A neural probabilistic language model
The Journal of Machine Learning Research
Continuous space language models
Computer Speech and Language
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Linear Relational Embedding (LRE) was introduced (Paccanaro and Hinton, 1999) as a means of extracting a distributed representation of concepts from relational data. The original formulation cannot use negative information and cannot properly handle data in which there are multiple correct answers. In this paper, we propose an extended formulation of LRE that solves both these problems. We present results in two simple domains, which show that learning leads to good generalization.