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spaCy/website/docs/api/span.md
Sofie Van Landeghem fed8f48965
raise NotImplementedError when noun_chunks iterator is not implemented (#6711) 
* raise NotImplementedError when noun_chunks iterator is not implemented

* bring back, fix and document span.noun_chunks

* formatting

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2021-01-17 19:56:05 +08:00

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---
title: Span
tag: class
source: spacy/tokens/span.pyx
---
A slice from a [`Doc`](/api/doc) object.
## Span.\_\_init\_\_ {#init tag="method"}
Create a `Span` object from the slice `doc[start : end]`.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> span = doc[1:4]
> assert [t.text for t in span] == ["it", "back", "!"]
> ```
| Name | Description |
| -------- | --------------------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `start` | The index of the first token of the span. ~~int~~ |
| `end` | The index of the first token after the span. ~~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[str, int]~~ |
| `kb_id` | A knowledge base ID to attach to the span, e.g. for named entities. ~~Union[str, int]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Span.\_\_getitem\_\_ {#getitem tag="method"}
Get a `Token` object.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> span = doc[1:4]
> assert span[1].text == "back"
> ```
| Name | Description |
| ----------- | ----------------------------------------------- |
| `i` | The index of the token within the span. ~~int~~ |
| **RETURNS** | The token at `span[i]`. ~~Token~~ |
Get a `Span` object.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> span = doc[1:4]
> assert span[1:3].text == "back!"
> ```
| Name | Description |
| ----------- | ------------------------------------------------- |
| `start_end` | The slice of the span to get. ~~Tuple[int, int]~~ |
| **RETURNS** | The span at `span[start : end]`. ~~Span~~ |
## Span.\_\_iter\_\_ {#iter tag="method"}
Iterate over `Token` objects.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> span = doc[1:4]
> assert [t.text for t in span] == ["it", "back", "!"]
> ```
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A `Token` object. ~~Token~~ |
## Span.\_\_len\_\_ {#len tag="method"}
Get the number of tokens in the span.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> span = doc[1:4]
> assert len(span) == 3
> ```
| Name | Description |
| ----------- | ----------------------------------------- |
| **RETURNS** | The number of tokens in the span. ~~int~~ |
## Span.set_extension {#set_extension tag="classmethod" new="2"}
Define a custom attribute on the `Span` which becomes available via `Span._`.
For details, see the documentation on
[custom attributes](/usage/processing-pipelines#custom-components-attributes).
> #### Example
>
> ```python
> from spacy.tokens import Span
> city_getter = lambda span: any(city in span.text for city in ("New York", "Paris", "Berlin"))
> Span.set_extension("has_city", getter=city_getter)
> doc = nlp("I like New York in Autumn")
> assert doc[1:4]._.has_city
> ```
| Name | Description |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `span._.my_attr`. ~~str~~ |
| `default` | Optional default value of the attribute if no getter or method is defined. ~~Optional[Any]~~ |
| `method` | Set a custom method on the object, for example `span._.compare(other_span)`. ~~Optional[Callable[[Span, ...], Any]]~~ |
| `getter` | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. ~~Optional[Callable[[Span], Any]]~~ |
| `setter` | Setter function that takes the `Span` and a value, and modifies the object. Is called when the user writes to the `Span._` attribute. ~~Optional[Callable[[Span, Any], None]]~~ |
| `force` | Force overwriting existing attribute. ~~bool~~ |
## Span.get_extension {#get_extension tag="classmethod" new="2"}
Look up a previously registered extension by name. Returns a 4-tuple
`(default, method, getter, setter)` if the extension is registered. Raises a
`KeyError` otherwise.
> #### Example
>
> ```python
> from spacy.tokens import Span
> Span.set_extension("is_city", default=False)
> extension = Span.get_extension("is_city")
> assert extension == (False, None, None, None)
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Span.has_extension {#has_extension tag="classmethod" new="2"}
Check whether an extension has been registered on the `Span` class.
> #### Example
>
> ```python
> from spacy.tokens import Span
> Span.set_extension("is_city", default=False)
> assert Span.has_extension("is_city")
> ```
| Name | Description |
| ----------- | --------------------------------------------------- |
| `name` | Name of the extension to check. ~~str~~ |
| **RETURNS** | Whether the extension has been registered. ~~bool~~ |
## Span.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
Remove a previously registered extension.
> #### Example
>
> ```python
> from spacy.tokens import Span
> Span.set_extension("is_city", default=False)
> removed = Span.remove_extension("is_city")
> assert not Span.has_extension("is_city")
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the removed extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Span.char_span {#char_span tag="method" new="2.2.4"}
Create a `Span` object from the slice `span.text[start:end]`. Returns `None` if
the character indices don't map to a valid span.
