Chatbots or Virtual Agents are rapidly ramping up to augment the human computer interaction starting from Self-help and gradaully moving up the Knowledge management chain to how an Agent (such as a technical support call center agent) uses a specialized chatbot inhouse for Agent Assist.
Some of these technologies are nascent, others more mature, such as Watson Virtual Agent.
The first thing that a virtual assistant does is to detect your intent: this is accomplished using Natural Language Understanding and Natural Language Classification. The process starts with a recognition of the goals of the personas interacting as well as being training on the most commonly recurring calls, requests, chat transcripts, etc.
What we are talking about here is more than Search or Text Analytics it is about Intent based understanding of content.
Search is more about Federation of search indices, Content level security, Content display facets, filtering, Community & Social extensions, Connectors ERP, DBs, etc.
Content Analytics on the other hand is about finding relevant words in text counting occurrences, Analytics for entity & associations extraction, Integration with analytics & prediction systems.
Intent-based Architectures allow you to understand the query down to the users intent, Execute interactive query refinement to be actionable, Generate a recommendation, Interactively access data with implied meaning & relationships Establish word / phrase proximity, document relationships.
All these capabilities are predicated upon the discovery of intent: intents that can be reused across personas in the interaction dialog .
An intent represents the purpose of a user's input. Each capability uses a natural language classifier that can evaluate a user utterance and find a predefined intent if it is present.
For example, a recommended practice is to review the intents that are identified most often just before users request to speak to a human agent. Investigating the causes of escalations can help you prioritize where to focus future training efforts. You can determine whether user inquiries are being misinterpreted, whether your service is missing a common intent altogether, whether the responses that are associated with an intent need improving, and so on.