The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Three new graphical models for statistical language modelling
Abstract
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficulties caused by data sparsity. We propose three new probabilistic language models that define the distribution of the next word in a sequence given several preceding words by using distributed representations of those words. We show how real-valued distributed representations for words can be learned at the same time as learning a large set of stochastic binary hidden features that are used to predict the distributed representation of the next word from previous distributed representations. Adding connections from the previous states of the binary hidden features improves performance as does adding direct connections between the real-valued distributed representations. One of our models significantly outperforms the very best n-gram models.
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Citations
Cites
A fast learning algorithm for deep belief nets
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Self-citationTraining Products of Experts by Minimizing Contrastive Divergence
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BackgroundInfluentialSelf-citation
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Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
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BackgroundMethodInfluentialA Fast and Simple Algorithm for Training Neural Probabilistic Language Models
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Language Models with Transformers
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Metadata
- Authors
- Mnih, A., & Hinton, G.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 641-648
- OpenAlex
- W2091812280
- Cited by
- 563
- References
- 2
- FWCI
- 3.47
- Type
- conference paper
- Language
- en
- Open access
- closed
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