 'I', '/', 'O'`]). To be safe, you only
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match on the uppercase versions, in case someone has written it as "Google i/o".
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```python
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### {executable="true"}
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import spacy
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from spacy.matcher import Matcher
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from spacy.tokens import Span
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nlp = spacy.load("en_core_web_sm")
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matcher = Matcher(nlp.vocab)
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def add_event_ent(matcher, doc, i, matches):
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# Get the current match and create tuple of entity label, start and end.
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# Append entity to the doc's entity. (Don't overwrite doc.ents!)
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match_id, start, end = matches[i]
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entity = Span(doc, start, end, label="EVENT")
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doc.ents += (entity,)
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print(entity.text)
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pattern = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]
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matcher.add("GoogleIO", add_event_ent, pattern)
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doc = nlp(u"This is a text about Google I/O.")
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matches = matcher(doc)
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```
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A very similar logic has been implemented in the built-in
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[`EntityRuler`](/api/entityruler) by the way. It also takes care of handling
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overlapping matches, which you would otherwise have to take care of yourself.
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> #### Tip: Visualizing matches
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>
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> When working with entities, you can use [displaCy](/api/top-level#displacy) to
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> quickly generate a NER visualization from your updated `Doc`, which can be
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> exported as an HTML file:
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>
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> ```python
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> from spacy import displacy
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> html = displacy.render(doc, style="ent", page=True,
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> options={"ents": ["EVENT"]})
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> ```
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>
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> For more info and examples, see the usage guide on
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> [visualizing spaCy](/usage/visualizers).
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We can now call the matcher on our documents. The patterns will be matched in
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the order they occur in the text. The matcher will then iterate over the
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matches, look up the callback for the match ID that was matched, and invoke it.
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```python
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doc = nlp(YOUR_TEXT_HERE)
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matcher(doc)
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```
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When the callback is invoked, it is passed four arguments: the matcher itself,
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the document, the position of the current match, and the total list of matches.
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This allows you to write callbacks that consider the entire set of matched
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phrases, so that you can resolve overlaps and other conflicts in whatever way
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you prefer.
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| Argument | Type | Description |
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| --------- | --------- | -------------------------------------------------------------------------------------------------------------------- |
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| `matcher` | `Matcher` | The matcher instance. |
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| `doc` | `Doc` | The document the matcher was used on. |
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| `i` | int | Index of the current match (`matches[i`]). |
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| `matches` | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. |
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### Using custom pipeline components {#matcher-pipeline}
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Let's say your data also contains some annoying pre-processing artifacts, like
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leftover HTML line breaks (e.g. `<br>` or `<BR/>`). To make your text easier to
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analyze, you want to merge those into one token and flag them, to make sure you
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can ignore them later. Ideally, this should all be done automatically as you
|
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process the text. You can achieve this by adding a
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[custom pipeline component](/usage/processing-pipelines#custom-components)
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that's called on each `Doc` object, merges the leftover HTML spans and sets an
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attribute `bad_html` on the token.
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|
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```python
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### {executable="true"}
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import spacy
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from spacy.matcher import Matcher
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from spacy.tokens import Token
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|
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# We're using a class because the component needs to be initialised with
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# the shared vocab via the nlp object
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class BadHTMLMerger(object):
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def __init__(self, nlp):
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# Register a new token extension to flag bad HTML
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Token.set_extension("bad_html", default=False)
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self.matcher = Matcher(nlp.vocab)
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self.matcher.add(
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"BAD_HTML",
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None,
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[{"ORTH": "<"}, {"LOWER": "br"}, {"ORTH": ">"}],
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[{"ORTH": "<"}, {"LOWER": "br/"}, {"ORTH": ">"}],
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)
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def __call__(self, doc):
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# This method is invoked when the component is called on a Doc
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matches = self.matcher(doc)
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spans = [] # Collect the matched spans here
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for match_id, start, end in matches:
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spans.append(doc[start:end])
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with doc.retokenize() as retokenizer:
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for span in spans:
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retokenizer.merge(span)
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for token in span:
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token._.bad_html = True # Mark token as bad HTML
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return doc
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nlp = spacy.load("en_core_web_sm")
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html_merger = BadHTMLMerger(nlp)
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nlp.add_pipe(html_merger, last=True) # Add component to the pipeline
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doc = nlp(u"Hello<br>world! <br/> This is a test.")
