mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-29 11:26:28 +03:00
166 lines
11 KiB
Markdown
166 lines
11 KiB
Markdown
---
|
||
title: Lexeme
|
||
teaser: An entry in the vocabulary
|
||
tag: class
|
||
source: spacy/lexeme.pyx
|
||
---
|
||
|
||
A `Lexeme` has no string context – it's a word type, as opposed to a word token.
|
||
It therefore has no part-of-speech tag, dependency parse, or lemma (if
|
||
lemmatization depends on the part-of-speech tag).
|
||
|
||
## Lexeme.\_\_init\_\_ {#init tag="method"}
|
||
|
||
Create a `Lexeme` object.
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | -------- | ----------------------------- |
|
||
| `vocab` | `Vocab` | The parent vocabulary. |
|
||
| `orth` | int | The orth id of the lexeme. |
|
||
| **RETURNS** | `Lexeme` | The newly constructed object. |
|
||
|
||
## Lexeme.set_flag {#set_flag tag="method"}
|
||
|
||
Change the value of a boolean flag.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
|
||
> nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| --------- | ---- | ------------------------------------ |
|
||
| `flag_id` | int | The attribute ID of the flag to set. |
|
||
| `value` | bool | The new value of the flag. |
|
||
|
||
## Lexeme.check_flag {#check_flag tag="method"}
|
||
|
||
Check the value of a boolean flag.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> is_my_library = lambda text: text in [u"spaCy", u"Thinc"]
|
||
> MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
|
||
> assert nlp.vocab[u"spaCy"].check_flag(MY_LIBRARY) == True
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | ---- | -------------------------------------- |
|
||
| `flag_id` | int | The attribute ID of the flag to query. |
|
||
| **RETURNS** | bool | The value of the flag. |
|
||
|
||
## Lexeme.similarity {#similarity tag="method" model="vectors"}
|
||
|
||
Compute a semantic similarity estimate. Defaults to cosine over vectors.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> apple = nlp.vocab[u"apple"]
|
||
> orange = nlp.vocab[u"orange"]
|
||
> apple_orange = apple.similarity(orange)
|
||
> orange_apple = orange.similarity(apple)
|
||
> assert apple_orange == orange_apple
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
|
||
| other | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
|
||
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
|
||
|
||
## Lexeme.has_vector {#has_vector tag="property" model="vectors"}
|
||
|
||
A boolean value indicating whether a word vector is associated with the lexeme.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> apple = nlp.vocab[u"apple"]
|
||
> assert apple.has_vector
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | ---- | ---------------------------------------------- |
|
||
| **RETURNS** | bool | Whether the lexeme has a vector data attached. |
|
||
|
||
## Lexeme.vector {#vector tag="property" model="vectors"}
|
||
|
||
A real-valued meaning representation.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> apple = nlp.vocab[u"apple"]
|
||
> assert apple.vector.dtype == "float32"
|
||
> assert apple.vector.shape == (300,)
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | ---------------------------------------- | ----------------------------------------------------- |
|
||
| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the lexeme's semantics. |
|
||
|
||
## Lexeme.vector_norm {#vector_norm tag="property" model="vectors"}
|
||
|
||
The L2 norm of the lexeme's vector representation.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> apple = nlp.vocab[u"apple"]
|
||
> pasta = nlp.vocab[u"pasta"]
|
||
> apple.vector_norm # 7.1346845626831055
|
||
> pasta.vector_norm # 7.759851932525635
|
||
> assert apple.vector_norm != pasta.vector_norm
|
||
> ```
|
||
|
||
| Name | Type | Description |
|
||
| ----------- | ----- | ----------------------------------------- |
|
||
| **RETURNS** | float | The L2 norm of the vector representation. |
|
||
|
||
## Attributes {#attributes}
|
||
|
||
| Name | Type | Description |
|
||
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------ |
|
||
| `vocab` | `Vocab` | The lexeme's vocabulary. |
|
||
| `text` | unicode | Verbatim text content. |
|
||
| `orth` | int | ID of the verbatim text content. |
|
||
| `orth_` | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
|
||
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
|
||
| `flags` | int | Container of the lexeme's binary flags. |
|
||
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
|
||
| `norm_` | unicode | The lexemes's norm, i.e. a normalized form of the lexeme text. |
|
||
| `lower` | int | Lowercase form of the word. |
|
||
| `lower_` | unicode | Lowercase form of the word. |
|
||
| `shape` | int | Transform of the word's string, to show orthographic features. |
|
||
| `shape_` | unicode | Transform of the word's string, to show orthographic features. |
|
||
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
|
||
| `prefix_` | unicode | Length-N substring from the start of the word. Defaults to `N=1`. |
|
||
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
|
||
| `suffix_` | unicode | Length-N substring from the start of the word. Defaults to `N=3`. |
|
||
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
|
||
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
|
||
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
|
||
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
|
||
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
|
||
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
|
||
| `is_punct` | bool | Is the lexeme punctuation? |
|
||
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
|
||
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
|
||
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
|
||
| `is_bracket` | bool | Is the lexeme a bracket? |
|
||
| `is_quote` | bool | Is the lexeme a quotation mark? |
|
||
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
|
||
| `like_url` | bool | Does the lexeme resemble a URL? |
|
||
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
|
||
| `like_email` | bool | Does the lexeme resemble an email address? |
|
||
| `is_oov` | bool | Is the lexeme out-of-vocabulary? |
|
||
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
|
||
| `lang` | int | Language of the parent vocabulary. |
|
||
| `lang_` | unicode | Language of the parent vocabulary. |
|
||
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
|
||
| `cluster` | int | Brown cluster ID. |
|
||
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
|