In the last post we had used Conditional Random Fields (CRF) to extract attributes from e-commerce product titles and description. CRFs are linear models just like Logistic Regression. The drawback with linear models is that they do not take feature-feature interaction or higher order feature terms into account while building model. Linear models can under-fit on the data while too much non-linearity can lead to over-fitting. Non-linear models such as Neural […]
In the second post of this series we had listed down different vectorization algorithms used in our experiments for representing questions. Representations form the core of our intent clusters, because the assumption is that if a representation algorithm can capture syntactic as well as semantic meaning of the questions well, then if two questions which actually speak of the same intent, will have representations that are very close to each […]
In continuation of my earlier posts on designing an automated question-answering system, in part three of the series we look into how to incorporate feedback into our system. Note that since getting labelled data is an expensive operation from the perspective of our company resources, the amount of feedback from human agents is very low (~ 2-3% of the total number of questions). So obviously with such less labelled data, […]
Natural Language Question Answering system such as chatbots and AI conversational agents requires answering customer queries in an intelligent fashion. Many companies employ manual resources to answer customer queries and complaints. Apart from the high cost factor with employing people, many of the customer queries are repetitive in nature and most of the time, same intents are asked in different tones.