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for token in doc:
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print(token.text, token._.bad_html)
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```
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||
|
||
Instead of hard-coding the patterns into the component, you could also make it
|
||
take a path to a JSON file containing the patterns. This lets you reuse the
|
||
component with different patterns, depending on your application:
|
||
|
||
```python
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html_merger = BadHTMLMerger(nlp, path="/path/to/patterns.json")
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```
|
||
|
||
<Infobox title="📖 Processing pipelines">
|
||
|
||
For more details and examples of how to **create custom pipeline components**
|
||
and **extension attributes**, see the
|
||
[usage guide](/usage/processing-pipelines).
|
||
|
||
</Infobox>
|
||
|
||
### Example: Using linguistic annotations {#example1}
|
||
|
||
Let's say you're analyzing user comments and you want to find out what people
|
||
are saying about Facebook. You want to start off by finding adjectives following
|
||
"Facebook is" or "Facebook was". This is obviously a very rudimentary solution,
|
||
but it'll be fast, and a great way to get an idea for what's in your data. Your
|
||
pattern could look like this:
|
||
|
||
```python
|
||
[{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"}, {"POS": "ADJ"}]
|
||
```
|
||
|
||
This translates to a token whose lowercase form matches "facebook" (like
|
||
Facebook, facebook or FACEBOOK), followed by a token with the lemma "be" (for
|
||
example, is, was, or 's), followed by an **optional** adverb, followed by an
|
||
adjective. Using the linguistic annotations here is especially useful, because
|
||
you can tell spaCy to match "Facebook's annoying", but **not** "Facebook's
|
||
annoying ads". The optional adverb makes sure you won't miss adjectives with
|
||
intensifiers, like "pretty awful" or "very nice".
|
||
|
||
To get a quick overview of the results, you could collect all sentences
|
||
containing a match and render them with the
|
||
[displaCy visualizer](/usage/visualizers). In the callback function, you'll have
|
||
access to the `start` and `end` of each match, as well as the parent `Doc`. This
|
||
lets you determine the sentence containing the match, `doc[start : end`.sent],
|
||
and calculate the start and end of the matched span within the sentence. Using
|
||
displaCy in ["manual" mode](/usage/visualizers#manual-usage) lets you pass in a
|
||
list of dictionaries containing the text and entities to render.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy import displacy
|
||
from spacy.matcher import Matcher
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
matcher = Matcher(nlp.vocab)
|
||
matched_sents = [] # Collect data of matched sentences to be visualized
|
||
|
||
def collect_sents(matcher, doc, i, matches):
|
||
match_id, start, end = matches[i]
|
||
span = doc[start:end] # Matched span
|
||
sent = span.sent # Sentence containing matched span
|
||
# Append mock entity for match in displaCy style to matched_sents
|
||
# get the match span by ofsetting the start and end of the span with the
|
||
# start and end of the sentence in the doc
|
||
match_ents = [{
|
||
"start": span.start_char - sent.start_char,
|
||
"end": span.end_char - sent.start_char,
|
||
"label": "MATCH",
|
||
}]
|
||
matched_sents.append({"text": sent.text, "ents": match_ents})
|
||
|
||
pattern = [{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"},
|
||
{"POS": "ADJ"}]
|
||
matcher.add("FacebookIs", collect_sents, pattern) # add pattern
|
||
doc = nlp(u"I'd say that Facebook is evil. – Facebook is pretty cool, right?")
|
||
matches = matcher(doc)
|
||
|
||
# Serve visualization of sentences containing match with displaCy
|
||
# set manual=True to make displaCy render straight from a dictionary
|
||
# (if you're not running the code within a Jupyer environment, you can
|
||
# use displacy.serve instead)
|
||
displacy.render(matched_sents, style="ent", manual=True)
|
||
```
|
||
|
||
### Example: Phone numbers {#example2}
|
||
|
||
Phone numbers can have many different formats and matching them is often tricky.
|
||
During tokenization, spaCy will leave sequences of numbers intact and only split
|
||
on whitespace and punctuation. This means that your match pattern will have to
|
||
look out for number sequences of a certain length, surrounded by specific
|
||
punctuation – depending on the
|
||
[national conventions](https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers).
|
||
|
||
The `IS_DIGIT` flag is not very helpful here, because it doesn't tell us
|
||
anything about the length. However, you can use the `SHAPE` flag, with each `d`
|
||
representing a digit:
|
||
|
||
```python
|
||
[{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "dddd"},
|
||
{"ORTH": "-", "OP": "?"}, {"SHAPE": "dddd"}]
|
||
```
|
||
|
||
This will match phone numbers of the format **(123) 4567 8901** or **(123)
|
||
4567-8901**. To also match formats like **(123) 456 789**, you can add a second
|
||
pattern using `'ddd'` in place of `'dddd'`. By hard-coding some values, you can
|
||
match only certain, country-specific numbers. For example, here's a pattern to
|
||
match the most common formats of
|
||
[international German numbers](https://en.wikipedia.org/wiki/National_conventions_for_writing_telephone_numbers#Germany):
|
||
|
||
```python
|
||
[{"ORTH": "+"}, {"ORTH": "49"}, {"ORTH": "(", "OP": "?"}, {"SHAPE": "dddd"},
|
||
{"ORTH": ")", "OP": "?"}, {"SHAPE": "dddddd"}]
|
||
```
|
||
|
||
Depending on the formats your application needs to match, creating an extensive
|
||
set of rules like this is often better than training a model. It'll produce more
|
||
predictable results, is much easier to modify and extend, and doesn't require
|
||
any training data – only a set of test cases.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.matcher import Matcher
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
matcher = Matcher(nlp.vocab)
|
||
pattern = [{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "ddd"},
|
||
{"ORTH": "-", "OP": "?"}, {"SHAPE": "ddd"}]
|
||
matcher.add("PHONE_NUMBER", None, pattern)
|
||
|
||
doc = nlp(u"Call me at (123) 456 789 or (123) 456 789!")
