Computing N Grams using Python

Computing N Grams using Python

N-grams are contiguous sequences of n items (words, characters, or symbols) from a given sample of text or speech. You can compute n-grams in Python using various libraries, but the nltk (Natural Language Toolkit) library is commonly used for this purpose. Here's how to compute n-grams using the nltk library:

  1. Install NLTK:

    If you haven't already, you need to install the nltk library. You can install it using pip:

    pip install nltk

  2. Import NLTK and Tokenize Text:

    Import the nltk library and tokenize your text into words or tokens. You can use the nltk.word_tokenize() function for this purpose.

    import nltk from nltk import word_tokenize nltk.download('punkt') # Download the necessary NLTK data text = "This is a sample text for computing n-grams using NLTK." tokens = word_tokenize(text)

  3. Compute N-Grams:

    Use the ngrams function from the nltk.util module to compute n-grams of the desired order (n).

    from nltk.util import ngrams n = 3 # You can change this to compute different n-grams (e.g., bigrams, trigrams, etc.) n_grams = list(ngrams(tokens, n))

    In this example, n is set to 3, so it computes trigrams. You can change the value of n to compute different n-grams (e.g., set n = 2 for bigrams).

  4. Print or Use N-Grams:

    You can now print or use the computed n-grams as needed.

    print(n_grams)

    This will print the list of trigrams based on the input text.

Here's a complete example:

import nltk from nltk import word_tokenize from nltk.util import ngrams nltk.download('punkt') # Download the necessary NLTK data text = "This is a sample text for computing n-grams using NLTK." tokens = word_tokenize(text) n = 3 # Compute trigrams n_grams = list(ngrams(tokens, n)) print(n_grams)

This code snippet will compute and print trigrams from the input text. You can adjust the value of n to compute different types of n-grams (e.g., bigrams, trigrams, etc.).

Examples

  1. How to compute N-grams using Python?

    Description: N-grams are contiguous sequences of n items from a given sample of text or speech. In Python, you can easily compute N-grams using libraries such as NLTK or scikit-learn. Below is a simple implementation using NLTK.

    from nltk import ngrams, word_tokenize def compute_ngrams(text, n): tokens = word_tokenize(text) return list(ngrams(tokens, n)) text = "This is a sample sentence for computing N-grams." n = 3 print(compute_ngrams(text, n))

  2. Python code for generating N-grams from text data.

    Description: Generating N-grams is a common task in natural language processing and text analysis. Here's a Python code snippet demonstrating how to generate N-grams using list comprehension.

    def generate_ngrams(text, n): words = text.split() return [' '.join(words[i:i + n]) for i in range(len(words) - n + 1)] text = "Python code for generating N-grams from text data" n = 2 print(generate_ngrams(text, n))

  3. How to implement N-grams in Python from scratch?

    Description: Implementing N-grams from scratch provides a deeper understanding of the underlying concept. Here's a Python function to generate N-grams without using any external libraries.

    def generate_ngrams(text, n): words = text.split() ngrams_list = [] for i in range(len(words) - n + 1): ngrams_list.append(' '.join(words[i:i + n])) return ngrams_list text = "Implementing N-grams in Python from scratch" n = 3 print(generate_ngrams(text, n))

  4. Python code for computing character-level N-grams.

    Description: N-grams are not limited to words; they can also be computed at the character level. Here's a Python function to compute character-level N-grams.

    def compute_char_ngrams(text, n): return [text[i:i + n] for i in range(len(text) - n + 1)] text = "Computing character-level N-grams using Python" n = 4 print(compute_char_ngrams(text, n))

  5. How to calculate N-grams frequency in Python?

    Description: Calculating the frequency of N-grams is essential for various text analysis tasks. Here's a Python code snippet demonstrating how to calculate the frequency of N-grams using a Counter.

    from collections import Counter from nltk import ngrams, word_tokenize def calculate_ngram_frequency(text, n): tokens = word_tokenize(text) ngrams_list = list(ngrams(tokens, n)) return Counter(ngrams_list) text = "Calculate N-grams frequency in Python" n = 2 print(calculate_ngram_frequency(text, n))

  6. Python implementation for computing N-grams with smoothing techniques.

    Description: Smoothing techniques are often used in language modeling to handle unseen N-grams. Here's a Python function that computes N-grams with Laplace smoothing.

    from collections import Counter from nltk import ngrams, word_tokenize def compute_ngrams_with_smoothing(text, n, k=1): tokens = word_tokenize(text) ngrams_list = list(ngrams(tokens, n)) counts = Counter(ngrams_list) total = len(ngrams_list) smoothed_counts = {gram: (counts[gram] + k) / (total + k * len(set(ngrams_list))) for gram in counts} return smoothed_counts text = "Python implementation for computing N-grams with smoothing techniques" n = 2 print(compute_ngrams_with_smoothing(text, n))

  7. How to use scikit-learn for computing N-grams in Python?

    Description: Scikit-learn provides a convenient way to compute N-grams using its CountVectorizer module. Here's an example of how to use it.

    from sklearn.feature_extraction.text import CountVectorizer def compute_ngrams_with_sklearn(texts, n): vectorizer = CountVectorizer(ngram_range=(n, n), token_pattern=r'\b\w+\b', min_df=1) X = vectorizer.fit_transform(texts) return vectorizer.get_feature_names_out() texts = ["This is an example", "Another example for computing N-grams"] n = 2 print(compute_ngrams_with_sklearn(texts, n))

  8. Python code to extract bi-grams from a text.

    Description: Bi-grams, or 2-grams, are sequences of two adjacent elements from a given text. Here's a Python function to extract bi-grams using NLTK.

    from nltk import bigrams, word_tokenize def extract_bigrams(text): tokens = word_tokenize(text) return list(bigrams(tokens)) text = "Python code to extract bi-grams from a text" print(extract_bigrams(text))

  9. How to handle stopwords when computing N-grams in Python?

    Description: Stopwords are common words that often do not carry much meaning in text analysis. Here's a Python code snippet demonstrating how to handle stopwords when computing N-grams using NLTK.

    from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk import ngrams def compute_ngrams_without_stopwords(text, n): stop_words = set(stopwords.words('english')) word_tokens = word_tokenize(text.lower()) filtered_tokens = [word for word in word_tokens if word not in stop_words] return list(ngrams(filtered_tokens, n)) text = "How to handle stopwords when computing N-grams in Python" n = 3 print(compute_ngrams_without_stopwords(text, n))

  10. Python code to generate sentence-level N-grams.

    Description: While N-grams are commonly associated with words, they can also be applied at the sentence level. Here's a Python function to generate N-grams at the sentence level.

    def generate_sentence_ngrams(text, n): sentences = text.split('.') ngrams_list = [] for sentence in sentences: words = sentence.split() ngrams_list.extend([' '.join(words[i:i + n]) for i in range(len(words) - n + 1)]) return ngrams_list text = "Python code to generate sentence-level N-grams" n = 2 print(generate_sentence_ngrams(text, n))

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