Continuous Dense Distributional Word Vector

A Continuous Dense Distributional Word Vector is a dense distributional word vector that is a continuous distributional word vector that represents words as continuous-valued dense vectors where each dimension captures semantic or syntactic features.

• AKA: Neural Word Vector, Dense Word Embedding.
• Context:
  • It can typically encode semantic relationships between words through distance metrics in vector space.
  • It can often capture multiple aspects of word meaning through its distributed representation.
  • It can range from being a Low Dimensional Word Vector to being a High Dimensional Word Vector, depending on its dimensionality.
  • It can be created by a Continuous Dense Distributional Word Model Training System.
  • It can support Word Similarity Tasks and Word Analogy Tasks.
  • It can preserve semantic relationships through vector arithmetic operations.
  • ...
• Examples:
  • Neural Word Vector Types, such as:
    • Word2Vec Vector for capturing semantic relationships.
    • GloVe Vector based on global word co-occurrence statistics.
    • FastText Vector incorporating subword information.
  • Vector Representations, such as:
    • [0.128, 0.208, 0.008] ← f("United Nations").
    • Vectors based on Tensor Factorization.
    • Skip Thought Vectors using Skipgram Matrix Factorization.
  • ...
• Counter-Examples:
  • Bag of Words Vector, which uses sparse discrete counts.
  • One Hot Word Vector, which lacks semantic density.
  • Binary Word Vector, which uses only 0/1 values.
  • Sparse Distributional Word Vector, which is not dense.
• See: Continuous Dense Distributional Vector, Word Embedding, Neural Word Representation., skipgram

---

---