Machine Learning With Python: The Definitive Tool to Improve Your Python Programming and Deep Learning to Take You to The Next Level of Coding and Algorithms Optimization by Algore Matt

Author:Algore, Matt [Algore, Matt]
Language: eng
Format: epub
Published: 2021-01-06T16:00:00+00:00

10. Explain the N-Gram Method.

Simply put, an N-gram is a contiguous sequence of n items in the given text. N-gram method is a probabilistic model used to predict an item in a sequence based on the previous n-1 items. You can choose the items to be either the words, phrases, etc. If n is 1, then it is called 1-gram; for n = 2, it is 2-gram or bigram.

N-grams can be used for approximate matching. Since they convert the sequence of items into a set of n-grams, you can compare one sequence with another by measuring the percentage of common n-grams in both of them.

11. How Many 3-Grams Can Be Generated from this Sentence "I Love New York Style Pizza"?

Breaking the given sentence into 3-grams, you get:

A.​ I love New

B.​ love New York

C.​ New York style

D.​ York-style pizza

# We will use the CountVectorizer package to demonstrate how to use N-Gram with Scikit-Learn.

# CountVectorizer converts a collection of text documents to a matrix of token counts.

# In our case, there is only one document.

from sklearn.feature_extraction.text import CountVectorizer

# N-gram_range specifies the lower and upper boundary on the range of N-gram tokens

# to be extracted. For our example, the range is from 3 to 3.

# We have to specify the token pattern because, by default, CountVectorizer treats single character words as stop words.

vectorizer = CountVectorizer(ngram_range=(3, 3),

token pattern = r"(?u)\w+",

lowercase=False)

# Now, let's fit the model with our input text

vectorizer.fit(["I love New York style pizza"])

# This will populate vectorizer's vocabulary_ dictionary with the tokens.

# Let's see the results of this vocabulary

print(vectorizer.vocabulary_.keys())

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.