Dense Continuous Word Modeling System

A Dense Continuous Word Modeling System is a Word Embedding System that is a continuous word vector space modeling system that implements a dense continuous word vector space modeling algorithm (to solve a dense continuous word vector space modeling task which requires a dense continuous word model).

• AKA: Dense Continuous Word Modeling System.
• Example(s):
  • Distributional Dense Continuous Word Vector Space Modeling System.
  • …
• Counter-Example(s):
  • Sparse Word Embedding System.
• See: Natural Language Processing System, Semantic Analysis System, Word Embedding Dataset, Vector Space Model.

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References

2020

• (Jurafsky & Martin, 2020) ⇒ Daniel Jurafsky, and James H. Martin. (2020). “Vector Semantics and Embeddings.” In: Speech and Language Processing (3rd ed. draft).
  • QUOTE: Vector semantic models fall into two classes: sparse and dense(...).
    • Dense vector models have dimensionality 50–1000. Word2vec algorithms like skip-gram are a popular way to compute dense embeddings. Skip-gram trains a logistic regression classifier to compute the probability that two words are ‘likely to occur nearby in text’. This probability is computed from the dot product between the embeddings for the two words.