> #### Example
>
> ```python
> doc = nlp("I like New York")
> span = doc[1:4].char_span(5, 13, label="GPE")
> assert span.text == "New York"
> ```
| Name | Description |
| ------------------------------------ | ----------------------------------------------------------------------------------------- |
| `start` | The index of the first character of the span. ~~int~~ |
| `end` | The index of the last character after the span. ~~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Span.similarity {#similarity tag="method" model="vectors"}
Make a semantic similarity estimate. The default estimate is cosine similarity
using an average of word vectors.
> #### Example
>
> ```python
> doc = nlp("green apples and red oranges")
> green_apples = doc[:2]
> red_oranges = doc[3:]
> apples_oranges = green_apples.similarity(red_oranges)
> oranges_apples = red_oranges.similarity(green_apples)
> assert apples_oranges == oranges_apples
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `other` | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Span.get_lca_matrix {#get_lca_matrix tag="method"}
Calculates the lowest common ancestor matrix for a given `Span`. Returns LCA
matrix containing the integer index of the ancestor, or `-1` if no common
ancestor is found, e.g. if span excludes a necessary ancestor.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn")
> span = doc[1:4]
> matrix = span.get_lca_matrix()
> # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
> ```
| Name | Description |
| ----------- | --------------------------------------------------------------------------------------- |
| **RETURNS** | The lowest common ancestor matrix of the `Span`. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
## Span.to_array {#to_array tag="method" new="2"}
Given a list of `M` attribute IDs, export the tokens to a numpy `ndarray` of
shape `(N, M)`, where `N` is the length of the document. The values will be
32-bit integers.
> #### Example
>
> ```python
> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
> doc = nlp("I like New York in Autumn.")
> span = doc[2:3]
> # All strings mapped to integers, for easy export to numpy
> np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `attr_ids` | A list of attributes (int IDs or string names) or a single attribute (int ID or string name). ~~Union[int, str, List[Union[int, str]]]~~ |
| **RETURNS** | The exported attributes as a numpy array. ~~Union[numpy.ndarray[ndim=2, dtype=uint64], numpy.ndarray[ndim=1, dtype=uint64]]~~ |
## Span.ents {#ents tag="property" new="2.0.13" model="ner"}
The named entities in the span. Returns a tuple of named entity `Span` objects,
if the entity recognizer has been applied.
> #### Example
>
> ```python
> doc = nlp("Mr. Best flew to New York on Saturday morning.")
> span = doc[0:6]
> ents = list(span.ents)
> assert ents[0].label == 346
> assert ents[0].label_ == "PERSON"
> assert ents[0].text == "Mr. Best"
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------------- |
| **RETURNS** | Entities in the span, one `Span` per entity. ~~Tuple[Span, ...]~~ |
## Span.noun_chunks {#noun_chunks tag="property" model="parser"}
Iterate over the base noun phrases in the span. Yields base noun-phrase `Span`
objects, if the document has been syntactically parsed. A base noun phrase, or
"NP chunk", is a noun phrase that does not permit other NPs to be nested within
it – so no NP-level coordination, no prepositional phrases, and no relative
clauses.
If the `noun_chunk` [syntax iterator](/usage/adding-languages#language-data) has
not been implemeted for the given language, a `NotImplementedError` is raised.
> #### Example
>
> ```python
> doc = nlp("A phrase with another phrase occurs.")
> span = doc[3:5]
> chunks = list(span.noun_chunks)
> assert len(chunks) == 1
> assert chunks[0].text == "another phrase"
> ```
| Name | Description |
| ---------- | --------------------------------- |
| **YIELDS** | Noun chunks in the span. ~~Span~~ |
## Span.as_doc {#as_doc tag="method"}
Create a new `Doc` object corresponding to the `Span`, with a copy of the data.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> span = doc[2:4]
> doc2 = span.as_doc()
> assert doc2.text == "New York"
> ```
| Name | Description |
| ---------------- | ------------------------------------------------------------- |
| `copy_user_data` | Whether or not to copy the original doc's user data. ~~bool~~ |
| **RETURNS** | A `Doc` object of the `Span`'s content. ~~Doc~~ |
## Span.root {#root tag="property" model="parser"}
The token with the shortest path to the root of the sentence (or the root
itself). If multiple tokens are equally high in the tree, the first token is
taken.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> i, like, new, york, in_, autumn, dot = range(len(doc))
> assert doc[new].head.text == "York"
> assert doc[york].head.text == "like"
> new_york = doc[new:york+1]
> assert new_york.root.text == "York"
> ```
| Name | Description |
| ----------- | ------------------------- |
| **RETURNS** | The root token. ~~Token~~ |
## Span.conjuncts {#conjuncts tag="property" model="parser"}
A tuple of tokens coordinated to `span.root`.
