spaCy - Container Lexeme Class

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 imppes, 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")
pbrary = lambda text: text in ["Website", "Tutorialspoint.com"]
my_pbrary = nlp_model.vocab.add_flag(pbrary)
nlp_model.vocab["Tutorialspoint.com"].check_flag(my_pbrary)

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_pbrary)

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