spaCy - Container Lexeme Class

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In this chapter, Lexeme Class in spaCy is explained in detail.

Lexeme Class

Lexeme class is an entry in the vocabulary. It has no string context. As opposed to a word token, it is a word type. That’s the reason it has no POS(part-of-speech) tag, dependency parse or lemma.

Attributes

The table below explains its arguments −

Methods

Following are the methods used in Lexeme class −

Lexeme._ _init_ _

This is one of the most useful methods of Lexeme class. As name implies, it is used to construct a Lexeme object.

Arguments

The table below explains its arguments −

Example

An example of Lexeme._ _init_ _ method is given below −

import spacy
nlp_model = spacy.load("en_core_web_sm")
doc = nlp_model("The website is Tutorialspoint.com.")
lexeme = doc[3]
lexeme.text

Output

When you run the code, you will see the following output −

'Tutorialspoint.com'

Lexeme.set_flag

This method is used to change the value of a Boolean flag.

Arguments

The table below explains its arguments −

Example

An example of Lexeme.set_flag method is given below −

import spacy
nlp_model = spacy.load("en_core_web_sm")
New_FLAG = nlp_model.vocab.add_flag(lambda text: False)
nlp_model.vocab["Tutorialspoint.com"].set_flag(New_FLAG, True)
New_FLAG

Output

When you run the code, you will see the following output −

25

Lexeme.check_flag

This method is used to check the value of a Boolean flag.

Argument

The table below explains its argument −

Example 1

An example of Lexeme.check_flag method is given below −

import spacy
nlp_model = spacy.load("en_core_web_sm")
library = lambda text: text in ["Website", "Tutorialspoint.com"]
my_library = nlp_model.vocab.add_flag(library)
nlp_model.vocab["Tutorialspoint.com"].check_flag(my_library)

Output

When you run the code, you will see the following output −

True

Example 2

Given below is another example of Lexeme.check_flag method −

nlp_model.vocab["Hello"].check_flag(my_library)

Output

When you run the code, you will see the following output −

False

Lexeme.similarity

This method is used to compute a semantic similarity estimate. The default is cosine over vectors.

Argument

The table below explains its argument −

Example

An example of Lexeme.similarity method is as follows −

import spacy
nlp_model = spacy.load("en_core_web_sm")
apple = nlp.vocab["apple"]
orange = nlp.vocab["orange"]
apple_orange = apple.similarity(orange)
orange_apple = orange.similarity(apple)
apple_orange == orange_apple

Output

When you run the code, you will see the following output −

True

Properties

Following are the properties of Lexeme Class.

Lexeme.vector

This Lexeme property represents a real-valued meaning. It will return a one-dimensional array representing the lexeme’s semantics.

Example

An example of Lexeme.vector property is given below −

import spacy
nlp_model = spacy.load("en_core_web_sm")
apple = nlp_model.vocab["apple"]
apple.vector.dtype

Output

You will see the following output −

dtype('float32')

Lexeme.vector_norm

This token property represents the L2 norm of the lexeme’s vector representation.

Example

An example of Lexeme.vector_norm property is as follows −

import spacy
nlp_model = spacy.load("en_core_web_sm")
apple = nlp.vocab["apple"]
pasta = nlp.vocab["pasta"]
apple.vector_norm != pasta.vector_norm

Output

You will see the following output −

True