On-site text classification and knowledge mining for large-scale projects construction by integrated intelligent approach

2021 ◽  
Vol 49 ◽  
pp. 101355
Author(s):  
Dan Tian ◽  
Mingchao Li ◽  
Jonathan Shi ◽  
Yang Shen ◽  
Shuai Han
Keyword(s):  
Text Classification ◽  
Large Scale ◽  
Knowledge Mining ◽  
Intelligent Approach

scholarly journals CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Aboubakar Nasser Samatin Njikam ◽  
Huan Zhao
Keyword(s):  
Text Classification ◽  
Building Block ◽  
Large Scale ◽  
State Of The Art ◽  
Building Blocks ◽  
Training Data ◽  
Superior Performance ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Convolutional Network

This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems. This new architecture is composed of four building blocks for feature extraction. Each of these building blocks, except the last one, uses 1 × 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block. In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block. Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net’s superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.

scholarly journals Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 30885-30896 ◽  
Author(s):  
Jibing Gong ◽  
Hongyuan Ma ◽  
Zhiyong Teng ◽  
Qi Teng ◽  
Hekai Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Large Scale ◽  
Learning Model ◽  
Hierarchical Graph ◽  
Deep Learning Model

Architecture and Key Technologies of Intelligent Dispatching Based on Situational Awareness

2013 ◽  
Vol 448-453 ◽  
pp. 2259-2265
Author(s):  
Sheng Chun Yang ◽  
Bi Qiang Tang ◽  
Jian Guo Yao ◽  
Feng Li ◽  
Yi Jun Yu ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Analysis ◽  
Human Computer Interaction ◽  
Early Warning ◽  
Large Scale ◽  
Situational Awareness ◽  
Power Grid ◽  
Operating Conditions ◽  
Knowledge Mining ◽  
Grid Operation

With the construction of UHV power grid, integration of large-scale renewable clean energy, and large-scale energy base putting into operation, the power grid dispatching faced with more and more complex challenges. On the basis of existing research results, architecture of intelligent dispatching based on situation awareness is proposed, so as to accurately achieve prevention and control of the power system. The shortcomings of traditional dispatching mode are analyzed firstly, and the concepts and characterization approaches of grid situational awareness and operation state trajectory of power grid are then introduced. The overall objective of intelligent dispatching is presented, including data processing and integrated knowledge mining, predictive perception of grid operation, risk analysis and comprehensive early warning, so as to achieve "automatic cruise under normal operating conditions, automatic navigation under abnormal operating conditions ". The functional framework of intelligent dispatching is also proposed in details, including four major aspects of the perception and forecasts, risk analysis, decision-making support, and automatic control, as well as three supporting functions such as post-assessment of dispatching, trajectory index calculation, and human-computer interaction (HCI).Technical innovations to support automatic intelligent dispatching are discussed and organised in three levels, i.e. perception, comprehension and projection. The breakthroughs are: construction of index system, trajectory recognition based on massive information and knowledge mining, trajectory projection taking into accounts the uncertainties, online risk assessment and early warning, power grid intelligent decision-making support, automatic coordination of grid operation control, online assessment, natural human-computer interaction mode, and etc... These are the future research areas of automatic intelligent dispatching.

scholarly journals A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

2019 ◽  
Author(s):  
Yair Fogel-Dror ◽  
Shaul R. Shenhav ◽  
Tamir Sheafer
Keyword(s):  
Content Analysis ◽  
Deep Learning ◽  
Text Classification ◽  
Large Scale ◽  
Low Cost ◽  
Initial Number ◽  
Analysis Method ◽  
Training Set ◽  
Topic Analysis ◽  
Weakly Supervised

The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.

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