NLP: The fuel for Chatbots

Chatbots have been becoming increasingly popular and are being adopted in many businesses, replacing complex processes and becoming more cost-effective and efficient. These automated processes help employees breathe more quickly by taking care of time-consuming mundane activities that dull creativity.   A recent survey conducted by Gartner suggested that more than 50% of companies will spend more per annum on developing chatbots than traditional mobile app development this year. 

Yet, even though chatbots are quickly being adopted in the workforce, the result is somehow less than satisfying. Most applications of chatbots often result in employees cleaning up after a chatbot due to their robotic response or the chatbot wasn’t able to understand the query. The rule-based responses have very limited and unsympathetic responses which dissuade a consumer from inquiring further. There is no universal formula for chatbots. The main difference between a limited chatbot vs a conversational AI is if they implement Natural Language Processing (NLP is a tool of an AI that is the system in understanding ‘natural language’) techniques. . NLP uses several tools to understand the basic techniques of word definition, phrases, sentences, and texts, as well as syntactic( knowledge of word meanings and vocabulary) and semantic processing (understanding the combination of phrases). It also develops applications such as machine translation (MT), question-answering (QA), data retrieval, discussion, document production, and recommendation programs.

A little Background on NLP

In earlier times, though computers could read digital texts, they could not understand natural language or follow up with context to the text. They could not  process language in the correct way it is supposed to be interpreted. Computers were not equipped to handle written text such as  perceived  scribbles on paper. Creating a natural flow of the conversation by converting text to speech and speech to text was another task the computer could not understand. Due to many failures, the research for these tasks was put to an end. In the early 1990s, new machine learning (ML) capabilities with rule-based parsing, morphology, and semantics created a whole range of possibilities for computers to understand natural language, which introduced NLP. Deep neural networks and representation learning assist in present-day NLP developments. 

Today, chatbots are slowly becoming the best alternative for customer queries, so companies spend additional resource investment for routine processes: This gives the company’s employees to work on something more productive than doing mundane and tedious daily tasks. To find a chatbot that suits a particular company, one must access all the options available in the market.

NLP techniques that make chatbots work better

As chatbots are becoming more prevalent with companies, they are investing in technologies that will improve the chatbot they are working with. The necessary fundamentals that create a better functioning chatbot are essentially AI. There are few standard NLP capabilities such as those listed below.  

Dynamic Text to Speech 

 Usually, a machine is incapable of producing natural-sounding speech as well as natural-sounding reading. With the assistance of machine learning(ML) models, engineers could train machines or bots to produce natural-sounding speech in different languages and accents. Additionally, speech inflections, emphasis, and adding tone to one’s reading also make the flow of the speech more natural. This is another skill that can be taught to a machine via ML training.

Named Entity Recognition (NER) 

NER is a basic technique that is used to perform entity recognition in order to extract entities from a text.  For example, in the sentence “Elon Musk is the owner of Tesla,” Elon Musk and Tesla are named entities. NER assists in downstream processing which helps in improving interaction with humans based on the information it gathers. 

Optical Character Recognition (OCR)

The  OCR  method  is used to extract information from digital and non-digital copies of data. Sometimes,  unintelligible  information  can  only be understood via  logic. One instance could be doctors’  prescriptions that needed a trained professional to interpret. 

Contextual Extraction 

Humans can easily follow the flow of an earlier conversation, but a machine usually cannot do the same if they are not categorized under the correct columns and rows in a database. However, through contextual extraction, machines could automatically understand structured information from an unstructured source.  

In Conclusion..
As discussed above, these few techniques can immensely improve your chatbot into a much better communicator. If your AI can take over mundane tasks, humans can focus on more complex issues instead of wasting time on tasks that are tedious. NLP helps in making that automation of work a reality.



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