Natural Language Processing (NLP) - tech9tel/ai GitHub Wiki
🌐 Natural Language Processing (NLP)
📚 Introduction to NLP
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human (natural) languages. It involves various techniques to process and analyze text data in a way that allows computers to understand, interpret, and generate human language. NLP is widely used in applications such as text classification, sentiment analysis, machine translation, and more.
🔧 NLP Techniques
📝 Text Classification
Text Classification involves assigning predefined labels to text based on its content. This can be used to categorize news articles, filter spam emails, or classify customer reviews.
Common Techniques:
- Supervised learning (e.g., Naive Bayes, SVM, Deep Learning)
- Transfer learning (e.g., fine-tuning BERT, GPT models)
✉️ Example: Categorizing emails as "Spam" or "Not Spam".
💭 Sentiment Analysis
Sentiment Analysis focuses on determining the sentiment (positive, negative, neutral) expressed in a piece of text. It's commonly used for analyzing customer feedback, social media posts, and product reviews.
Common Techniques:
- TextBlob
- VADER
- Pretrained transformer models like BERT for fine-tuning.
👍👎 Example: Analyzing the sentiment in a customer review to determine if it's positive or negative.
🏷️ Named Entity Recognition (NER)
NER is used to identify and classify proper names (entities) such as people, organizations, locations, dates, and more, within text. It helps to extract structured data from unstructured text.
Common Techniques:
- Rule-based systems (e.g., using dictionaries)
- Machine learning algorithms (e.g., CRF, BiLSTM-CRF)
- Pretrained models like SpaCy, BERT
🏙️ Example: Identifying "New York" as a location and "Microsoft" as an organization in a sentence.
🌍 Machine Translation
Machine Translation involves automatically translating text from one language to another. It helps break down language barriers and facilitates multilingual communication.
Common Techniques:
- Statistical machine translation (SMT)
- Neural machine translation (NMT), e.g., using seq2seq models, Transformer architecture
🌐 Example: Google Translate automatically translating text between languages.
⚙️ Common NLP Tools & Libraries
- SpaCy – A fast and efficient library for NLP tasks like NER, tokenization, and POS tagging.
- NLTK – A comprehensive library for text processing and analysis with a variety of algorithms.
- Hugging Face Transformers – A library for pre-trained transformer models like BERT, GPT, T5 for various NLP tasks.
- TextBlob – A simple library for text processing, providing tools for sentiment analysis and more.
🚀 Real-World Applications of NLP
- 🛍️ Customer Support – Chatbots and virtual assistants that understand and respond to customer queries.
- 📧 Email Filtering – Automatically classify and prioritize emails as important or spam.
- 📰 News Categorization – Organize and tag news articles based on topics such as technology, politics, sports, etc.
- 🌍 Multilingual Communication – Real-time translations between different languages, like in apps or websites.
🔮 Future of NLP
- Improved Multilingual Models – Models that seamlessly support multiple languages without requiring retraining.
- Few-shot and Zero-shot Learning – Reducing the need for large datasets to train NLP models on new tasks.
- More Context-Aware Models – Leveraging deeper understanding of context to improve the accuracy of NLP systems.