How Machine Learning and Fundamental Meaning aid your Chatbot’s NLP

 

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What to expect, and not to!

Don’t expect chatbots to come right away with the ability to understand the human language. Just like we humans need to be trained before we start conversing with the world, the bots too need to be trained. This is where Machine Learning plays a pivotal role. Developers use Machine Learning to enable natural language – conversation between bots and systems/people. But this does not happen overnight, it requires an ample amount of time and substantial data to understand, interpret and bring out the desired output.

 

Should companies wait till NLP gets PERFECT?

The answer is ‘NO’. And we have valid reasons for this. To begin with, ‘Machine Learning’ takes time and the earlier you set out to teach your bot to interpret and respond to your customers the faster you are prepared for tomorrows changing digital world. You got to be out there now, else your competitors are going to leaving you trailing behind. It’s all about perfecting NLP-but if you are not in it, you are not going to reap the benefits of it when the bot is ready to go.

 

Analysing Machine Learning and Chatbots

Machine Learning plays an important role in training the chatbot and it’s important not to undermine its role in doing so. A common misconception is that ML leads to a bot understanding human language word for word. This is not entirely true. In reality, what happens is that ML doesn’t view the words themselves when analysing what the user has said. On the other hand it uses what a developer has fed it with and this includes data, patterns, algorithms and statistics.

ML requires large amounts of data to attain the desired state of accuracy. Typically it can take atleast a thousand examples to reach an accurate state. Teaching the bot through ample data is the only way we can achieve positive user experiences.

During pre-development stage bot developers use custom rules to identify the intent of the message to be relayed to the end user. For instance a rule may include “if a sentence contains the word ‘rewards,’” then the user is asking about the banking reward points. It sounds like an easy solution but the problem arises when the conversation gets complex. It is at this point that the user experience may suffer.

So give your bot ample amount of time and data to achieve an user experience that is hitch free.

 

Fundamental Meaning and Chatbots

Fundamental Meaning is all about understanding the words. With this kind of approach to NLP, each conversation is broken down word by word, as if breaking a sentence word by word. The two major factors here are ‘intent’ and ‘entities’ – ie what user wants to achieve and what data is required to complete this task.

Fundamental Meaning is an approach to NLP that’s all about understanding words themselves. Each user utterance is broken down word-for-word, as if the chatbot were in school breaking down a sentence on the chalkboard. During this process, it’s looking for two things – intent (what the user is asking it to do) and entities (the necessary data needed to complete a task). Here the bot can identify common synonyms and thus determine the action required to complete the task. Here the bot developer adds new synonyms to the bot’s vocabulary.

 

Why we use the KORE NLP engine and Bot building platform?

Kore.ai’s robus bots platform magically combines Machine Learning with Fundamental Meaning and thus solves a major issue with bot building ie. ML only approach. The model adopted by Kore improves the development cycle of a chatbot. Combining ML and FM ensures a well-rounded NLP engine and allows bot developers to resolve conflicts with bot communication.

At Avenir we use Kore’s bot building platform to build chatbots as the Fundamental Meaning approach to NLP helps organizations get chatbots off the ground faster and easier, while at the same time ML enables developers to resolve idiomatic phrase conflicts and to add utterances to the lexicon library. The two combine to enable not only faster NLP development but more thorough NLP processing. For enterprises, a chatbot strategy doesn’t have to be a choice between one or the other. Now it can be both.

 

Want to know more about NLP and Bots? Ask for a bot demo through this link REQUEST BANKING BOT DEMO or speak to us now by writing to bankingbot@avenir-it.com or call us on +44(0) 20 8596 5007

Avenir Business Solutions, Headquarted in the UK is a Technology company serving Enterprises globally and is an exclusive KORE Messaging & Bots solution partner. We have recently launched the Avenir Banking Bot.

 

Ansy Austin

Ansy Austin

Marketing Manager at Avenir Business Solutions
As a keen Digital Marketer, Ansy plays a key role in strategising and implementing marketing activities for Avenir Business Solutions, an IT company headquartered in the UK with a global presence in India and Middle East. Avenir excels in building and customising intelligent NLP enabled bots. With her deep understanding of business trends and the need for marrying technological advancements with business processses, Ansy enables enterpises to use bots to their competitive advantage as we head on to a fully automated age. She has completed her MBA from University of Bath and also comes with a background of rich experience working for 7 years with an international news media company.

She can be reached at ansy@avenirbs.com
Ansy Austin

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