NLP Essentials
GitHub Author
  • 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)
Powered by GitBook

Copyright © 2023 All rights reserved

On this page

Was this helpful?

Export as PDF

Distributional Semantics

PreviousHomeworkNextDistributional Hypothesis

Last updated 1 year ago

Was this helpful?

Distributional semantics represents the meaning of words based on their distributional properties in large corpora of text. It follows the distributional hypothesis, which states that "words with similar meanings tend to occur in similar contexts".

Contents

Distributional Hypothesis
Word Representations
Latent Semantic Analysis
Neural Networks
Word2Vec
Homework