Segment Information Extraction from Financial Annual Reports Using Neural Network

2020 ◽  
pp. 215-226
Author(s):  
Tomoki Ito ◽  
Hiroki Sakaji ◽  
Kiyoshi Izumi
Keyword(s):  
Neural Network ◽  
Information Extraction ◽  
Annual Reports ◽  
Segment Information

Graded information extraction by neural-network dynamics with multihysteretic neurons

2009 ◽  
Vol 22 (7) ◽  
pp. 922-930 ◽  
Author(s):  
Yukihiro Tsuboshita ◽  
Hiroshi Okamoto
Keyword(s):  
Neural Network ◽  
Information Extraction ◽  
Network Dynamics ◽  
Neural Network Dynamics

scholarly journals Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Moyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Information Extraction ◽  
Spatial Scale ◽  
Quantitative Evaluation ◽  
Urban Landscape ◽  
Kappa Coefficient ◽  
Deep Convolutional Neural Network ◽  
Landscape Information ◽  
Extraction Model

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

scholarly journals Hierarchical attention networks for information extraction from cancer pathology reports

2017 ◽  
Vol 25 (3) ◽  
pp. 321-330 ◽  
Author(s):  
Shang Gao ◽  
Michael T Young ◽  
John X Qiu ◽  
Hong-Jun Yoon ◽  
James B Christian ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Information Extraction ◽  
Model Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Attention Networks ◽  
Cancer Pathology ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

Abstract Objective We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1–G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. Results Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macroF-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). Conclusions HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

The Use Of CAMELS In Detecting Financial Distress Of Islamic Banks In Malaysia

2014 ◽  
Vol 30 (2) ◽  
pp. 445 ◽  
Author(s):  
Rashidah Abdul Rahman ◽  
Mazni Yanti Masngut
Keyword(s):  
Neural Network ◽  
Quality Management ◽  
Financial Distress ◽  
Capital Adequacy ◽  
Advisory Council ◽  
Annual Reports ◽  
Islamic Banks ◽  
Return On Assets ◽  
Management Quality ◽  
Asset Quality

The current study uses CAMEL (Capital Adequacy, Asset Quality, Management Quality, Earnings Efficiency, and Liquidity) ratings system, with the addition of Shariah Compliance Ratio (CAMELS) in order to detect the financial distress of Islamic banks in Malaysia. Using neural network, the study analyses data collected from the 17 Islamic banks annual reports for the period 2006 to 2010. It was found that all Islamic banks have higher ETA ratios which portray a good performance of capital adequacy and are less likely to face financial distress. As for asset quality, all Islamic banks did not have the possibility to face financial distress as they are able to handle their non-performing loans throughout the years. Meanwhile for management quality, all Islamic banks show lower ratios in paying salaries to their employee. Earning efficiency for all Islamic banks show better performance and will be less likely to face financial distress in terms of return on assets but not for return of equity. Liquidity indicates that the Islamic banks have a large number of loans but they have sufficient liquid assets in order to cover their liabilities and commitments. Lastly for Shariah Compliance, Islamic banks have complied with all rules and regulations that have been regulated by Bank Negara Malaysias Shariah Advisory Council.

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