Continuous Dense Distributional Word Vector Space Model
(Redirected from Dense Distributional Word Vectorization Model)
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A Continuous Dense Distributional Word Vector Space Model is a Distributional Word Vector Space Model that is a continuous word vector space model and a dense word vector space model.
- Context:
- It can be produced by a Continuous Dense Distributional Word Model Training System (that solves a continuous dense distributional word model training task)
- It can be produced by a Distributional Word Embedding System (that solves a Distributional Word Embedding Modeling Task).
- Example(s):
- Counter-Example(s):
- See: Dense Word Vectorization Function, Continuous Word Vectorization Function, Word Vector Space Model Mapping Function.
References
2014
- (Rei & Briscoe, 2014) ⇒ M. Rei, and T. Briscoe. (2014). “Looking for Hyponyms in Vector Space.” In: Proceedings of CoNLL-2014.
2013
- http://google-opensource.blogspot.be/2013/08/learning-meaning-behind-words.html
- QUOTE: Word2vec uses distributed representations of text to capture similarities among concepts. For example, it understands that Paris and France are related the same way Berlin and Germany are (capital and country), and not the same way Madrid and Italy are. This chart shows how well it can learn the concept of capital cities, just by reading lots of news articles -- with no human supervision:
The model not only places similar countries next to each other, but also arranges their capital cities in parallel. The most interesting part is that we didn’t provide any supervised information before or during training. Many more patterns like this arise automatically in training.This has a very broad range of potential applications: knowledge representation and extraction; machine translation; question answering; conversational systems; and many others. We’re open sourcing the code for computing these text representations efficiently (on even a single machine) so the research community can take these models further.
- QUOTE: Word2vec uses distributed representations of text to capture similarities among concepts. For example, it understands that Paris and France are related the same way Berlin and Germany are (capital and country), and not the same way Madrid and Italy are. This chart shows how well it can learn the concept of capital cities, just by reading lots of news articles -- with no human supervision:
2010
- (Turney & Pantel, 2010) ⇒ Peter D. Turney, and Patrick Pantel. (2010). “From Frequency to Meaning: Vector Space Models of Semantics.” In: Journal of Artificial Intelligence Research, 37(1).
- QUOTE: … This intimate connection between the distributional hypothesis and VSMs is a strong motivation for taking a close look at VSMs. Not all uses of vectors and matrices count as vector space models. For the purposes of this survey, we take it as a defining property of VSMs that the values of the elements in a VSM must be derived from event frequencies, such as the number of times that a given word appears in a given context (see Section 2.6).