[PDF] Top 20 Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network
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Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network
... Table 3 presents running time with different meth- ods to train word representations on 536 million words corpus. BLSTM-RNN is trained on one NVIDIA Tesla M2090 GPU. The other three meth- ods are trained on ... See full document
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Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
... Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network Peilu Wang, Yao Qian, Frank K.. Soong, Lei He and Hai Zhao.[r] ... See full document
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Using Bidirectional, GRU and LSTM Neural Network methods for Multi Currency Exchange Rates Prediction
... profound learning models which incorporate top remote trade (Forex) monetary ...the Recurrent Neural Network models using Bidirectional RNN, Gated Recurrent Unit (GRU) and Long ... See full document
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Text classification based on conditional reflection
... deep learning, the distributed representation of words solves the sparsity problem ...of word vectors, neural networks have made great progress in text ...Convolutional Neural Networks ... See full document
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An Investigation of Recurrent Neural Architectures for Drug Name Recognition
... The LSTM was de- signed to overcome this limitation by incorporating a gated memory-cell to capture long-range depen- dencies within the data (Hochreiter and Schmidhu- ber, ...the bidirectional LSTM, ... See full document
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Bidirectional Recurrent Convolutional Neural Network for Relation Classification
... or recurrent neu- ral ...convolutional neural networks and two- channel recurrent neural networks with long short term memory (LSTM) ...a bidirectional architecture to learn ... See full document
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Unified Framework For Deep Learning Based Text Classification
... into learning features which are used for training the ...text representations such as term frequency & inverse document frequency (TFIDF), bag of words, n-grams, character level representation etc ... See full document
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Abstractive Compression of Captions with Attentive Recurrent Neural Networks
... in neural machine transla- tion, we select a lower number of units than in this earlier work, namely 512 instead of 1024 (Sutskever et ...dimensional word embeddings are jointly learned during training. We ... See full document
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Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
... deep learning-based architectures, which can automatically learn representations of data at multiple levels of ab- straction, have been proposed and have demonstrated successes in multiple domains including ... See full document
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Word Sense Disambiguation using a Bidirectional LSTM
... of recurrent neural network ...The bidirectional variant of LSTM, (BLSTM) (Graves and Schmidhuber, 2005) is an adaptation of the LSTM where the state at each time step consist of ... See full document
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Recurrent Neural Network with Word Embedding for Complaint Classification
... the word embedding used for complaint classification which combine with recurrent neural network LSTM and GRU with a single direction and also ...of bidirectional LSTM- ... See full document
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Gated Word Character Recurrent Language Model
... The bidirectional LSTMs have 200 hidden units, and their weights are initialized with Xavier ...the LSTM language model are also initialized with Xavier ...A learning rate is fixed at 0.2. ... See full document
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Improving Machine Translation Quality Estimation with Neural Network Features
... and traditional methods, such as QuEst (Specia et al., 2013), extract linguistically motivated fea- tures to improve the correlation between the au- tomatic QE and human assessment. However, ex- tracting linguistically ... See full document
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NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT BiLSTM Attention Model
... BiLSTM network with attention mechanism is integrated for textual ...Each word refers to distributed vectors from a pre- trained BERT to form as an embedding ...BiLSTM network with attention ... See full document
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Sense Aware Neural Models for Pun Location in Texts
... a word) suggests two or more meanings by exploit- ing polysemy for an intended humorous or rhetorical ...pun word in a given short ...rectional LSTM network to model each se- quence of ... See full document
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Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
... our neural network on its SDP extracted from ...SDP, recurrent neural net- works are applied to learn hidden representations of words and dependency relations, ...hidden ... See full document
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Deep Learning Based Crime Investigation Framework
... used against individuals to control the restlessness in the community or to stop a protest against the government. Similar types of crime also happen in states without this act. They may be registered under different ... See full document
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List of Deep Learning Models
... deep learning [1-8]. Deep learning methods very fast emerged and expanded applications in various scientific and engineer- ing ...machine learning algo- rithms, e.g., [9-26], indicates that deep ... See full document
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Application of Artificial Intelligence for Epilepsy Disease
... For measure the EEG put the electrodes on the cranium of the tolerant here they collect electrical scheme of the cerebrum [13] [14]. That scheme consequences from stimulant neurons ejecting activity powers. At some ... See full document
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Neural Models of Factuality
... on neural mod- els in the closely related domain of generic- ity/habituality prediction suggests that inclusion of hand-annotated lexical features can improve clas- sification performance (Becker et ...input ... See full document
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