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The evaluation technology of water quality is of great significance for monitoring and management of surface water quality. In this paper, the Extreme Learning Machine algorithm was optimized with the Dolphin Swarm Algorithm. Optimal weight and threshold of Extreme Learning Machine algorithm was searched by the process of creating a virtual team and seeking the best position of Dolphin Swarm. Four...
In this paper, we propose a recurrent transductive support vector machine (rtsvm) for semi-supervised slot tagging. Taking advantage of the superior sequence representation capability of recurrent neural networks (rnns) and the semi-supervised learning capability of transductive support vector machines (tsvms), the rtsvm is stacking a tsvm on top of a rnn. The performance of the traditional tsvm is...
We present a contextual spoken language understanding (contextual SLU) method using Recurrent Neural Networks (RNNs). Previous work has shown that context information, specifically the previously estimated domain assignment, is helpful for domain identification. We further show that other context information such as the previously estimated intent and slot labels are useful for both intent classification...
In many spoken language understanding systems (SLUS), domain classification is the most crucial component, as system responses based on wrong domains often yield very unpleasant user experiences. In multi-lingual domain classification, the training data for some poor-resource languages often comes from machine translation. Some of the higher order n-gram features are distorted during machine translation...
Neural network based approaches have recently produced record-setting performances in natural language understanding tasks such as word labeling. In the word labeling task, a tagger is used to assign a label to each word in an input sequence. Specifically, simple recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have shown to significantly outperform the previous state-of-the-art...
Conventional n-gram language models are known for their limited ability to capture long-distance dependencies and their brittleness with respect to within-domain variations. In this paper, we propose a k-component recurrent neural network language model using curriculum learning (CL-KRNNLM) to address within-domain variations. Based on a Dutch-language corpus, we investigate three methods of curriculum...
We propose a dynamic Bayesian classifier for the socio-situational setting of a conversation. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models in speech recognition. The dynamic Bayesian classifier has the advantage - compared to static classifiers such a naive Bayes and support vector machines - that...
We present a method for automatic classification of the socio-situational setting of a conversation based on the language used. The socio-situational setting depicts the social background of a conversation which involves the communicative goals, number of speakers, number of listeners and the relationship among the speakers and the listeners. Knowledge of the socio-situational setting can be used...
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