|
||
print([t.text for t in doc])
|
||
matches = matcher(doc)
|
||
for match_id, start, end in matches:
|
||
span = doc[start:end]
|
||
print(span.text)
|
||
```
|
||
|
||
### Example: Hashtags and emoji on social media {#example3}
|
||
|
||
Social media posts, especially tweets, can be difficult to work with. They're
|
||
very short and often contain various emoji and hashtags. By only looking at the
|
||
plain text, you'll lose a lot of valuable semantic information.
|
||
|
||
Let's say you've extracted a large sample of social media posts on a specific
|
||
topic, for example posts mentioning a brand name or product. As the first step
|
||
of your data exploration, you want to filter out posts containing certain emoji
|
||
and use them to assign a general sentiment score, based on whether the expressed
|
||
emotion is positive or negative, e.g. 😀 or 😞. You also want to find, merge and
|
||
label hashtags like `#MondayMotivation`, to be able to ignore or analyze them
|
||
later.
|
||
|
||
> #### Note on sentiment analysis
|
||
>
|
||
> Ultimately, sentiment analysis is not always _that_ easy. In addition to the
|
||
> emoji, you'll also want to take specific words into account and check the
|
||
> `subtree` for intensifiers like "very", to increase the sentiment score. At
|
||
> some point, you might also want to train a sentiment model. However, the
|
||
> approach described in this example is very useful for **bootstrapping rules to
|
||
> collect training data**. It's also an incredibly fast way to gather first
|
||
> insights into your data – with about 1 million tweets, you'd be looking at a
|
||
> processing time of **under 1 minute**.
|
||
|
||
By default, spaCy's tokenizer will split emoji into separate tokens. This means
|
||
that you can create a pattern for one or more emoji tokens. Valid hashtags
|
||
usually consist of a `#`, plus a sequence of ASCII characters with no
|
||
whitespace, making them easy to match as well.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
from spacy.lang.en import English
|
||
from spacy.matcher import Matcher
|
||
|
||
nlp = English() # We only want the tokenizer, so no need to load a model
|
||
matcher = Matcher(nlp.vocab)
|
||
|
||
pos_emoji = [u"😀", u"😃", u"😂", u"🤣", u"😊", u"😍"] # Positive emoji
|
||
neg_emoji = [u"😞", u"😠", u"😩", u"😢", u"😭", u"😒"] # Negative emoji
|
||
|
||
# Add patterns to match one or more emoji tokens
|
||
pos_patterns = [[{"ORTH": emoji}] for emoji in pos_emoji]
|
||
neg_patterns = [[{"ORTH": emoji}] for emoji in neg_emoji]
|
||
|
||
# Function to label the sentiment
|
||
def label_sentiment(matcher, doc, i, matches):
|
||
match_id, start, end = matches[i]
|
||
if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string!
|
||
doc.sentiment += 0.1 # Add 0.1 for positive sentiment
|
||
elif doc.vocab.strings[match_id] == "SAD":
|
||
doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment
|
||
|
||
matcher.add("HAPPY", label_sentiment, *pos_patterns) # Add positive pattern
|
||
matcher.add("SAD", label_sentiment, *neg_patterns) # Add negative pattern
|
||
|
||
# Add pattern for valid hashtag, i.e. '#' plus any ASCII token
|
||
matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}])
|
||
|
||
doc = nlp(u"Hello world 😀 #MondayMotivation")
|
||
matches = matcher(doc)
|
||
for match_id, start, end in matches:
|
||
string_id = doc.vocab.strings[match_id] # Look up string ID
|
||
span = doc[start:end]
|
||
print(string_id, span.text)
|
||
```
|
||
|
||
Because the `on_match` callback receives the ID of each match, you can use the
|
||
same function to handle the sentiment assignment for both the positive and
|
||
negative pattern. To keep it simple, we'll either add or subtract `0.1` points –
|
||
this way, the score will also reflect combinations of emoji, even positive _and_
|
||
negative ones.
|
||
|
||
With a library like [Emojipedia](https://github.com/bcongdon/python-emojipedia),
|
||
we can also retrieve a short description for each emoji – for example, 😍's
|
||
official title is "Smiling Face With Heart-Eyes". Assigning it to a
|
||
[custom attribute](/usage/processing-pipelines#custom-components-attributes) on
|
||
the emoji span will make it available as `span._.emoji_desc`.
