- Aim was to develop and NLP engine to process the adjuster notes to extract semantic features for Machine Learning model to find the probability of Subrogation in an insurance case. - Exploration was done on the data provided by client and features identified. - Implementation of an NLP rule-based feature extraction module. This helped us identify the semantic information from the note instead of just working using a keyword based system. - Work involved NLP techniques like dependency parsing, POS tagging, Lemmatization, True Casing and also word vectors to extract features. - This project also involved an Machine Learning module developed in Python to give the prediction based on the features extracted from NLP engine.