[PDF] Top 20 Hierarchical Recurrent Neural Network for Document Modeling
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Hierarchical Recurrent Neural Network for Document Modeling
... a neural net- work model to predict discourse coherence qual- ity in ...work, recurrent (Sutskever et ...2013) neural networks are both examined to learn dis- tributed sentence representation given ... See full document
9
A Parallel Recurrent Neural Network for Language Modeling with POS Tags
... In recent years, there was an increasing number of research integrating knowledge into RNN. Mikolov and Zweig (2012) incorporated topic information as a feature layer into RNNLM. Ji et al. (2015) em- ployed the hidden ... See full document
8
Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks
... This is the only case where for one of the datasets (UoP) the probabilistic modeling outperforms RNN, as shown in Table 10 (for the TUC and LJMU datasets again RNN excels). One reason for this different result is ... See full document
10
Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network
... The accuracy of GRN reconstruction using synthetic gene expression data generated from the linear model are presented in Figs. 6, 7 and 8. These figures compare the Link F- score, Delay F-score and Effect F-score of the ... See full document
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Hierarchical Modeling of Global Context for Document Level Neural Machine Translation
... Recent years have witnessed a variety of ap- proaches proposed for document-level machine translation. Most of existing studies aim to im- prove overall translation quality with the aid of document context. ... See full document
10
Quantifying Uncertainties in Natural Language Processing Tasks
... language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but ... See full document
8
Improving Machine Translation Quality Estimation with Neural Network Features
... The recurrent neural network possesses sequen- tiality and memorability, and it performs well in sequential data ...rent Neural Network Language Model (RNNLM) (Mikolov et ... See full document
5
A hybrid input-type recurrent neural network for LVCSR language modeling
... information from multiple-type input units through a hybrid input vector of words and PMs, but can also capture long context history through recurrent connections. Several hybrid input representations were also ... See full document
12
Modeling long-term human activeness using recurrent neural networks for biometric data
... The recurrent behavior of RNNs has made them an effective solution for various tasks involving sequential data modeling: stock markets [20], energy consumption [21], genetic expression [22], speech [23], ... See full document
15
The Importance of Being Recurrent for Modeling Hierarchical Structure
... Recurrent neural networks (RNNs), in particu- lar Long Short-Term Memory networks (LSTMs), have become a dominant tool in natural language ...for modeling sequential data, recently a class of ... See full document
6
An Approach for Document Clustering using Agglomerative Clustering and Hebbian type Neural Network
... Hebbian-type neural network) and Repeated bisection and direct method of clustering from the point of dimension reduction of document space and ...compared neural networks and by the random ... See full document
6
Bidirectional Recurrent Convolutional Neural Network for Relation Classification
... on modeling the shortest de- pendency path (SDP) between two entities leveraging convolutional or recurrent neu- ral ...convolutional neural networks and two- channel recurrent neural ... See full document
10
Continuous Learning in a Hierarchical Multiscale Neural Network
... language modeling has been shown to be an ad- equate proxy for learning unsupervised represen- tations of high-quality in tasks like text classifica- tion (Howard and Ruder, 2018), sentiment detec- tion (Radford ... See full document
7
Recurrent Neural Network Grammars
... discriminative modeling as well, where sequences of transitions are modeled conditional on the full input sentence along with the incrementally constructed syntactic ... See full document
11
Duration Modeling For Telugu Language with Recurrent Neural Network
... duration modeling and more relevant work is briefly explained ...proposed Recurrent Fuzzy Neural Network (RFNN) can generate proper prosodic features including pitch means, pitch shapes, ... See full document
6
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
... Recently, neural network e- merges as an effective way to learn continuous text representation for sentiment ...sive neural networks for sentence-level semantic composition. Recursive neural ... See full document
11
Enhancing recurrent neural network-based language models by word tokenization
... modified recurrent neural network-based language model for language ...the network input into three ...basic recurrent neural network ... See full document
13
Hierarchical Attention Networks for Document Classification
... convolutional neural net- works (Blunsom et al., 2014) and recurrent neural networks based on long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) to learn text ... See full document
10
Hierarchical Context Enabled Recurrent Neural Network for Recommendation
... three hierarchical con- texts of the global, the local, and the temporary ...a hierarchical context-based gate structure to incorporate our interest drift ... See full document
9
A Dual Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
... convolutional neural net- work ...variable recurrent neural network was developed for jointly modelling sequences of words and discourse relations between adjacent sentences (Ji et ... See full document
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