|
||
|
||
```python
|
||
from emojipedia import Emojipedia # Installation: pip install emojipedia
|
||
from spacy.tokens import Span # Get the global Span object
|
||
|
||
Span.set_extension("emoji_desc", default=None) # Register the custom attribute
|
||
|
||
def label_sentiment(matcher, doc, i, matches):
|
||
match_id, start, end = matches[i]
|
||
if doc.vocab.strings[match_id] == "HAPPY": # Don't forget to get string!
|
||
doc.sentiment += 0.1 # Add 0.1 for positive sentiment
|
||
elif doc.vocab.strings[match_id] == "SAD":
|
||
doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment
|
||
span = doc[start:end]
|
||
emoji = Emojipedia.search(span[0].text) # Get data for emoji
|
||
span._.emoji_desc = emoji.title # Assign emoji description
|
||
|
||
```
|
||
|
||
To label the hashtags, we can use a
|
||
[custom attribute](/usage/processing-pipelines#custom-components-attributes) set
|
||
on the respective token:
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.matcher import Matcher
|
||
from spacy.tokens import Token
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
matcher = Matcher(nlp.vocab)
|
||
|
||
# Add pattern for valid hashtag, i.e. '#' plus any ASCII token
|
||
matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}])
|
||
|
||
# Register token extension
|
||
Token.set_extension("is_hashtag", default=False)
|
||
|
||
doc = nlp(u"Hello world 😀 #MondayMotivation")
|
||
matches = matcher(doc)
|
||
hashtags = []
|
||
for match_id, start, end in matches:
|
||
if doc.vocab.strings[match_id] == "HASHTAG":
|
||
hashtags.append(doc[start:end])
|
||
with doc.retokenize() as retokenizer:
|
||
for span in spans:
|
||
retokenizer.merge(span)
|
||
for token in span:
|
||
token._.is_hashtag = True
|
||
|
||
for token in doc:
|
||
print(token.text, token._.is_hashtag)
|
||
```
|
||
|
||
To process a stream of social media posts, we can use
|
||
[`Language.pipe`](/api/language#pipe), which will return a stream of `Doc`
|
||
objects that we can pass to [`Matcher.pipe`](/api/matcher#pipe).
|
||
|
||
```python
|
||
docs = nlp.pipe(LOTS_OF_TWEETS)
|
||
matches = matcher.pipe(docs)
|
||
```
|
||
|
||
## Efficient phrase matching {#phrasematcher}
|
||
|
||
If you need to match large terminology lists, you can also use the
|
||
[`PhraseMatcher`](/api/phrasematcher) and create [`Doc`](/api/doc) objects
|
||
instead of token patterns, which is much more efficient overall. The `Doc`
|
||
patterns can contain single or multiple tokens.
|
||
|
||
### Adding phrase patterns {#adding-phrase-patterns}
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.matcher import PhraseMatcher
|
||
|
||
nlp = spacy.load('en_core_web_sm')
|
||
matcher = PhraseMatcher(nlp.vocab)
|
||
terminology_list = [u"Barack Obama", u"Angela Merkel", u"Washington, D.C."]
|
||
# Only run nlp.make_doc to speed things up
|
||
patterns = [nlp.make_doc(text) for text in terminology_list]
|
||
matcher.add("TerminologyList", None, *patterns)
|
||
|
||
doc = nlp(u"German Chancellor Angela Merkel and US President Barack Obama "
|
||
u"converse in the Oval Office inside the White House in Washington, D.C.")
|
||
matches = matcher(doc)
|
||
for match_id, start, end in matches:
|
||
span = doc[start:end]
|
||
print(span.text)
|
||
```
|
||
|
||
Since spaCy is used for processing both the patterns and the text to be matched,
|
||
you won't have to worry about specific tokenization – for example, you can
|
||
simply pass in `nlp(u"Washington, D.C.")` and won't have to write a complex
|
||
token pattern covering the exact tokenization of the term.
|
||
|
||
<Infobox title="Important note on creating patterns" variant="warning">
|
||
|
||
To create the patterns, each phrase has to be processed with the `nlp` object.
|
||
If you have a mode loaded, doing this in a loop or list comprehension can easily
|
||
become inefficient and slow. If you only need the tokenization and lexical
|
||
attributes, you can run [`nlp.make_doc`](/api/language#make_doc) instead, which
|
||
will only run the tokenizer. For an additional speed boost, you can also use the
|
||
[`nlp.tokenizer.pipe`](/api/tokenizer#pipe) method, which will process the texts
|
||
as a stream.
|
||
|
||
```diff
|
||
- patterns = [nlp(term) for term in LOTS_OF_TERMS]
|
||
+ patterns = [nlp.make_doc(term) for term in LOTS_OF_TERMS]
|
||
+ patterns = list(nlp.tokenizer.pipe(LOTS_OF_TERMS))
|
||
```
|
||
|
||
</Infobox>
|
||
|
||
### Matching on other token attributes {#phrasematcher-attrs new="2.1"}
|
||
|
||
By default, the `PhraseMatcher` will match on the verbatim token text, e.g.
