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165 lines
17 KiB
Markdown
165 lines
17 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 | Description |
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| ------- | ---------------------------------- |
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| `vocab` | The parent vocabulary. ~~Vocab~~ |
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| `orth` | The orth id of the lexeme. ~~int~~ |
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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 | Description |
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| --------- | -------------------------------------------- |
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| `flag_id` | The attribute ID of the flag to set. ~~int~~ |
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| `value` | The new value of the flag. ~~bool~~ |
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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 | Description |
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| ----------- | ---------------------------------------------- |
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| `flag_id` | The attribute ID of the flag to query. ~~int~~ |
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| **RETURNS** | The value of the flag. ~~bool~~ |
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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 | Description |
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| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| other | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
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| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
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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 | Description |
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| ----------- | ------------------------------------------------------- |
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| **RETURNS** | Whether the lexeme has a vector data attached. ~~bool~~ |
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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 | Description |
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| ----------- | ------------------------------------------------------------------------------------------------ |
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| **RETURNS** | A 1-dimensional array representing the lexeme's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
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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 | Description |
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| ----------- | --------------------------------------------------- |
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| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
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## Attributes {#attributes}
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| Name | Description |
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| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The lexeme's vocabulary. ~~Vocab~~ |
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| `text` | Verbatim text content. ~~str~~ |
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| `orth` | ID of the verbatim text content. ~~int~~ |
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| `orth_` | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. ~~str~~ |
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| `rank` | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ |
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| `flags` | Container of the lexeme's binary flags. ~~int~~ |
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| `norm` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~int~~ |
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| `norm_` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~str~~ |
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| `lower` | Lowercase form of the word. ~~int~~ |
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| `lower_` | Lowercase form of the word. ~~str~~ |
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| `shape` | 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"`. ~~int~~ |
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| `shape_` | 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"`. ~~str~~ |
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| `prefix` | Length-N substring from the start of the word. Defaults to `N=1`. ~~int~~ |
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| `prefix_` | Length-N substring from the start of the word. Defaults to `N=1`. ~~str~~ |
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| `suffix` | Length-N substring from the end of the word. Defaults to `N=3`. ~~int~~ |
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| `suffix_` | Length-N substring from the start of the word. Defaults to `N=3`. ~~str~~ |
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| `is_alpha` | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. ~~bool~~ |
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| `is_ascii` | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. ~~bool~~ |
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| `is_digit` | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. ~~bool~~ |
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| `is_lower` | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. ~~bool~~ |
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| `is_upper` | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. ~~bool~~ |
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| `is_title` | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. ~~bool~~ |
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| `is_punct` | Is the lexeme punctuation? ~~bool~~ |
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| `is_left_punct` | Is the lexeme a left punctuation mark, e.g. `(`? ~~bool~~ |
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| `is_right_punct` | Is the lexeme a right punctuation mark, e.g. `)`? ~~bool~~ |
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| `is_space` | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~ |
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| `is_bracket` | Is the lexeme a bracket? ~~bool~~ |
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| `is_quote` | Is the lexeme a quotation mark? ~~bool~~ |
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| `is_currency` <Tag variant="new">2.0.8</Tag> | Is the lexeme a currency symbol? ~~bool~~ |
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| `like_url` | Does the lexeme resemble a URL? ~~bool~~ |
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| `like_num` | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ |
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| `like_email` | Does the lexeme resemble an email address? ~~bool~~ |
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| `is_oov` | Does the lexeme have a word vector? ~~bool~~ |
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| `is_stop` | Is the lexeme part of a "stop list"? ~~bool~~ |
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| `lang` | Language of the parent vocabulary. ~~int~~ |
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| `lang_` | Language of the parent vocabulary. ~~str~~ |
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| `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ |
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| `cluster` | Brown cluster ID. ~~int~~ |
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| `sentiment` | A scalar value indicating the positivity or negativity of the lexeme. ~~float~~ |
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