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Progress in Deep Learning Mechanisms for Information Extraction Social Networks: An Expository Overview

Israel Fianyi, Gifty Andoh Appiah
2021 International Journal of Computer Applications  
Deep learning algorithms have shown to be robust in extracting high quality information from a wide range of online platforms.  ...  The study considers relevant published articles from the year 2009-2020 that focused on deep learning approach for information extraction from text.  ...  In the next section, we discuss recent trends (deep learning methods) for information extraction.  ... 
doi:10.5120/ijca2021921155 fatcat:qzneg7npn5d5hocznq53oyek6y

Deep learning applications and challenges in big data analytics

Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic
2015 Journal of Big Data  
Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such  ...  Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.  ...  These discriminative results with Deep Learning are useful for data tagging and information retrieval and can be used in search engines.  ... 
doi:10.1186/s40537-014-0007-7 fatcat:65mi6dnv5rg6poesotupqbsm7y

A Novel Method of Hyperspectral Data Classification Based on Transfer Learning and Deep Belief Network

Ke Li, Mingju Wang, Yixin Liu, Nan Yu, Wei Lan
2019 Applied Sciences  
Then, we use the transfer learning method to find the common features of the source domain data and target domain data.  ...  Second, we propose a model structure that combines the deep transfer learning model to utilize a combination of spatial information and spectral information.  ...  The spectral information obtained in the previous part is combined with the spatial features into the fused feature We use the source domain data for the deep learning network training, and we use the  ... 
doi:10.3390/app9071379 fatcat:vldpks4iu5atvds4d6lclkf4gy

A short review on Applications of Deep learning for Cyber security [article]

Mohammed Harun Babu R, Vinayakumar R, Soman KP
2019 arXiv   pre-print
Deep learning is an advanced model of traditional machine learning. This has the capability to extract optimal feature representation from raw input samples.  ...  This paper outlines the survey of all the works related to deep learning based solutions for various cyber security use cases.  ...  In [17] comes up with semantic information extraction from system call sequence method using NLP which helps in construction of deep learning model.  ... 
arXiv:1812.06292v2 fatcat:o7pcaf7xyncrpdn64byjxh47im

An overview of event extraction and its applications [article]

Jiangwei Liu, Liangyu Min, Xiaohong Huang
2021 arXiv   pre-print
This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction.  ...  As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language.  ...  Closed Domain Event Extraction This section categorizes closed-domain event extraction approaches into pattern matching, machine learning, deep learning, and semi-supervised learning methods.  ... 
arXiv:2111.03212v1 fatcat:o3oagnjrybh3vapvvp7twgjtuu

How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction

Jefferson A. Peña-Torres, Raúl E. Gutiérrez, Víctor A. Bucheli, Fabio A. González
2019 Tecno Lógicas  
We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains?  ...  The model should be adaptable to automatically extract relationships across different domains using the DL network.  ...  Table 1 . 1 Survey of studies into RE tasks using Deep Learning approaches. Source: Created by the authors.  ... 
doi:10.22430/22565337.1483 fatcat:c3dqq2hx6ngqvccnc74eemi2eq

An overview of information extraction techniques for legal document analysis and processing

Ashwini V. Zadgaonkar, Avinash J. Agrawal
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches.  ...  Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents.  ...  Chalkidis and Kampas [12] in a survey discusses applications of deep learning for processing legal text-based of three different NLP tasks namely text classification, information extraction, and information  ... 
doi:10.11591/ijece.v11i6.pp5450-5457 fatcat:cigtd4kh4vc4hhfnl32sss25ye

Enhancing RF Sensing with Deep Learning: A Layered Approach

Tianyue Zheng, Zhe Chen, Shuya Ding, Jun Luo
2021 IEEE Communications Magazine  
One potential solution leverages deep learning to build direct mappings from the RF domain to target domains, hence avoiding complex RF physical modeling.  ...  To better understand this potential, this article takes a layered approach to summarize RF sensing enabled by deep learning.  ...  Therefore, deep learning comes into play: by fully exploring RF data and extracting deep features, irrelevant information can be stripped while useful spatial features retained [3] , [9] , and localization  ... 
doi:10.1109/mcom.001.2000288 fatcat:zwth73kis5aqxh3vfagyjxsmsm