|
||
`Token.text`. By setting the `attr` argument on initialization, you can change
|
||
**which token attribute the matcher should use** when comparing the phrase
|
||
pattern to the matched `Doc`. For example, using the attribute `LOWER` lets you
|
||
match on `Token.lower` and create case-insensitive match patterns:
|
||
|
||
```python
|
||
### {executable="true"}
|
||
from spacy.lang.en import English
|
||
from spacy.matcher import PhraseMatcher
|
||
|
||
nlp = English()
|
||
matcher = PhraseMatcher(nlp.vocab, attr="LOWER")
|
||
patterns = [nlp.make_doc(name) for name in [u"Angela Merkel", u"Barack Obama"]]
|
||
matcher.add("Names", None, *patterns)
|
||
|
||
doc = nlp(u"angela merkel and us president barack Obama")
|
||
for match_id, start, end in matcher(doc):
|
||
print("Matched based on lowercase token text:", doc[start:end])
|
||
```
|
||
|
||
Another possible use case is matching number tokens like IP addresses based on
|
||
their shape. This means that you won't have to worry about how those string will
|
||
be tokenized and you'll be able to find tokens and combinations of tokens based
|
||
on a few examples. Here, we're matching on the shapes `ddd.d.d.d` and
|
||
`ddd.ddd.d.d`:
|
||
|
||
```python
|
||
### {executable="true"}
|
||
from spacy.lang.en import English
|
||
from spacy.matcher import PhraseMatcher
|
||
|
||
nlp = English()
|
||
matcher = PhraseMatcher(nlp.vocab, attr="SHAPE")
|
||
matcher.add("IP", None, nlp(u"127.0.0.1"), nlp(u"127.127.0.0"))
|
||
|
||
doc = nlp(u"Often the router will have an IP address such as 192.168.1.1 or 192.168.2.1.")
|
||
for match_id, start, end in matcher(doc):
|
||
print("Matched based on token shape:", doc[start:end])
|
||
```
|
||
|
||
In theory, the same also works for attributes like `POS`. For example, a pattern
|
||
`nlp("I like cats")` matched based on its part-of-speech tag would return a
|
||
match for "I love dogs". You could also match on boolean flags like `IS_PUNCT`
|
||
to match phrases with the same sequence of punctuation and non-punctuation
|
||
tokens as the pattern. But this can easily get confusing and doesn't have much
|
||
of an advantage over writing one or two token patterns.
|
||
|
||
## Rule-based entity recognition {#entityruler new="2.1"}
|
||
|
||
The [`EntityRuler`](/api/entityruler) is an exciting new component that lets you
|
||
add named entities based on pattern dictionaries, and makes it easy to combine
|
||
rule-based and statistical named entity recognition for even more powerful
|
||
models.
|
||
|
||
### Entity Patterns {#entityruler-patterns}
|
||
|
||
Entity patterns are dictionaries with two keys: `"label"`, specifying the label
|
||
to assign to the entity if the pattern is matched, and `"pattern"`, the match
|
||
pattern. The entity ruler accepts two types of patterns:
|
||
|
||
1. **Phrase patterns** for exact string matches (string).
|
||
|
||
```python
|
||
{"label": "ORG", "pattern": "Apple"}
|
||
```
|
||
|
||
2. **Token patterns** with one dictionary describing one token (list).
|
||
|
||
```python
|
||
{"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
|
||
```
|
||
|
||
### Using the entity ruler {#entityruler-usage}
|
||
|
||
The [`EntityRuler`](/api/entityruler) is a pipeline component that's typically
|
||
added via [`nlp.add_pipe`](/api/language#add_pipe). When the `nlp` object is
|
||
called on a text, it will find matches in the `doc` and add them as entities to
|
||
the `doc.ents`, using the specified pattern label as the entity label.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
from spacy.lang.en import English
|
||
from spacy.pipeline import EntityRuler
|
||
|
||
nlp = English()
|
||
ruler = EntityRuler(nlp)
|
||
patterns = [{"label": "ORG", "pattern": "Apple"},
|
||
{"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}]
|
||
ruler.add_patterns(patterns)
|
||
nlp.add_pipe(ruler)
|
||
|
||
doc = nlp(u"Apple is opening its first big office in San Francisco.")
|
||
print([(ent.text, ent.label_) for ent in doc.ents])
|
||
```
|
||
|
||
The entity ruler is designed to integrate with spaCy's existing statistical
|
||
models and enhance the named entity recognizer. If it's added **before the
|
||
`"ner"` component**, the entity recognizer will respect the existing entity
|
||
spans and adjust its predictions around it. This can significantly improve
|
||
accuracy in some cases. If it's added **after the `"ner"` component**, the
|
||
entity ruler will only add spans to the `doc.ents` if they don't overlap with
|
||
existing entities predicted by the model. To overwrite overlapping entities, you
|
||
can set `overwrite_ents=True` on initialization.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.pipeline import EntityRuler
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
ruler = EntityRuler(nlp)
|
||
patterns = [{"label": "ORG", "pattern": "MyCorp Inc."}]
|
||
ruler.add_patterns(patterns)
|
||
nlp.add_pipe(ruler)
|
||
|
||
doc = nlp(u"MyCorp Inc. is a company in the U.S.")
