NLP Tutorial

Natural Language Processing (NLP) is an interdisciplinary field of artificial intelligence and linguistics, dedicated to enabling computers to understand, interpret, and generate human language.

NLP combines knowledge from computer science, artificial intelligence, and linguistics, aiming to achieve natural language communication between humans and machines.

Core Tasks of NLP

  1. Text Understanding: Enable computers to understand the meaning of human language.
  2. Text Generation: Enable computers to generate natural language text.
  3. Language Translation: Achieve automatic translation between different languages.
  4. Sentiment Analysis: Identify the emotional tendency expressed in text.

Who Should Learn NLP

  • Computer Science/AI majors: Have a foundation in programming and algorithms, wish to go deeper into AI.
  • Linguistics or psychology researchers: Interested in language structure and cognitive science, want to use technical methods to analyze language phenomena.
  • Data scientists/engineers: Wish to expand text data processing capabilities, apply to scenarios such as recommendation systems and search engines.
  • Cross-domain practitioners: Such as those in finance, healthcare, legal and other industries who need to process large amounts of text data.
  • Beginners interested in AI: Even with zero foundation, they can gradually get started through systematic learning.

Prerequisite Knowledge

1. Math and Statistics Basics

  • Probability and Statistics: Bayes' theorem, probability distributions, statistical tests, etc. (NLP models such as language models rely on probability).

  • Linear Algebra: Matrix operations, vector spaces (basis for word embeddings and neural networks).

  • Calculus: Gradient descent, optimization algorithms (to understand the model training process).

2. Programming Skills

  • Python: Mainstream NLP libraries (such as NLTK, spaCy, Hugging Face) are all based on Python.

  • Basic Algorithms: Understand recursion and dynamic programming (such as the edit distance algorithm).

  • Data Processing: Familiarity with libraries such as Pandas and NumPy.

3. Linguistics Background (not required but a plus)

  • Grammar and Semantics: Part-of-speech tagging, syntactic trees, semantic role labeling, etc.

  • Corpus Linguistics: Familiarity with the structure of text data and annotation methods.

4. Machine Learning Basics

  • Supervised Learning: Classification, sequence labeling (such as Naive Bayes, SVM, CRF).

  • Deep Learning: RNN, LSTM, Transformer (the basis for models such as BERT/GPT).

  • Tools and Frameworks：Scikit-learn、PyTorch/TensorFlow。

5. Tools and Resources

  • NLP Libraries：NLTK、spaCy、Hugging Face Transformers。

  • Data Processing: Regular expressions, SQL (for text cleaning and storage).

Learning Path Recommendations

1. Beginner Stage

  • Learn Python and basic math → Master basic NLP tasks (tokenization, part-of-speech tagging) → Use NLTK/spaCy to implement simple projects.

2. Advanced Stage

  • Learn machine learning → Implement text classification, sentiment analysis → Learn RNN/Transformer.

3. Practical Stage

  • Participate in Kaggle competitions (such as Quora question-answer matching) → Reproduce paper models → Deploy NLP services (such as chatbots).

Application Scenarios of NLP

  • Intelligent customer service and chatbots
  • Machine translation (such as Google Translate)
  • Voice assistants (such as Siri, Alexa)
  • Spam email filtering
  • Text summarization generation
  • Sentiment analysis (product review analysis)
Other Extensions