> #### Example
>
> ```python
> doc = nlp("I like apples and oranges")
> apples_conjuncts = doc[2:3].conjuncts
> assert [t.text for t in apples_conjuncts] == ["oranges"]
> ```
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The coordinated tokens. ~~Tuple[Token, ...]~~ |
## Span.lefts {#lefts tag="property" model="parser"}
Tokens that are to the left of the span, whose heads are within the span.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> lefts = [t.text for t in doc[3:7].lefts]
> assert lefts == ["New"]
> ```
| Name | Description |
| ---------- | ---------------------------------------------- |
| **YIELDS** | A left-child of a token of the span. ~~Token~~ |
## Span.rights {#rights tag="property" model="parser"}
Tokens that are to the right of the span, whose heads are within the span.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> rights = [t.text for t in doc[2:4].rights]
> assert rights == ["in"]
> ```
| Name | Description |
| ---------- | ----------------------------------------------- |
| **YIELDS** | A right-child of a token of the span. ~~Token~~ |
## Span.n_lefts {#n_lefts tag="property" model="parser"}
The number of tokens that are to the left of the span, whose heads are within
the span.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> assert doc[3:7].n_lefts == 1
> ```
| Name | Description |
| ----------- | ---------------------------------------- |
| **RETURNS** | The number of left-child tokens. ~~int~~ |
## Span.n_rights {#n_rights tag="property" model="parser"}
The number of tokens that are to the right of the span, whose heads are within
the span.
> #### Example
>
> ```python
> doc = nlp("I like New York in Autumn.")
> assert doc[2:4].n_rights == 1
> ```
| Name | Description |
| ----------- | ----------------------------------------- |
| **RETURNS** | The number of right-child tokens. ~~int~~ |
## Span.subtree {#subtree tag="property" model="parser"}
Tokens within the span and tokens which descend from them.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> subtree = [t.text for t in doc[:3].subtree]
> assert subtree == ["Give", "it", "back", "!"]
> ```
| Name | Description |
| ---------- | ----------------------------------------------------------- |
| **YIELDS** | A token within the span, or a descendant from it. ~~Token~~ |
## Span.has_vector {#has_vector tag="property" model="vectors"}
A boolean value indicating whether a word vector is associated with the object.
> #### Example
>
> ```python
> doc = nlp("I like apples")
> assert doc[1:].has_vector
> ```
| Name | Description |
| ----------- | ----------------------------------------------------- |
| **RETURNS** | Whether the span has a vector data attached. ~~bool~~ |
## Span.vector {#vector tag="property" model="vectors"}
A real-valued meaning representation. Defaults to an average of the token
vectors.
> #### Example
>
> ```python
> doc = nlp("I like apples")
> assert doc[1:].vector.dtype == "float32"
> assert doc[1:].vector.shape == (300,)
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| **RETURNS** | A 1-dimensional array representing the span's vector. ~~`numpy.ndarray[ndim=1, dtype=float32]~~ |
## Span.vector_norm {#vector_norm tag="property" model="vectors"}
The L2 norm of the span's vector representation.
> #### Example
>
> ```python
> doc = nlp("I like apples")
> doc[1:].vector_norm # 4.800883928527915
> doc[2:].vector_norm # 6.895897646384268
> assert doc[1:].vector_norm != doc[2:].vector_norm
> ```
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Description |
| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `doc` | The parent document. ~~Doc~~ |
| `tensor` <Tag variant="new">2.1.7</Tag> | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~ |
| `sent` | The sentence span that this span is a part of. ~~Span~~ |
| `start` | The token offset for the start of the span. ~~int~~ |
| `end` | The token offset for the end of the span. ~~int~~ |
| `start_char` | The character offset for the start of the span. ~~int~~ |
| `end_char` | The character offset for the end of the span. ~~int~~ |
| `text` | A string representation of the span text. ~~str~~ |
| `text_with_ws` | The text content of the span with a trailing whitespace character if the last token has one. ~~str~~ |
| `orth` | ID of the verbatim text content. ~~int~~ |
| `orth_` | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. ~~str~~ |
| `label` | The hash value of the span's label. ~~int~~ |
| `label_` | The span's label. ~~str~~ |
| `lemma_` | The span's lemma. Equivalent to `"".join(token.text_with_ws for token in span)`. ~~str~~ |
| `kb_id` | The hash value of the knowledge base ID referred to by the span. ~~int~~ |
| `kb_id_` | The knowledge base ID referred to by the span. ~~str~~ |
| `ent_id` | The hash value of the named entity the token is an instance of. ~~int~~ |
| `ent_id_` | The string ID of the named entity the token is an instance of. ~~str~~ |
| `sentiment` | A scalar value indicating the positivity or negativity of the span. ~~float~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |
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