NLP data Scientist mind-map
1. Natural Language Processing (Central Node)
2. Overview
• Definition
3. Core Concepts
• Tokenization: Breaking text into individual tokens (words, phrases).
• Named Entity Recognition (NER): Identifying and classifying key information (entities) in text.
• Part-of-Speech Tagging: Assigning parts of speech to each word (e.g., noun, verb).
• Stemming: Reducing words to their base or root form.
• Lemmatization: Similar to stemming but more context-aware, reducing words to their base form.
4. Techniques
• Word Embeddings: Representing words in a continuous vector space (e.g., Word2Vec, GloVe).
• Bag of Words: Representing text as the frequency of words.
• TF-IDF (Term Frequency-Inverse Document Frequency): Reflecting how important a word is to a document in a collection.
5. Algorithms
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Support Vector Machines (SVM)
• Naive Bayes
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• Transformer Networks
6. Transformer Models
• GPT (Generative Pre-trained Transformer)
• BERT (Bidirectional Encoder Representations from Transformers)
7. Tools and Libraries
• NLTK (Natural Language Toolkit)
• Gensim
• SpaCy
8. Applications
• (Examples of real-world uses, although not specified in the image, common applications include text summarization, sentiment analysis, machine translation, chatbots, etc.)
9. Challenges
• Multilingual Processing: Handling multiple languages.
• Context Understanding: Grasping the context in which words are used.
• Ambiguity: Words or sentences having multiple meanings.
• Sarcasm Detection: Identifying sarcastic remarks.
10. Future Trends
• Multimodal NLP: Combining text with other modalities like images and audio.
• Real-Time Processing: Handling and analyzing text data in real-time.
• Explainable AI: Making AI models more interpretable.
• Few-Shot Learning: Training models with very few examples.