Efficient Deep Learning Models for DGA Domain Detection

Juhong Namgung, Siwoon Son, Yang-Sae Moon, Savio Sciancalepore
2021 Security and Communication Networks  
Recently, long short-term memory- (LSTM-) based deep learning models have been introduced to detect DGA domains in real time using only domain names without feature extraction or additional information  ...  In this paper, we propose an efficient DGA domain detection method based on bidirectional LSTM (BiLSTM), which learns bidirectional information as opposed to unidirectional information learned by LSTM.  ...  Deep Learning Model. e deep learning models presented in Section 4 detect the DGA domains from the embedded domain names.  ... 
doi:10.1155/2021/8887881 fatcat:ftf47pxdxnhc7je6hyzttplnmy

Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach [article]

Yizhi Liu, Fang Yu Lin, Mohammadreza Ebrahimi, Weifeng Li, Hsinchun Chen
2021 arXiv   pre-print
While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency  ...  In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations.  ...  Deep Learning for Information Extraction (IE) Information Extraction (IE) is a process of extracting target information (e.g., location, name, birthdate) from unstructured textual data automatically [  ... 
arXiv:2111.09415v1 fatcat:5carl4zvszebdc2pi7xb6uttkm

Explicit-implicit dual stream network for image quality assessment

Guangyi Yang, Xingyu Ding, Tian Huang, Kun Cheng, Weizheng Jin
2020 EURASIP Journal on Image and Video Processing  
Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features.  ...  We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features  ...  To ensure the sufficient extraction of information, most researchers use deep networks to automatically learn features from big data.  ... 
doi:10.1186/s13640-020-00538-y fatcat:metfzfhnynaffac2pvhualto3y

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research  ...  Deep Learning Deep learning refers to "a class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction and transformation  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Radio Frequency Fingerprint identification Based on Deep Complex Residual Network

Shenhua Wang, Hongliang Jiang, Xiaofang Fang, Yulong Ying, Jingchao Li, Bin Zhang
2020 IEEE Access  
The deep learning method can also be used to directly process the signal to be recognized.  ...  The waveform domain method uses the time-domain waveform of the signal to be identified to extract features, and uses the fractal dimension of the waveform and the duration of the transient signal as fingerprint  ... 
doi:10.1109/access.2020.3037206 fatcat:w6db6j47und23mvx3sfq3qzwyy

Dual-input Neural Network Integrating Feature Extraction and Deep Learning for Coronary Artery Disease Detection Using Electrocardiogram and Phonocardiogram

Han Li, Xinpei Wang, Changchun Liu, Yan Wang, Peng Li, Hong Tang, Lianke Yao, Huan Zhang
2019 IEEE Access  
First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features.  ...  To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed.  ...  If multi-domain feature extraction can be combined with deep learning, sufficient information can be provided for the classification task, and better results are likely to be achieved.  ... 
doi:10.1109/access.2019.2943197 fatcat:oo5iyuu6azg25ovpvg7jsw7hnm

Altered Fingerprints Detection Based on Deep Feature Fusion

Chao XU, Yunfeng YAN, Lehangyu YANG, Sheng LI, Guorui FENG
2022 IEICE transactions on information and systems  
After the extraction network, a feature fusion module is then used to exploit relationship of two network features.  ...  The method is constructed by two deep convolutional neural networks to train the time-domain and frequency-domain features. A spectral attention module is added to connect two networks.  ...  Experimental Settings We use PyTorch deep learning framework for experiments.  ... 
doi:10.1587/transinf.2022edl8028 fatcat:vhyayg4g2faena7zyu245c5u2e
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