https://github.com/shruthimohan03/basic-sentence-generation-using-ngram

Generating similar sentences given input sentences using n-gram approach
https://github.com/shruthimohan03/basic-sentence-generation-using-ngram

natural-language-processing ngrams sentence-generation

Last synced: 6 months ago
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Generating similar sentences given input sentences using n-gram approach

• Host: GitHub
• URL: https://github.com/shruthimohan03/basic-sentence-generation-using-ngram
• Owner: shruthimohan03
• Created: 2024-04-10T14:34:00.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-21T08:20:58.000Z (8 months ago)
• Last Synced: 2025-02-21T09:28:32.460Z (8 months ago)
• Topics: natural-language-processing, ngrams, sentence-generation
• Language: Jupyter Notebook
• Homepage:
• Size: 2.74 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Sentence Generation using N-grams from Text Data

This code demonstrates the process of generating sentences using an n-gram model based on a dataset of text. Here's a brief description of each part:

1. Reading and Preprocessing Data:

   - The code reads a dataset from a CSV file named 'Context.csv' using pandas.

   - It selects only the 'Text' column from the dataset and takes a subset of the data (first 3000 rows).

   - The text data is converted to lowercase and tokenized using NLTK's word_tokenize function.

2. Creating the N-gram Model:

   - The code defines a function named `generate_ngram` that creates n-grams (bigrams in this case) from a list of words and stores them in a dictionary named `master_ngram`.

   - For each sentence in the preprocessed data, the `generate_ngram` function is called to create and store the n-grams in the `master_ngram` dictionary.

3. Generating Sentences:

   - Another function named `generate_sentence` is defined to generate sentences based on the provided n-gram model.

   - Given an input sentence, the code preprocesses it, creates n-grams, and appends them to the `master_ngram` dictionary.

   - The `generate_sentence` function then uses the input sentence and the n-gram model to generate a new sentence.

4. Example Usage:

   - The code provides three example input sentences: "I want to understand", "I want to know how someone", and "I want to make a statement".

   - Each input sentence undergoes preprocessing, n-gram creation, and sentence generation using the previously defined functions.

   - The generated sentences based on each input are printed out.

Overall, this code demonstrates a basic approach to generating sentences using an n-gram model trained on a dataset of text. It shows how to preprocess the data, create the n-gram model, and use it to generate sentences based on user-provided input.