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) Mac...
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