|
||
print([(ent.text, ent.label_) for ent in doc.ents])
|
||
```
|
||
|
||
### Using pattern files {#entityruler-files}
|
||
|
||
The [`to_disk`](/api/entityruler#to_disk) and
|
||
[`from_disk`](/api/entityruler#from_disk) let you save and load patterns to and
|
||
from JSONL (newline-delimited JSON) files, containing one pattern object per
|
||
line.
|
||
|
||
```json
|
||
### patterns.jsonl
|
||
{"label": "ORG", "pattern": "Apple"}
|
||
{"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
|
||
```
|
||
|
||
```python
|
||
ruler.to_disk("./patterns.jsonl")
|
||
new_ruler = EntityRuler(nlp).from_disk("./patterns.jsonl")
|
||
```
|
||
|
||
<Infobox title="Integration with Prodigy">
|
||
|
||
If you're using the [Prodigy](https://prodi.gy) annotation tool, you might
|
||
recognize these pattern files from bootstrapping your named entity and text
|
||
classification labelling. The patterns for the `EntityRuler` follow the same
|
||
syntax, so you can use your existing Prodigy pattern files in spaCy, and vice
|
||
versa.
|
||
|
||
</Infobox>
|
||
|
||
When you save out an `nlp` object that has an `EntityRuler` added to its
|
||
pipeline, its patterns are automatically exported to the model directory:
|
||
|
||
```python
|
||
nlp = spacy.load("en_core_web_sm")
|
||
ruler = EntityRuler(nlp)
|
||
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
|
||
nlp.add_pipe(ruler)
|
||
nlp.to_disk("/path/to/model")
|
||
```
|
||
|
||
The saved model now includes the `"entity_ruler"` in its `"pipeline"` setting in
|
||
the `meta.json`, and the model directory contains a file `entityruler.jsonl`
|
||
with the patterns. When you load the model back in, all pipeline components will
|
||
be restored and deserialized – including the entity ruler. This lets you ship
|
||
powerful model packages with binary weights _and_ rules included!
|
||
|
||
## Combining models and rules {#models-rules}
|
||
|
||
You can combine statistical and rule-based components in a variety of ways.
|
||
Rule-based components can be used to improve the accuracy of statistical models,
|
||
by presetting tags, entities or sentence boundaries for specific tokens. The
|
||
statistical models will usually respect these preset annotations, which
|
||
sometimes improves the accuracy of other decisions. You can also use rule-based
|
||
components after a statistical model to correct common errors. Finally,
|
||
rule-based components can reference the attributes set by statistical models, in
|
||
order to implement more abstract logic.
|
||
|
||
### Example: Expanding named entities {#models-rules-ner}
|
||
|
||
When using the a pre-trained
|
||
[named entity recognition](/usage/linguistic-features/#named-entities) model to
|
||
extract information from your texts, you may find that the predicted span only
|
||
includes parts of the entity you're looking for. Sometimes, this happens if
|
||
statistical model predicts entities incorrectly. Other times, it happens if the
|
||
way the entity type way defined in the original training corpus doesn't match
|
||
what you need for your application.
|
||
|
||
> #### Where corpora come from
|
||
>
|
||
> Corpora used to train models from scratch are often produced in academia. They
|
||
> contain text from various sources with linguistic features labeled manually by
|
||
> human annotators (following a set of specific guidelines). The corpora are
|
||
> then distributed with evaluation data, so other researchers can benchmark
|
||
> their algorithms and everyone can report numbers on the same data. However,
|
||
> most applications need to learn information that isn't contained in any
|
||
> available corpus.
|
||
|
||
For example, the corpus spaCy's [English models](/models/en) were trained on
|
||
defines a `PERSON` entity as just the **person name**, without titles like "Mr"
|
||
or "Dr". This makes sense, because it makes it easier to resolve the entity type
|
||
back to a knowledge base. But what if your application needs the full names,
|
||
_including_ the titles?
