Software system development is guided by the evolution of requirements. In this paper, we address the task of requirements traceability, which is concerned with providing bi-directional traceability between various re...
The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural language Processing(NLP)*** Arabic language has a lot of *** ins...
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The text classification process has been extensively investigated in various languages,especially *** classification models are vital in several Natural language Processing(NLP)*** Arabic language has a lot of *** instance,it is the fourth mostly-used language on the internet and the sixth official language of ***,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic *** general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the *** this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is *** major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic *** first step in the proposed model is to pre-process the input data to transform it into a useful *** Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature ***,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic *** the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s *** proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (...
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Hybrid deep neural network hidden Markov models (DNN-HMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNN-HMM models are not e...
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This paper presents a MapReduce algorithm for computing pairwise document similarity in large document collections. MapReduce is an attractive framework because it allows us to decompose the inner products involved in...
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Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre-trained on potentially sensitive dat...
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Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such...
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This paper investigates the combination of different short-term features and the combination of recurrent and non-recurrent neural networks (NNs) on a Spanish speech recognition task. Several methods exist to combine ...
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ISBN:
(纸本)9781479903573
This paper investigates the combination of different short-term features and the combination of recurrent and non-recurrent neural networks (NNs) on a Spanish speech recognition task. Several methods exist to combine different feature sets such as concatenation or linear discriminant analysis (LDA). Even though all these techniques achieve reasonable improvements, feature combination by multi-layer perceptrons (MLPs) outperforms all known approaches. We develop the concept of MLP based feature combination further using recurrent neural networks (RNNs). The phoneme posterior estimates derived from an RNN lead to a significant improvement over the result of the MLPs and achieve a 5% relative better word error rate (WER) with much less parameters. Moreover, we improve the system performance further by combining an MLP and an RNN in a hierarchical framework. The MLP benefits from the preprocessing of the RNN. All NNs are trained on phonemes. Nevertheless, the same concepts could be applied using context-dependent states. In addition to the improvements in recognition performance w.r.t. WER, NN based feature combination methods reduce both, the training and the testing complexity. Overall, the systems are based on a single set of acoustic models, together with the training of different NNs.
We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular question, and a possible solution argument, ...
Sentiment analysis often relies on a semantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lexicons, but they tend to be computationally expensive, an...
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