NLP Essentials
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  • Overview
    • Syllabus
    • Schedule
    • Development Environment
    • Homework
  • Text Processing
    • Frequency Analysis
    • Tokenization
    • Lemmatization
    • Regular Expressions
    • Homework
  • Language Models
    • N-gram Models
    • Smoothing
    • Maximum Likelihood Estimation
    • Entropy and Perplexity
    • Homework
  • Vector Space Models
    • Bag-of-Words Model
    • Term Weighting
    • Document Similarity
    • Document Classification
    • Homework
  • Distributional Semantics
    • Distributional Hypothesis
    • Word Representations
    • Latent Semantic Analysis
    • Neural Networks
    • Word2Vec
    • Homework
  • Contextual Encoding
    • Subword Tokenization
    • Recurrent Neural Networks
    • Transformer
    • Encoder-Decoder Framework
    • Homework
  • NLP Tasks & Applications
    • Text Classification
    • Sequence Tagging
    • Structure Parsing
    • Relation Extraction
    • Question Answering
    • Machine Translation
    • Text Summarization
    • Dialogue Management
    • Homework
  • Projects
    • Speed Dating
    • Team Formation
    • Proposal Pitch
    • Proposal Report
    • Live Demonstration
    • Final Report
    • Team Projects
      • Team Projects (2024)
    • Project Ideas
      • Project Ideas (2024)
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Text Processing

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Last updated 4 months ago

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Text processing refers to the manipulation and analysis of textual data through techniques applied to raw text, making it more structured, understandable, and suitable for various applications.

Sections

If you are not acquainted with Python programming, we strongly recommend going through all the examples in this section, as they provide detailed explanations of packages and functions commonly used for language processing.

Frequency Analysis
Tokenization
Lemmatization
Regular Expressions
Homework