Counting n-gram frequency in python nltk

Counting n-gram frequency in python nltk

You can count n-gram frequency using the Natural Language Toolkit (NLTK) in Python by first tokenizing your text and then using NLTK's ngrams function to generate n-grams. After that, you can use Python's collections module to count the frequency of each n-gram. Here's a step-by-step example:

import nltk from nltk.util import ngrams from nltk.tokenize import word_tokenize from collections import Counter # Sample text text = "This is a sample sentence. This sentence contains some words." # Tokenize the text (you can use more advanced tokenization methods if needed) tokens = word_tokenize(text) # Define the value of 'n' for n-grams n = 2 # Change this to the desired n-gram size # Generate n-grams n_grams = list(ngrams(tokens, n)) # Count the frequency of each n-gram n_gram_frequency = Counter(n_grams) # Print the frequency of each n-gram for n_gram, frequency in n_gram_frequency.items(): print(f"{n}-gram: {n_gram}, Frequency: {frequency}")

In this example:

  1. We import the necessary modules, including nltk, collections, and specific NLTK functions for tokenization and n-grams.

  2. We define a sample text and tokenize it using word_tokenize. You can replace this with your own text or use more advanced tokenization methods if needed.

  3. We specify the value of n to determine the size of the n-grams (e.g., n = 2 for bigrams, n = 3 for trigrams, etc.).

  4. We generate n-grams using ngrams(tokens, n).

  5. We count the frequency of each n-gram using Counter(n_grams) from the collections module.

  6. Finally, we print the frequency of each n-gram.

You can adjust the value of n to count n-grams of different sizes, and you can replace the sample text with your own text data for analysis.

Examples

  1. Python NLTK count unigram frequency

    • Description: This query involves counting the frequency of unigrams (single words) in text data using NLTK (Natural Language Toolkit) in Python, which is a fundamental step in text analysis and processing.
    # Code to count unigram frequency using NLTK in Python from nltk import FreqDist from nltk.tokenize import word_tokenize def count_unigram_frequency(text): tokens = word_tokenize(text) freq_dist = FreqDist(tokens) return freq_dist # Example usage text = "This is a sample sentence to demonstrate unigram frequency counting." unigram_freq_dist = count_unigram_frequency(text) print("Unigram frequency distribution:", unigram_freq_dist.most_common())

  2. Python NLTK count bigram frequency

    • Description: This query focuses on counting the frequency of bigrams (pairs of two consecutive words) in text data using NLTK in Python, enabling analysis of word associations and patterns.
    # Code to count bigram frequency using NLTK in Python from nltk import FreqDist from nltk.util import ngrams from nltk.tokenize import word_tokenize def count_bigram_frequency(text): tokens = word_tokenize(text) bigrams = ngrams(tokens, 2) freq_dist = FreqDist(bigrams) return freq_dist # Example usage text = "This is a sample sentence to demonstrate bigram frequency counting." bigram_freq_dist = count_bigram_frequency(text) print("Bigram frequency distribution:", bigram_freq_dist.most_common())

  3. Python NLTK count trigram frequency

    • Description: This query addresses counting the frequency of trigrams (sequences of three consecutive words) in text data using NLTK in Python, facilitating deeper linguistic analysis and understanding.
    # Code to count trigram frequency using NLTK in Python from nltk import FreqDist from nltk.util import ngrams from nltk.tokenize import word_tokenize def count_trigram_frequency(text): tokens = word_tokenize(text) trigrams = ngrams(tokens, 3) freq_dist = FreqDist(trigrams) return freq_dist # Example usage text = "This is a sample sentence to demonstrate trigram frequency counting." trigram_freq_dist = count_trigram_frequency(text) print("Trigram frequency distribution:", trigram_freq_dist.most_common())

  4. Python NLTK count n-gram frequency from text file

    • Description: This query involves counting the frequency of n-grams (sequences of 'n' consecutive words) in text data from a file using NLTK in Python, enabling analysis of textual patterns and structures.
    # Code to count n-gram frequency from text file using NLTK in Python from nltk import FreqDist from nltk.tokenize import word_tokenize def count_ngram_frequency_from_file(file_path, n): with open(file_path, 'r') as file: text = file.read() tokens = word_tokenize(text) n_grams = [ngrams(tokens, n) for n in range(1, n + 1)] freq_dist = [FreqDist(gram) for gram in n_grams] return freq_dist # Example usage file_path = 'sample.txt' n = 3 # For trigrams trigram_freq_dist = count_ngram_frequency_from_file(file_path, n) print("Trigram frequency distribution:", trigram_freq_dist[-1].most_common())

  5. Python NLTK count n-gram frequency with custom tokenizer

    • Description: This query involves counting the frequency of n-grams in text data using NLTK in Python with a custom tokenizer, allowing flexibility in preprocessing text for n-gram analysis.
    # Code to count n-gram frequency with custom tokenizer using NLTK in Python from nltk import FreqDist from nltk.util import ngrams def count_ngram_frequency_with_tokenizer(text, tokenizer, n): tokens = tokenizer(text) n_grams = ngrams(tokens, n) freq_dist = FreqDist(n_grams) return freq_dist # Example usage text = "This is a sample sentence to demonstrate n-gram frequency counting." custom_tokenizer = lambda text: text.split() # Example of a custom tokenizer n = 3 # For trigrams trigram_freq_dist = count_ngram_frequency_with_tokenizer(text, custom_tokenizer, n) print("Trigram frequency distribution:", trigram_freq_dist.most_common())

  6. Python NLTK count n-gram frequency with stopwords removal

    • Description: This query focuses on counting the frequency of n-grams in text data using NLTK in Python with stopwords removal, helping to eliminate common words and focus on meaningful phrases.
    # Code to count n-gram frequency with stopwords removal using NLTK in Python from nltk import FreqDist from nltk.util import ngrams from nltk.corpus import stopwords from nltk.tokenize import word_tokenize def count_ngram_frequency_with_stopwords_removal(text, n): stop_words = set(stopwords.words('english')) tokens = [word for word in word_tokenize(text) if word.lower() not in stop_words] n_grams = ngrams(tokens, n) freq_dist = FreqDist(n_grams) return freq_dist # Example usage text = "This is a sample sentence to demonstrate n-gram frequency counting with stopwords removal." n = 2 # For bigrams bigram_freq_dist = count_ngram_frequency_with_stopwords_removal(text, n) print("Bigram frequency distribution with stopwords removal:", bigram_freq_dist.most_common())

  7. Python NLTK count n-gram frequency with stemming

    • Description: This query addresses counting the frequency of n-grams in text data using NLTK in Python with stemming, which reduces words to their root form, aiding in capturing variations of words.
    # Code to count n-gram frequency with stemming using NLTK in Python from nltk import FreqDist from nltk.util import ngrams from nltk.stem import PorterStemmer from nltk.tokenize import word_tokenize def count_ngram_frequency_with_stemming(text, n): stemmer = PorterStemmer() tokens = [stemmer.stem(word) for word in word_tokenize(text)] n_grams = ngrams(tokens, n) freq_dist = FreqDist(n_grams) return freq_dist # Example usage text = "This is a sample sentence to demonstrate n-gram frequency counting with stemming." n = 2 # For bigrams bigram_freq_dist = count_ngram_frequency_with_stemming(text, n) print("Bigram frequency distribution with stemming:", bigram_freq_dist.most_common())

  8. **Python NLTK count n-gram frequency with

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