Two weeks ago, we proposed chabots as one of the current hottest topics in data mining and natural language processing. Chatbots are becoming the new “Big Data”, everyone is starting to talk about them. But there is not much information about the development resources they will consume or the applications they may have.
If you are a business thinking about incorporating a bot to your digital strategy, you may face different questions, and those are the ones we want to solve in this blog:
-What type of bots are in the market
-Limitations of each of them
-How to improve them
In today’s market we can find two different types of chatbots:
- Fixed chatbots:
The first one is based on programming rules. These types of chatbots are very fixed and not particularly user friendly. Why? Because if the customer doesn’t write in a way the machine is not programmable to understand, it won’t respond or answer with a wrong message thus immediately showing your customer that there is a machine on the other side of the conversation.
These kinds of bots are very limited and its performance depend 100% on how have they been programmed to answer.
- Chatbots based on machine learning:
They offer better results than the programmable bots, and this is because they have an “artificial intelligence” that processes natural language. What does this mean? that users don’t need to talk with the bot using specific commands or expressions. The chatbot is built to understand language over specific orders.
Another advantage from this type of bots is that they keep learning from each interaction with a user.
However even if machine learning bots seem to be a better option than programmable chatbots, they also present some limitations.
Chatbots based on machine learning can understand language, but as we experience every day, we do not communicate with each other in a unmistakably simple and grammatically perfect way. