I am sure I won’t make a spelling mistake while writing this blog. But unfortunately, it’s hard to expect the same while chatting, tweeting or commenting or social media. IVAs (Intelligent Virtual Assistants) built on the Haptik platform have to cater to such noisy queries and still provide quick and correct resolution.

Machine Learning has proven to be a great advantage over the simple rule-based system. However, it comes with its own set of complexities such as training model, its size, computation, etc. As a result, it becomes challenging to use machine learning for mobile applications, where users expect a quick response. 

One of the key components of most successful NLP applications is the Named Entity Recognition (NER) module which accurately identifies the entities in text such as date, time, location, quantities, names and product specifications. There are already existing sophisticated systems for NER such as spaCy, Stanford NER, etc.