Journal ArticleDOI
Bidirectional LSTM with attention mechanism and convolutional layer for text classification
Gang Liu,Jiabao Guo +1 more
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TLDR
A novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper, which outperforms other state-of-the-art text classification methods in terms of the classification accuracy.About:
This article is published in Neurocomputing.The article was published on 2019-04-14. It has received 581 citations till now. The article focuses on the topics: Word embedding & Recurrent neural network.read more
Citations
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A review on the attention mechanism of deep learning
TL;DR: An overview of the state-of-the-art attention models proposed in recent years is given and a unified model that is suitable for most attention structures is defined.
Journal ArticleDOI
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
TL;DR: An Attention-based Bidirectional CNN-RNN Deep Model (ABCDM) is proposed that achieves state-of-the-art results on both long review and short tweet polarity classification and is evaluated on sentiment polarity detection.
Synthesis Lectures on Human Language Technologies
Ido Dagan,Dan Roth,Mark Sammons,Fabio Massimo Zanzotto,Web Corpus Construction,Roland Schäfer,Felix Bildhauer +6 more
TL;DR: This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems and presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research.
Journal ArticleDOI
CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.
TL;DR: Two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images and it is proved that the proposed architecture shows outstanding success in infection detection.
Journal ArticleDOI
Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism
TL;DR: An attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism is proposed that produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or L STM models as the hybrid models.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.