Large-Scale Network Involvement in Language Processing

Large-scale network involvement in language processing.

Large Scale Hierarchical Neural Network Language Models

Feed-forward neural network language models (NNLMs) are known to improve both perplexity and word error rate performance for speech recognition compared with conventional ngram language models. We present experimental results showing how much the WER can be improved by increasing the scale of the NNLM, in terms of model size and training data. However, training time can become very long. We imp...

Large-scale neural network for sentence processing.

Our model of sentence comprehension includes at least grammatical processes important for structure-building, and executive resources such as working memory that support these grammatical processes. We hypothesized that a core network of brain regions supports grammatical processes, and that additional brain regions are activated depending on the working memory demands associated with processin...

Curbing variations in packaging process through Six Sigma way in a large-scale food-processing industry

Indian industries need overall operational excellence for sustainable profitability and growth in the present age of global competitiveness. Among different quality and productivity improvement techniques, Six Sigma has emerged as one of the most effective breakthrough improvement strategies. Though Indian industries are exploring this improvement methodology to their advantage and reaping the ...

Large - Scale Semi - Supervised Learning for Natural Language Processing

Natural Language Processing (NLP) develops computational approaches to processing language data. Supervised machine learning has become the dominant methodology of modern NLP. The performance of a supervised NLP system crucially depends on the amount of data available for training. In the standard supervised framework, if a sequence of words was not encountered in the training set, the system c...