Word Embeddings

Word embeddings are a method used in natural language processing (NLP) to represent words as real-valued vectors in a predefined vector space. The goal is to encode the semantic meaning of words in such a way that words with similar meanings are represented by vectors that are close to each other in the vector space.

Word Embeddings

Areas of application

  • Natural Language Processing (NLP)
  • Sentiment Analysis
  • Information Retrieval
  • Machine Translation
  • Text Classification
  • Speech Recognition
  • Chatbots and Conversational Agents
  • Recommendation Systems

Example

For instance, the word embedding for the word ‘dog’ might be represented as [0.7, 0.3, 0.1], indicating that it is closely related to the words ‘cat’ and ‘bone’, while being distinct from the word ‘car’.

Resources