  Financial sector and especially the insurance industry collect vast volumes
of text on a daily basis and through multiple channels (their agents, customer
care centers, emails, social networks, and web in general). The information
collected includes policies, expert and health reports, claims and complaints,
results of surveys, and relevant social media posts. It is difficult to
effectively extract label, classify, and interpret the essential information
from such varied and unstructured material. Therefore, the Insurance Industry
is among the ones that can benefit from applying technologies for the
intelligent analysis of free text through Natural Language Processing (NLP).
  In this paper, CRL+, a novel text classification model combining Contrastive
Representation Learning (CRL) and Active Learning is proposed to handle the
challenge of using semi-supervised learning for text classification. In this
method, supervised (CRL) is used to train a RoBERTa transformer model to encode
the textual data into a contrastive representation space and then classify
using a classification layer. This (CRL)-based transformer model is used as the
base model in the proposed Active Learning mechanism to classify all the data
in an iterative manner. The proposed model is evaluated using unstructured
obituary 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 the
RoBERTa base model. The experiment shows that the proposed method can
outperform both methods for this specific task.
