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• W2131462252 abstract "Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs is their extremely long training and testing times. Morin and Bengio have proposed a hierarchical language model built around a binary tree of words, which was two orders of magnitude faster than the non-hierarchical model it was based on. However, it performed considerably worse than its non-hierarchical counterpart in spite of using a word tree created using expert knowledge. We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data. We then show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models." @default.
• W2131462252 created "2016-06-24" @default.
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• W2131462252 date "2008-12-08" @default.
• W2131462252 modified "2023-09-30" @default.
• W2131462252 title "A Scalable Hierarchical Distributed Language Model" @default.
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