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
|
||
print([(ent.text, ent.label_) for ent in doc.ents])
|
||
```
|
||
|
||
While you could try and teach the model a new definition of the `PERSON` entity
|
||
by [updating it](/usage/training/#example-train-ner) with more examples of spans
|
||
that include the title, this might not be the most efficient approach. The
|
||
existing model was trained on over 2 million words, so in order to completely
|
||
change the definition of an entity type, you might need a lot of training
|
||
examples. However, if you already have the predicted `PERSON` entities, you can
|
||
use a rule-based approach that checks whether they come with a title and if so,
|
||
expands the entity span by one token. After all, what all titles in this example
|
||
have in common is that _if_ they occur, they occur in the **previous token**
|
||
right before the person entity.
|
||
|
||
```python
|
||
### {highlight="7-11"}
|
||
from spacy.tokens import Span
|
||
|
||
def expand_person_entities(doc):
|
||
new_ents = []
|
||
for ent in doc.ents:
|
||
# Only check for title if it's a person and not the first token
|
||
if ent.label_ == "PERSON" and ent.start != 0:
|
||
prev_token = doc[ent.start - 1]
|
||
if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
|
||
new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
|
||
new_ents.append(new_ent)
|
||
else:
|
||
new_ents.append(ent)
|
||
doc.ents = new_ents
|
||
return doc
|
||
```
|
||
|
||
The above function takes a `Doc` object, modifies its `doc.ents` and returns it.
|
||
This is exactly what a [pipeline component](/usage/processing-pipelines) does,
|
||
so in order to let it run automatically when processing a text with the `nlp`
|
||
object, we can use [`nlp.add_pipe`](/api/language#add_pipe) to add it to the
|
||
current pipeline.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.tokens import Span
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
|
||
def expand_person_entities(doc):
|
||
new_ents = []
|
||
for ent in doc.ents:
|
||
if ent.label_ == "PERSON" and ent.start != 0:
|
||
prev_token = doc[ent.start - 1]
|
||
if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
|
||
new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
|
||
new_ents.append(new_ent)
|
||
else:
|
||
new_ents.append(ent)
|
||
doc.ents = new_ents
|
||
return doc
|
||
|
||
# Add the component after the named entity recognizer
|
||
nlp.add_pipe(expand_person_entities, after='ner')
|
||
|
||
doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
|
||
print([(ent.text, ent.label_) for ent in doc.ents])
|
||
```
|
||
|
||
An alternative approach would be to an
|
||
[extension attribute](/usage/processing-pipelines/#custom-components-attributes)
|
||
like `._.person_title` and add it to `Span` objects (which includes entity spans
|
||
in `doc.ents`). The advantage here is that the entity text stays intact and can
|
||
still be used to look up the name in a knowledge base. The following function
|
||
takes a `Span` object, checks the previous token if it's a `PERSON` entity and
|
||
returns the title if one is found. The `Span.doc` attribute gives us easy access
|
||
to the span's parent document.
|
||
|
||
```python
|
||
def get_person_title(span):
|
||
if span.label_ == "PERSON" and span.start != 0:
|
||
prev_token = span.doc[span.start - 1]
|
||
if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
|
||
return prev_token.text
|
||
```
|
||
|
||
We can now use the [`Span.set_extension`](/api/span#set_extension) method to add
|
||
the custom extension attribute `"person_title"`, using `get_person_title` as the
|
||
getter function.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.tokens import Span
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
|
||
def get_person_title(span):
|
||
if span.label_ == "PERSON" and span.start != 0:
|
||
prev_token = span.doc[span.start - 1]
|
||
if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
|
||
return prev_token.text
|
||
|
||
# Register the Span extension as 'person_title'
|
||
Span.set_extension("person_title", getter=get_person_title)
|
||
|
||
doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
|
||
print([(ent.text, ent.label_, ent._.person_title) for ent in doc.ents])
|
||
```
|
||
|
||
### Example: Using entities, part-of-speech tags and the dependency parse {#models-rules-pos-dep}
|
||
|
||
> #### Linguistic features
|
||
>
|
||
> This example makes extensive use of part-of-speech tag and dependency
|
||
> attributes and related `Doc`, `Token` and `Span` methods. For an introduction
|
||
> on this, see the guide on
|
||
> [linguistic features](http://localhost:8000/usage/linguistic-features/). Also
|
||
> see the [annotation specs](/api/annotation#pos-tagging) for details on the
|
||
> label schemes.
|
||
|
||
Let's say you want to parse professional biographies and extract the person
|
||
names and company names, and whether it's a company they're _currently_ working
|
||
at, or a _previous_ company. One approach could be to try and train a named
|
||
entity recognizer to predict `CURRENT_ORG` and `PREVIOUS_ORG` – but this
|
||
distinction is very subtle and something the entity recognizer may struggle to
|
||
learn. Nothing about "Acme Corp Inc." is inherently "current" or "previous".
|
||
|
||
However, the syntax of the sentence holds some very important clues: we can
|
||
check for trigger words like "work", whether they're **past tense** or **present
|
||
tense**, whether company names are attached to it and whether the person is the
|
||
subject. All of this information is available in the part-of-speech tags and the
|
||
dependency parse.
