Natural Language Processing(NLP)

 

Step 1: Input Text

Collect raw text data from sources such as documents, emails, social media, or user input.

Step 2: Text Preprocessing

Clean and prepare the text for analysis.

  • Convert text to lowercase

  • Remove punctuation and special characters

  • Remove stop words (e.g., is, the, and)

  • Tokenization (split text into words or sentences)

  • Stemming or Lemmatization (reduce words to root form)

Step 3: Feature Extraction

Convert text into numerical representations.

  • Bag of Words (BoW)

  • Term Frequency–Inverse Document Frequency (TF-IDF)

  • Word Embeddings (Word2Vec, GloVe)

Step 4: Model Selection

Choose an appropriate NLP model based on the task.

  • Naïve Bayes

  • Support Vector Machine (SVM)

  • Recurrent Neural Networks (RNN)

  • Transformer models

Step 5: Model Training

Train the selected model using labeled or unlabeled data.

Step 6: NLP Task Execution

Perform specific NLP tasks such as:

  • Text Classification

  • Sentiment Analysis

  • Named Entity Recognition (NER)

  • Machine Translation

  • Text Summarization

Step 7: Output Generation

Generate meaningful results such as predictions, summaries, or responses.

Step 8: Evaluation and Optimization

Evaluate model performance using metrics like accuracy, precision, recall, and improve using tuning techniques.

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