Word Vector Model Training Algorithm

A Word Vector Model Training Algorithm is a vector-based modeling algorithm that can be implemented into a Word Vector Model Training System (that solved a Word Vector Model Training Task).

• AKA: Data-Driven Word Vector Model Creation Algorithm.
• Context:
  • … that creates word vectorizing functions
• Counter-Example(s):
  • a Word Classification Training Algorithm.
  • a Sentence Vector Model Training Algorithm.
  • a Document Vector Model Training Algorithm.
• See: Vector Space, Word Vector Space.

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References

2012

• (Huang et al., 2012) ⇒ Eric H. Huang, Richard Socher, Christopher D. Manning, and Andrew Y. Ng. (2012). “Improving Word Representations via Global Context and Multiple Word Prototypes.” In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012).
  • QUOTE: Vector-space models (VSM) represent word meanings with vectors that capture semantic and syntactic information of words. These representations can be used to induce similarity measures by computing distances between the vectors, leading to many useful applications, such as information retrieval (Manning et al., 2008), document classification (Sebastiani, 2002) and question answering (Tellex et al., 2003).

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