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176 lines
8.9 KiB
Markdown
176 lines
8.9 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. See the [usage guide](/usage/rule-based-matching#phrasematcher) for
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examples.
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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 | Description |
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| --------------------------------------- | ------------------------------------------------------------------------------------------------------ |
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| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
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| `attr` <Tag variant="new">2.1</Tag> | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
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| `validate` <Tag variant="new">2.1</Tag> | Validate patterns added to the matcher. ~~bool~~ |
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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", [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 | Description |
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| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doc` | The document to match over. ~~Doc~~ |
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| _keyword-only_ | |
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| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~ |
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| **RETURNS** | 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. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
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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.\_\_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", [nlp("Barack Obama")])
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> assert len(matcher) == 1
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The number of rules. ~~int~~ |
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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", [nlp("Barack Obama")])
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> assert "OBAMA" in matcher
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> ```
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| Name | Description |
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| ----------- | -------------------------------------------------------------- |
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| `key` | The match ID. ~~str~~ |
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| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
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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", [nlp("Barack Obama")], on_match=on_match)
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> matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
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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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<Infobox title="Changed in v3.0" variant="warning">
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As of spaCy v3.0, `PhraseMatcher.add` takes a list of patterns as the second
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argument (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", 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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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `match_id` | An ID for the thing you're matching. ~~str~~ | |
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| `docs` | `Doc` objects of the phrases to match. ~~List[Doc]~~ |
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| _keyword-only_ | |
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| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
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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", [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 | Description |
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| ----- | --------------------------------- |
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| `key` | The ID of the match rule. ~~str~~ |
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