https://en.m.wikipedia.org/wiki/Natural_language_processing
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Feature extaction includes vectorization using Bag of word, word2vec, TF-IDF.
NLP model developing steps by ChatGPT:
To develop an NLP model using the terms provided, the process generally follows these steps:
1. **Data Collection**: Gather and prepare a dataset of text that will be used for training and testing the NLP model.
2. **Tokenization**:
- **Explanation**: Split the text into smaller units called tokens, which can be words, subwords, or characters.
- **Purpose**: Tokenization allows the model to process and analyze text at a granular level.
- **Example**: "The cat sat on the mat." becomes ["The", "cat", "sat", "on", "the", "mat"].
3. **Stop Words Removal**:
- **Explanation**: Remove common words that have little meaning on their own, such as "the," "is," and "and."
- **Purpose**: Reduce noise in the data, focusing the model on more meaningful words.
- **Example**: After removal, ["The", "cat", "sat", "on", "the", "mat"] might become ["cat", "sat", "mat"].
4. **Stemming**:
- **Explanation**: Reduce words to their root form by removing suffixes (e.g., "running" → "run").
- **Purpose**: Simplify words to a common base form, reducing vocabulary size.
- **Example**: "running", "runner", "ran" all stem to "run".
5. **Lemmatization**:
- **Explanation**: Similar to stemming, but it reduces words to their base or dictionary form, known as the lemma, considering the context.
- **Purpose**: Ensure words are reduced to their meaningful base form, which may differ based on context.
- **Example**: "better" → "good", "running" → "run".
6. **Feature Extraction**:
- **Explanation**: Convert tokens into numerical features that the model can understand.
- **Methods**:
- **Bag of Words (BoW)**: Represents text by the frequency of words in the document.
- **TF-IDF (Term Frequency-Inverse Document Frequency)**: Adjusts the frequency of words by their importance across documents. (https://bdi.or.th/big-data-101/tf-idf-1/)
- **Word Embedding**: An advanced method that transforms words into dense vectors capturing semantic relationships between words. Common methods include Word2Vec, GloVe, and FastText.
- **Purpose**: Transform text data into a format suitable for modeling, whether through simple frequency counts or more complex vector representations.
7. **Modeling with Deep Learning Algorithms**:
- **Explanation**: Use deep learning techniques to build the NLP model.
- **Purpose**: Leverage complex neural networks to capture patterns and relationships in text data.
- **Common Models**:
- **RNN (Recurrent Neural Network)**: Suitable for sequence-based tasks like text generation.
- **LSTM (Long Short-Term Memory)**: An advanced form of RNN that handles long-term dependencies.
- **Transformer**: State-of-the-art model architecture for NLP tasks (e.g., BERT, GPT).
8. **Model Training**:
- **Explanation**: Train the deep learning model using the processed text data.
- **Purpose**: Optimize model parameters to minimize error and improve accuracy.
9. **Evaluation**:
- **Explanation**: Assess the model's performance on a validation set.
- **Purpose**: Ensure the model generalizes well to unseen data.
10. **Deployment**:
- **Explanation**: Integrate the trained model into a production environment.
- **Purpose**: Make the model available for practical use.
11. **Monitoring and Maintenance**:
- **Explanation**: Continuously monitor the model's performance and update it as needed.
- **Purpose**: Ensure the model remains accurate and relevant over time
Example implementation of ChatBot using LSTM
