mirror of https://github.com/explosion/spaCy.git
196 lines
8.3 KiB
Markdown
196 lines
8.3 KiB
Markdown
---
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title: PhraseMatcher
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teaser: Match sequences of tokens, based on documents
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tag: class
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source: spacy/matcher/phrasematcher.pyx
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new: 2
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---
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The `PhraseMatcher` lets you efficiently match large terminology lists. While
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the [`Matcher`](/api/matcher) lets you match sequences based on lists of token
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descriptions, the `PhraseMatcher` accepts match patterns in the form of `Doc`
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objects.
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## PhraseMatcher.\_\_init\_\_ {#init tag="method"}
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Create the rule-based `PhraseMatcher`. Setting a different `attr` to match on
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will change the token attributes that will be compared to determine a match. By
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default, the incoming `Doc` is checked for sequences of tokens with the same
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`ORTH` value, i.e. the verbatim token text. Matching on the attribute `LOWER`
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will result in case-insensitive matching, since only the lowercase token texts
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are compared. In theory, it's also possible to match on sequences of the same
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part-of-speech tags or dependency labels.
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If `validate=True` is set, additional validation is performed when pattern are
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added. At the moment, it will check whether a `Doc` has attributes assigned that
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aren't necessary to produce the matches (for example, part-of-speech tags if the
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`PhraseMatcher` matches on the token text). Since this can often lead to
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significantly worse performance when creating the pattern, a `UserWarning` will
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be shown.
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> #### Example
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>
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> ```python
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> from spacy.matcher import PhraseMatcher
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> matcher = PhraseMatcher(nlp.vocab)
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> ```
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| Name | Type | Description |
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| --------------------------------------- | --------------- | ------------------------------------------------------------------------------------------- |
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| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
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| `attr` <Tag variant="new">2.1</Tag> | int / str | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. |
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| `validate` <Tag variant="new">2.1</Tag> | bool | Validate patterns added to the matcher. |
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| **RETURNS** | `PhraseMatcher` | The newly constructed object. |
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## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
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Find all token sequences matching the supplied patterns on the `Doc`.
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> #### Example
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>
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> ```python
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> from spacy.matcher import PhraseMatcher
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>
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", None, nlp("Barack Obama"))
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> doc = nlp("Barack Obama lifts America one last time in emotional farewell")
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> matches = matcher(doc)
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `doc` | `Doc` | The document to match over. |
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| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. |
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<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
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Because spaCy stores all strings as integers, the `match_id` you get back will
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be an integer, too – but you can always get the string representation by looking
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it up in the vocabulary's `StringStore`, i.e. `nlp.vocab.strings`:
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```python
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match_id_string = nlp.vocab.strings[match_id]
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```
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</Infobox>
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## PhraseMatcher.pipe {#pipe tag="method"}
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Match a stream of documents, yielding them in turn.
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> #### Example
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>
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> ```python
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> from spacy.matcher import PhraseMatcher
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> matcher = PhraseMatcher(nlp.vocab)
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> for doc in matcher.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Type | Description |
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| ------------ | -------- | --------------------------------------------------------- |
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| `docs` | iterable | A stream of documents. |
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| `batch_size` | int | The number of documents to accumulate into a working set. |
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| **YIELDS** | `Doc` | Documents, in order. |
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## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
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Get the number of rules added to the matcher. Note that this only returns the
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number of rules (identical with the number of IDs), not the number of individual
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patterns.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> assert len(matcher) == 0
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> matcher.add("OBAMA", None, nlp("Barack Obama"))
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> assert len(matcher) == 1
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> ```
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| Name | Type | Description |
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| ----------- | ---- | -------------------- |
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| **RETURNS** | int | The number of rules. |
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## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
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Check whether the matcher contains rules for a match ID.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> assert "OBAMA" not in matcher
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> matcher.add("OBAMA", None, nlp("Barack Obama"))
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> assert "OBAMA" in matcher
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ----------------------------------------------------- |
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| `key` | str | The match ID. |
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| **RETURNS** | bool | Whether the matcher contains rules for this match ID. |
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## PhraseMatcher.add {#add tag="method"}
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Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
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callback function to act on the matches. The callback function will receive the
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arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
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the given ID, the patterns will be extended. An `on_match` callback will be
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overwritten.
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> #### Example
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>
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> ```python
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> def on_match(matcher, doc, id, matches):
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> print('Matched!', matches)
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>
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", on_match, nlp("Barack Obama"))
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> matcher.add("HEALTH", on_match, nlp("health care reform"),
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> nlp("healthcare reform"))
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> doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
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> matches = matcher(doc)
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> ```
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| Name | Type | Description |
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| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
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| `match_id` | str | An ID for the thing you're matching. |
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| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
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| `*docs` | `Doc` | `Doc` objects of the phrases to match. |
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<Infobox title="Changed in v2.2.2" variant="warning">
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As of spaCy 2.2.2, `PhraseMatcher.add` also supports the new API, which will
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become the default in the future. The `Doc` patterns are now the second argument
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and a list (instead of a variable number of arguments). The `on_match` callback
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becomes an optional keyword argument.
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```diff
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patterns = [nlp("health care reform"), nlp("healthcare reform")]
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- matcher.add("HEALTH", None, *patterns)
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+ matcher.add("HEALTH", patterns)
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- matcher.add("HEALTH", on_match, *patterns)
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+ matcher.add("HEALTH", patterns, on_match=on_match)
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```
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</Infobox>
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## PhraseMatcher.remove {#remove tag="method" new="2.2"}
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Remove a rule from the matcher by match ID. A `KeyError` is raised if the key
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does not exist.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", None, nlp("Barack Obama"))
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> assert "OBAMA" in matcher
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> matcher.remove("OBAMA")
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> assert "OBAMA" not in matcher
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> ```
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| Name | Type | Description |
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| ----- | ---- | ------------------------- |
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| `key` | str | The ID of the match rule. |
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