Resurgence of Deep Learning: Genesis of Word Embedding

2018 ◽  
pp. 129-139
Author(s):  
Vimal Kumar Soni ◽  
Dinesh Gopalani ◽  
M. C. Govil
Keyword(s):  
Deep Learning ◽  
Word Embedding

scholarly journals WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning

2019 ◽  
Vol 7 (4) ◽  
pp. 293-301 ◽  
Author(s):  
Xiaofeng Fu ◽  
Yang Yang
Keyword(s):  
Deep Learning ◽  
Word Embedding ◽  
Type Iii

A Position Weighted Information Based Word Embedding Model for Machine Translation

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040005
Author(s):  
Zhen Li ◽  
Dan Qu ◽  
Yanxia Li ◽  
Chaojie Xie ◽  
Qi Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Semantic Information ◽  
Word Embedding ◽  
Vector Model ◽  
Learning Technology ◽  
Initial Value ◽  
Input Layer ◽  
End To End

Deep learning technology promotes the development of neural network machine translation (NMT). End-to-End (E2E) has become the mainstream in NMT. It uses word vectors as the initial value of the input layer. The effect of word vector model directly affects the accuracy of E2E-NMT. Researchers have proposed many approaches to learn word representations and have achieved significant results. However, the drawbacks of these methods still limit the performance of E2E-NMT systems. This paper focuses on the word embedding technology and proposes the PW-CBOW word vector model which can present better semantic information. We apply these word vector models on IWSLT14 German-English, WMT14 English-German, WMT14 English-French corporas. The results evaluate the performance of the PW-CBOW model. In the latest E2E-NMT systems, the PW-CBOW word vector model can improve the performance.

scholarly journals An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level

2019 ◽  
Vol 9 (16) ◽  
pp. 3414 ◽  
Author(s):  
Ren-Hung Hwang ◽  
Min-Chun Peng ◽  
Van-Linh Nguyen ◽  
Yu-Lun Chang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Technologies ◽  
Word Embedding ◽  
Detection Systems ◽  
Real Time Analysis ◽  
Detection Delay ◽  
Novel Approach ◽  
Prior Literature

Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we propose a novel word embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and for further classifying whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, IoT dataset from Robert Gordon University and IoT dataset collected on our Mirai Botnet show that our approach is competitive to the prior literature which detects malicious traffic at the flow level. While the network traffic is booming year by year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay.

scholarly journals US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1553
Author(s):  
Harun Yasar ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Word Embedding ◽  
Learning Models ◽  
Exchange Rate Forecasting ◽  
Us Dollar ◽  
Turkish Lira ◽  
Embedding Methods

Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

scholarly journals Abstractive Text Summary of COVID-19 Documents based on LSTM Method and Word Embedding

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1011-1022
Author(s):  
Saja Naeem Turky ◽  
Ahmed Sabah Ahmed AL-Jumaili ◽  
Rajaa K. Hasoun
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Input Sequence ◽  
Word Embedding ◽  
Suggested Model ◽  
Specific Domain ◽  
Scientific Texts ◽  
Proposed Model ◽  
Pass Through ◽  
Single Statement

An abstractive summary is a process of producing a brief and coherent summary that contains the original text's main concepts. In scientific texts, summarization has generally been restricted to extractive techniques. Abstractive methods that use deep learning have proven very effective in summarizing articles in public fields, like news documents. Because of the difficulty of the neural frameworks for learning specific domain- knowledge especially in NLP task, they haven't been more applied to documents that are related to a particular domain such as the medical domain. In this study, an abstractive summary is proposed. The proposed system is applied to the COVID-19 dataset which a collection of science documents linked to the coronavirus and associated illnesses, in this work 12000 samples from this dataset have been used. The suggested model is an abstractive summary model that can read abstracts of Covid-19 papers then create summaries in the style of a single-statement headline. A text summary model has been designed based on the LSTM method architecture. The proposed model includes using a glove model for word embedding which is converts input sequence to vector forms, then these vectors pass through LSTM layers to produce the summary. The results indicate that using an LSTM and glove model for word embedding together improves the summarization system's performance. This system was evaluated by rouge metrics and it achieved (43.6, 36.7, 43.6) for Rouge-1, Rouge-2, and Rouge-L respectively.

scholarly journals Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 127913-127928 ◽  
Author(s):  
Huda Abdulrahman Almuzaini ◽  
Aqil M. Azmi
Keyword(s):  
Deep Learning ◽  
Text Categorization ◽  
Word Embedding ◽  
Arabic Text

scholarly journals Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

2018 ◽  
Vol 12 (4) ◽  
pp. 1099-1117 ◽  
Author(s):  
Yi Zhang ◽  
Jie Lu ◽  
Feng Liu ◽  
Qian Liu ◽  
Alan Porter ◽  
...  
Keyword(s):  
Deep Learning ◽  
Word Embedding ◽  
Clustering Method ◽  
Topic Extraction
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