Building an Enterprise Chatbot

Chapter 1: Processes in the Banking and Insurance IndustryThe chapter will focus on explaining some core process within the banking and insurance industry that is suitable for a chatbot application.No of 30Chapter 2: Identifying the Sources of DataThis chapter will discuss sources of data for conversation and action-based event triggers for a chatbot. Conversation courses would be from customer service centers, online chats, emails and other NLP sources, while action sources are customer account details and more personalize data.No of 30Chapter 3: Mining Intents from the Data SourcesThis chapter will discuss how to build a business-specific intent engine for chatbots.No of 30Chapter 4: Building a Business Use-CaseThis chapter will focus on how to identify the right business process to introduce chatbots. It will also discuss how to look at some of the metrics of success and RoI given a chatbot is deployed.No of 30Chapter 5: Natural Language Processing (NLP)Chapter This chapter focusses on processing and understanding natural language through the computer algorithm. It also introduces how to prepare data for applying the NLP algorithms. We will use Stanford CoreNLP, NLTK, gensim, OpenIE tools to explore and model.No of 80Sub - topicsQuestion & answering, information extraction, sentiment analysis, Machine translation,Text Regex, tokenization, normalization - lower case, lemmatization, stemming (Porters Algorithm), sentence segmentationConverting text to Syntactical parsing - dependency grammar, PoS, entity parsing - phrase detection, topic modeling, statistical features - TF-IDF, word embeddingsClassification - spam filter using naïve Bayes, sentiment analysis.........