The sequence labelling algorithm has two options - deep learning and traditional statistical models. Deep learning has a special class of neural networks called Recurrent Neural Network (RNN), which has multiple architectures, such as LSTM, GRU, Bi-directional LSTM, etc., and statistical models include hidden Markov model, maximum entropy Markov model, Conditional Random Field (CRF) etc. Since we do not have very large volumes of data, we decided to go with the statistical model.
Among the mentioned statistical models, CRF is the most efficient for scenarios such as this. CRF is a probabilistic framework for labelling and segmenting structured data, such as sequences, trees etc. It defines a conditional probability distribution over input sequences, given an output sequence. Based on the above understanding, we decided to the CRF model.
To sum up, the solution approach for SOP interpretation using ML involves sentence splitting, SOP interpretation and action prediction using CRF.
Reduced processing time with assistive automation implementation
As part of the overall assistive automation implementation, ML and NLP were used to interpret SOPs, corrections were made by agents through assistive UI and required actions were executed on the claim system using RPA jobs. This solution can be used for interpretation of other type of SOPs and user manuals, and troubleshooting guide.
The automation solution can not only reduce overall processing cycle time but also improve processing quality and customer satisfaction.