Question and Answer Chatbot for Customer Support
I want to build a chatbot that serves as a first line customer support on a retail website. I have a large log of chat sessions between customers and support professionals that I can use. I am wondering what is the best way to go about building this chatbot.
Here are some ideas I have looked into;
* The first thing I did was to breakdown the chat logs into Q&A pairs. Treat each question as a document, compute a term-document matrix and use it to retrieve the most similar question when a customer asks sometihng. The idea was to then simply pick the answer given to the question by the human (with some modifications). However, this gives really abysmal results and does not match to previous questions very well. Even if I get this approach to work well, we would be limited to the questions that already been asked.
* Another thing I thought about was to modify something like ALICE and write some AIML for customer support related QA. 373
However, that would take a lot of very precise AIML writint to work well. This solution would not be able to scale to other languages, which is something I want to do.
* Another idea I have is to try and cluster the answers given by the customer support staff (they are more likely to be aligned compared to the questions asked by customers). This would give me a sense of what questions are similar. Then I can use something like LSA on the questions and fall back to the first approach.
* Yet another way is to build an ontology of the domain specific knowledge and given a question decide which category the question falls into. The questions from the chat logs can then be mapped to the ontology and I could train classifiers for mapping questions to different knowledge areas. Once I reach the leaf nodes, I can give back pre-defined answers. However, my reservation is that mapping chat logs to the ontology would be very tedious. Is there a good way to map the existing QA to the ontology? 946