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
doc = nlp("Alex Smith worked at Acme Corp Inc.")
|
||
print([(ent.text, ent.label_) for ent in doc.ents])
|
||
```
|
||
|
||
> - `nsubj`: Nominal subject.
|
||
> - `prep`: Preposition.
|
||
> - `pobj`: Object of preposition.
|
||
> - `NNP`: Proper noun, singular.
|
||
> - `VBD`: Verb, past tense.
|
||
> - `IN`: Conjunction, subordinating or preposition.
|
||
|
||
|
||
|
||
In this example, "worked" is the root of the sentence and is a past tense verb.
|
||
Its subject is "Alex Smith", the person who worked. "at Acme Corp Inc." is a
|
||
prepositional phrase attached to the verb "worked". To extract this
|
||
relationship, we can start by looking at the predicted `PERSON` entities, find
|
||
their heads and check whether they're attached to a trigger word like "work".
|
||
Next, we can check for prepositional phrases attached to the head and whether
|
||
they contain an `ORG` entity. Finally, to determine whether the company
|
||
affiliation is current, we can check the head's part-of-speech tag.
|
||
|
||
```python
|
||
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
|
||
for ent in person_entities:
|
||
# Because the entity is a spans, we need to use its root token. The head
|
||
# is the syntactic governor of the person, e.g. the verb
|
||
head = ent.root.head
|
||
if head.lemma_ == "work":
|
||
# Check if the children contain a preposition
|
||
preps = [token for token in head.children if token.dep_ == "prep"]
|
||
for prep in preps:
|
||
# Check if tokens part of ORG entities are in the preposition's
|
||
# children, e.g. at -> Acme Corp Inc.
|
||
orgs = [token for token in prep.children if token.ent_type_ == "ORG"]
|
||
# If the verb is in past tense, the company was a previous company
|
||
print({'person': ent, 'orgs': orgs, 'past': head.tag_ == "VBD"})
|
||
```
|
||
|
||
To apply this logic automatically when we process a text, we can add it to the
|
||
`nlp` object as a
|
||
[custom pipeline component](/usage/processing-pipelines/#custom-components). The
|
||
above logic also expects that entities are merged into single tokens. spaCy
|
||
ships with a handy built-in `merge_entities` that takes care of that. Instead of
|
||
just printing the result, you could also write it to
|
||
[custom attributes](/processing-pipelines#custom-components-attributes) on the
|
||
entity `Span` – for example `._.orgs` or `._.prev_orgs` and `._.current_orgs`.
|
||
|
||
> #### Merging entities
|
||
>
|
||
> Under the hood, entities are merged using the
|
||
> [`Doc.retokenize`](/api/doc#retokenize) context manager:
|
||
>
|
||
> ```python
|
||
> with doc.retokenize() as retokenize:
|
||
> for ent in doc.ents:
|
||
> retokenizer.merge(ent)
|
||
> ```
|
||
|
||
```python
|
||
### {executable="true"}
|
||
import spacy
|
||
from spacy.pipeline import merge_entities
|
||
from spacy import displacy
|
||
|
||
nlp = spacy.load("en_core_web_sm")
|
||
|
||
def extract_person_orgs(doc):
|
||
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
|
||
for ent in person_entities:
|
||
head = ent.root.head
|
||
if head.lemma_ == "work":
|
||
preps = [token for token in head.children if token.dep_ == "prep"]
|
||
for prep in preps:
|
||
orgs = [token for token in prep.children if token.ent_type_ == "ORG"]
|
||
print({'person': ent, 'orgs': orgs, 'past': head.tag_ == "VBD"})
|
||
return doc
|
||
|
||
# To make the entities easier to work with, we'll merge them into single tokens
|
||
nlp.add_pipe(merge_entities)
|
||
nlp.add_pipe(extract_person_orgs)
|
||
|
||
doc = nlp("Alex Smith worked at Acme Corp Inc.")
|
||
# If you're not in a Jupyter / IPython environment, use displacy.serve
|
||
displacy.render(doc, options={'fine_grained': True})
|
||
```
|
||
|
||
If you change the sentence structure above, for example to "was working", you'll
|
||
notice that our current logic fails and doesn't correctly detect the company as
|
||
a past organization. That's because the root is a participle and the tense
|
||
information is in the attached auxiliary "was":
|
||
|
||
|
||
|
||
To solve this, we can adjust the rules to also check for the above construction:
|
||
|
||
```python
|
||
### {highlight="9-11"}
|
||
def extract_person_orgs(doc):
|
||
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
|
||
for ent in person_entities:
|
||
head = ent.root.head
|
||
if head.lemma_ == "work":
|
||
preps = [token for token in head.children if token.dep_ == "prep"]
|
||
for prep in preps:
|
||
orgs = [t for t in prep.children if t.ent_type_ == "ORG"]
|
||
aux = [token for token in head.children if token.dep_ == "aux"]
|
||
past_aux = any(t.tag_ == "VBD" for t in aux)
|
||
past = head.tag_ == "VBD" or head.tag_ == "VBG" and past_aux
|
||
print({'person': ent, 'orgs': orgs, 'past': past})
|
||
return doc
|
||
```
|
||
|
||
In your final rule-based system, you may end up with **several different code
|
||
paths** to cover the types of constructions that occur in your data.
|