Sentence examples for neural language from inspiring English sources

The brain can do this because it "speaks" in the same neural "language" of electrochemical signals regardless of what kinds of environmental stimuli are interacting with the body's sense organs.

Collobert et al. [22] used a neural language model to initialize their word representations.

As already mentioned, we generated representations with HAL, a neural language model (NLM) and skip-grams (SG).

The neural language model vectors were obtained after around 10 days running on a 2.7 GHz Intel Xeon processor in a Linux server.

The prediction is based on the neural language model introduced in [10], which assigns a score to each word in the vocabulary.

The neural language model is clearly the best suited for the task, being especially useful for providing knowledge about out-of-vocabulary words.

The recurrent neural network language model (RNNLM) has shown significant promise for statistical language modeling.

The proposed model is tested with the English AMI speech recognition dataset and outperforms the baseline n-gram model, the basic recurrent neural network language models (RNNLM) and the GPU-based recurrent neural network language models (CUED-RNNLM) in perplexity and word error rate.

With languages that have a rich morphological system and a huge number of vocabulary words, the major trade-off with neural network language models is the size of the network.

This paper presents a recurrent neural network language model based on the tokenization of words into three parts: the prefix, the stem, and the suffix.

neural network language model.