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Financial sector and especially the insurance industry collect vast volumesof text on a daily basis and through multiple channels (their agents, customercare centers, emails, social networks, and web in general). The informationcollected includes policies, expert and health reports, claims and complaints,results of surveys, and relevant social media posts. It is difficult toeffectively extract label, classify, and interpret the essential informationfrom such varied and unstructured material. Therefore, the Insurance Industryis among the ones that can benefit from applying technologies for theintelligent analysis of free text through Natural Language Processing (NLP).  In this paper, CRL+, a novel text classification model combining ContrastiveRepresentation Learning (CRL) and Active Learning is proposed to handle thechallenge of using semi-supervised learning for text classification. In thismethod, supervised (CRL) is used to train a RoBERTa transformer model to encodethe textual data into a contrastive representation space and then classifyusing a classification layer. This (CRL)-based transformer model is used as thebase model in the proposed Active Learning mechanism to classify all the datain an iterative manner. The proposed model is evaluated using unstructuredobituary data with objective to determine the cause of the death from the data.This model is compared with the CRL model and an Active Learning model with theRoBERTa base model. The experiment shows that the proposed method canoutperform both methods for this specific task.

CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data