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How Does Natural Language Processing (NLP) help Chatbots?

NLP helps the chatbots understand the intent of the sentences by breaking down the semantics of natural language into individual words. It then tries all possible combinations of these words to frame a meaningful response.


How Does Natural Language Processing (NLP) help Chatbots? | HiTechNectar
Published By - Kelsey Taylor

Chatbots are the future of customer engagement, and we all know this. There are many features of chatbots, but the most widely used, for now, is to address concerns of customers over a chat application. As a consumer, you must have interacted with a chatbot many times without even realizing it, and this is exactly what we will be discussing here. Chatbots have evolved with time and technology has pushed the boundaries of possibilities so far ahead, it is surprising to see what chatbots can do now. Natural Language Processing (NLP) has a major role to play here in the development of chatbots. NLP chatbots are the future, and their development and growth starts from here.

How NLP Helps Chatbots?

NLP is an interesting tool that helps break down the semantics of natural language such as English, Spanish, German, etc. to individual words. It then deciphers the intent of the input using various combinations of these words and responds appropriately.

This piece of technology has been put to use by many organizations across the globe, and you might not even understand when you are interacting with a machine because it seems very human-like, but is very efficient and there is a very minute lag in terms of responses. Quicker responses help keep customers happy with the speedy resolution of issues and hence eventually result in more business and a boost to the top line.

NLP chatbots are usually paired with Mathematical Linguistics (ML) to make them more effective. The more interactions a chatbot faces, the smarter it becomes because ML ensures that with each interaction the chatbot learns something new as to what the customers are expecting as a resolution. The best part about chatbots is the ability to run multiple instances at the same time, based on the data load that the server hosting the chatbot can handle.

So consider this, in an organization, the customer service online is handled through a chatbot. When the chatbot has interacted with over 100 customers, it has the data to analyze which are the top complaints. So the next time the chatbot is interacting with the next customer, it might suggest a quick solution to the customer for the common problem, and hence the customer receives a quicker response. The customer is happy, the company is happy, and NLP has done its job to make the chatbot smarter in conjunction with ML.

Implementation of NLP is not an easy task by any means. The process involves a couple of steps as follows:

  • Parsing –this process consists of analyzing the syntax of the text and its meaning and how it is used in a sentence. This can then be converted into machine language accurately.
  • Tokenization – This involves dividing the word into a group based on their meanings, call to action, etc. when similar words are encountered a certain action has to be performed and those processes are formed within the program.
  • Lemmatization/stemming – this is a bit of a complex step where the inflections of a word are removed and the morphological structure of a word is formed in a way that all possible usage of the word which means a specific or similar meaning are analyzed and grouped together. Hence, no matter what grammatical syntax is used, the chatbot will analyze the basic word in question and suggest relevant solutions accordingly.
  • Language Detection – this is helpful in chatbots which support multiple languages.
  • Part-of-speech Tagging – this is connected to lemmatization and language detection. The chatbot checks for the part-of-speech and grammatical usage of the command received in the natural language and then analyzes it.

All these steps when performed properly shall result in an efficient NLP chatbot.

Examples of some Chatbots Using NLP

What we have been discussing till now is not just theory. NLP chatbots are already available for use. Companies have developed solutions and put NLP chatbots into use. Let’s have a look at some examples:

  1. Endurance:

    A solution developed for patients suffering from Alzheimer’s and Dementia. It identifies deviations in conversational branches and helps identify memory loss. It is a great companion for the patients as the chatbot provides recollection pointers for the patients, and it is largely helpful for the doctors and the family since all logs from the chatbot can be accessed.

  2. Casper:

    It is a very ambitious product to help insomniacs keep busy during the night by conversing with the chatbot as they find it difficult to get sleep. This helps keep the insomniacs busy. The chatbot is still in its initial phase of development and hence it is a bit rudimentary in terms of responses for the questions, but with time it is sure to improve.

  3. UNICEF:

    The world body had made use of NLP chatbot to gather information from areas where it is running development campaigns. Their bot, U-Report, conducts polls on a wide range of social issues. The users can then respond to these polls with their inputs and the data so collected is used as a basis for designing policies.

Benefits of NLP Enabled Chatbots for Businesses

Businesses have a lot of gain from NLP chatbots. The advantages are galore as follows:

  • Available 24×7; the only downtime will be in case of scheduled server maintenance or for any upgrades.
  • Can handle multiple customers seamlessly which humans cannot, limited only by server capability.
  • Saves money for the business by handling the task of hundreds of customer service agents, thus reducing the need for actual people down to a tenth of the original.
  • Relatively better customer satisfaction as the resolution to issues is immediate and there is no ambiguity and confusion in case of NLP chatbots.
  • Can handle repetitive work without losing efficiency.
  • Can also act as a personal assistant based on its application design. Google has showcased how with its Google Duplex product, and we already have Google Now, Alexa, Siri, and Cortana.

NLP chatbots definitely have a bright future ahead. It is only a matter of time that someone develops a chatbot for their business and revolutionizes the customer experience. The rise of the digital revolution is going to bring us more interesting innovations to relish upon. Until then let’s make use of the available technology to the best of our ability and grow.


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