A C-BiLSTM Approach to Classify Construction Accident Reports

[1]  Peng Wang,et al.  Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification , 2016, Neurocomputing.

[2]  Zhipeng Zhou,et al.  Overview and analysis of safety management studies in the construction industry , 2015 .

[3]  S Leclercq,et al.  Extracting recurrent scenarios from narrative texts using a Bayesian network: application to serious occupational accidents with movement disturbance. , 2014, Accident; analysis and prevention.

[4]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[5]  John A. Gambatese,et al.  Activity-Based Safety Risk Quantification for Concrete Formwork Construction , 2009 .

[6]  Rafael Sacks,et al.  Spatial and Temporal Exposure to Safety Hazards in Construction , 2009 .

[7]  Jimmie Hinze,et al.  Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system , 2010 .

[8]  Aviad Shapira,et al.  Identification and Analysis of Factors Affecting Safety on Construction Sites with Tower Cranes , 2009 .

[9]  Mark R Lehto,et al.  Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review. , 2017, Accident; analysis and prevention.

[10]  Shan Liu,et al.  An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm , 2016, Expert Syst. Appl..

[11]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[12]  Moses O. Tadé,et al.  A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models , 2012 .

[13]  Matthew R. Hallowell,et al.  Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports , 2016 .

[14]  Hasan Fleyeh,et al.  Construction site accident analysis using text mining and natural language processing techniques , 2019, Automation in Construction.

[15]  Matthew R. Hallowell,et al.  Application of machine learning to construction injury prediction , 2016 .

[16]  Mumtaz Usmen,et al.  Comparative Injury and Fatality Risk Analysis of Building Trades , 2006 .

[17]  Hongfei Lin,et al.  A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification , 2017, Inf..

[18]  Igor Kononenko,et al.  Semi-Naive Bayesian Classifier , 1991, EWSL.

[19]  Peter E.D. Love,et al.  Convolutional neural network: Deep learning-based classification of building quality problems , 2019, Adv. Eng. Informatics.

[20]  H. M. Al-Humaidi,et al.  Construction Safety in Kuwait , 2010 .

[21]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[22]  Yang Miang Goh,et al.  Construction accident narrative classification: An evaluation of text mining techniques. , 2017, Accident; analysis and prevention.

[23]  Ludovic Tanguy,et al.  Natural language processing for aviation safety reports: From classification to interactive analysis , 2016, Comput. Ind..

[24]  Yang Miang Goh,et al.  Incident Causation Model for Improving Feedback of Safety Knowledge , 2004 .

[25]  Yang Liu,et al.  A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm , 2017, Inf. Sci..

[26]  Ye Duan,et al.  A multi-view recurrent neural network for 3D mesh segmentation , 2017, Comput. Graph..

[27]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[28]  S.J. Bertke,et al.  Development and evaluation of a Naïve Bayesian model for coding causation of workers' compensation claims. , 2012, Journal of safety research.

[29]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[30]  TanguyLudovic,et al.  Natural language processing for aviation safety reports , 2016 .

[31]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[32]  Matthew R. Hallowell,et al.  Diffusion of Safety Innovations in the Construction Industry , 2012 .

[33]  Gang Liu,et al.  Bidirectional LSTM with attention mechanism and convolutional layer for text classification , 2019, Neurocomputing.

[34]  Fredric C. Gey,et al.  The relationship between recall and precision , 1994 .

[35]  Jie Gong,et al.  Predicting construction cost overruns using text mining, numerical data and ensemble classifiers , 2014 .