Classification of construction accident court cases via natural language processing in Hong Kong

Abstract

Construction accidents often lead to injuries, pain, loss of future earnings and even deaths. One way to lower the likelihood of accidents is to levy high compensation on wrongdoers, including main contractors, subcontractors and even the workers who fail to take safety measures or are careless. Under the common law system, precedents are part of the legal system. Hong Kong is no exception. Therefore, construction companies and legal firms are interested to know the possible amount of compensation. FastText-based classification, a kind of Computer-based automated text classification, classifies documents into predefined categories according to the content of the papers, is proposed in this book chapter for accident compensation in courts. We utilised 3000 sentences in court cases in Hong Kong. 90% of the data was used for training, and 10% was used for testing. The results show that the system’s precision for classifying construction accident cases into successfully or unsuccessfully obtained compensation was 95.7%. This demonstrates that the fastText-based classification can successfully classify papers with a high level of accuracy. This pilot research provides a practical example to showcase the possibility of utilising artificial intelligence for predicting the likelihood of obtaining construction accident compensation. This approach could offer a rough estimation of the chance of getting compensation, save human resources, and allow non-specialists without much legal knowledge to have a quick reference on the likelihood of obtaining compensation for accidents. The results can also be generalised to other types of accidents and regions operated under the common law system.