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165 lines
16 KiB
Markdown
165 lines
16 KiB
Markdown
---
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title: Lexeme
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teaser: An entry in the vocabulary
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tag: class
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source: spacy/lexeme.pyx
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---
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A `Lexeme` has no string context – it's a word type, as opposed to a word token.
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It therefore has no part-of-speech tag, dependency parse, or lemma (if
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lemmatization depends on the part-of-speech tag).
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## Lexeme.\_\_init\_\_ {#init tag="method"}
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Create a `Lexeme` object.
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| Name | Type | Description |
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| ------- | ------- | -------------------------- |
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| `vocab` | `Vocab` | The parent vocabulary. |
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| `orth` | int | The orth id of the lexeme. |
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## Lexeme.set_flag {#set_flag tag="method"}
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Change the value of a boolean flag.
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> #### Example
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>
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> ```python
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> COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
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> nlp.vocab["spaCy"].set_flag(COOL_FLAG, True)
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> ```
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| Name | Type | Description |
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| --------- | ---- | ------------------------------------ |
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| `flag_id` | int | The attribute ID of the flag to set. |
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| `value` | bool | The new value of the flag. |
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## Lexeme.check_flag {#check_flag tag="method"}
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Check the value of a boolean flag.
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> #### Example
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>
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> ```python
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> is_my_library = lambda text: text in ["spaCy", "Thinc"]
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> MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
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> assert nlp.vocab["spaCy"].check_flag(MY_LIBRARY) == True
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> ```
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| Name | Type | Description |
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| ----------- | ---- | -------------------------------------- |
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| `flag_id` | int | The attribute ID of the flag to query. |
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| **RETURNS** | bool | The value of the flag. |
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## Lexeme.similarity {#similarity tag="method" model="vectors"}
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Compute a semantic similarity estimate. Defaults to cosine over vectors.
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> #### Example
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>
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> ```python
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> apple = nlp.vocab["apple"]
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> orange = nlp.vocab["orange"]
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> apple_orange = apple.similarity(orange)
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> orange_apple = orange.similarity(apple)
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> assert apple_orange == orange_apple
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> ```
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| Name | Type | Description |
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| ----------- | ----- | -------------------------------------------------------------------------------------------- |
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| other | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
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| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
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## Lexeme.has_vector {#has_vector tag="property" model="vectors"}
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A boolean value indicating whether a word vector is associated with the lexeme.
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> #### Example
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>
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> ```python
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> apple = nlp.vocab["apple"]
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> assert apple.has_vector
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ---------------------------------------------- |
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| **RETURNS** | bool | Whether the lexeme has a vector data attached. |
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## Lexeme.vector {#vector tag="property" model="vectors"}
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A real-valued meaning representation.
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> #### Example
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>
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> ```python
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> apple = nlp.vocab["apple"]
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> assert apple.vector.dtype == "float32"
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> assert apple.vector.shape == (300,)
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> ```
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| Name | Type | Description |
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| ----------- | ---------------------------------------- | ----------------------------------------------------- |
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| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the lexeme's semantics. |
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## Lexeme.vector_norm {#vector_norm tag="property" model="vectors"}
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The L2 norm of the lexeme's vector representation.
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> #### Example
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>
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> ```python
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> apple = nlp.vocab["apple"]
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> pasta = nlp.vocab["pasta"]
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> apple.vector_norm # 7.1346845626831055
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> pasta.vector_norm # 7.759851932525635
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> assert apple.vector_norm != pasta.vector_norm
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ----------------------------------------- |
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| **RETURNS** | float | The L2 norm of the vector representation. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `vocab` | `Vocab` | The lexeme's vocabulary. |
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| `text` | str | Verbatim text content. |
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| `orth` | int | ID of the verbatim text content. |
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| `orth_` | str | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
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| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
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| `flags` | int | Container of the lexeme's binary flags. |
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| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
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| `norm_` | str | The lexemes's norm, i.e. a normalized form of the lexeme text. |
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| `lower` | int | Lowercase form of the word. |
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| `lower_` | str | Lowercase form of the word. |
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| `shape` | int | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
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| `shape_` | str | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
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| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
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| `prefix_` | str | Length-N substring from the start of the word. Defaults to `N=1`. |
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| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
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| `suffix_` | str | Length-N substring from the start of the word. Defaults to `N=3`. |
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| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
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| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
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| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
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| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
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| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
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| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
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| `is_punct` | bool | Is the lexeme punctuation? |
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| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
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| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
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| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
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| `is_bracket` | bool | Is the lexeme a bracket? |
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| `is_quote` | bool | Is the lexeme a quotation mark? |
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| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
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| `like_url` | bool | Does the lexeme resemble a URL? |
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| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
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| `like_email` | bool | Does the lexeme resemble an email address? |
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| `is_oov` | bool | Does the lexeme have a word vector? |
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| `is_stop` | bool | Is the lexeme part of a "stop list"? |
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| `lang` | int | Language of the parent vocabulary. |
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| `lang_` | str | Language of the parent vocabulary. |
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| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
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| `cluster` | int | Brown cluster ID. |
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| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
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