Introduction  
1 What is it about?  
2 Opportunities for the insurance industry.  
3 Main challenges to embed NLP models into solutions.  
3.1 How to access or collect useful text data?  
3.2 Enrich traditional information to enhance services.  
3.3 Interfacing NLP models.  
4 Some history to understand evolution of NLP and new opportunities  
4.1 Name Entity Recognition  
4.2 The study of relationships  
4.3 The statistical approach and the use of Machine Learning  
NLP in practice: from data processing to production  
5 Text mining illustrated through use cases  
6 Data is never clean: the preprocessing  
7 Converting text to numerical vectors: Text Embedding  
8 Feature engineering.  
9 Modelling with NLP.  
10 How to test and analyze a text mining model?  
Further description of NLP  
11 Deeper understanding of text preprocessing  
11.1 From lower casing to tokenization  
11.2 Length of the text  
12 Deeper understanding of text embedding  
12.1 Bag Of Words (BoW) and Term Frequency-Inverse Document Frequency (Tf-Idf)  
12.2 Word2Vec  
12.3 Doc2Vec  
12.4.1 The Attention Mechanism  
12.5 Bidirectional Encoder Representation for Transformers (BERT)  
12.5.1 Inputs Representation  
13 Reminder on machine learning metrics that can be used also for text mining  
13.1 Classification Metrics  
13.2 Regression Metrics  
13.3 Business Metrics  
Conclusion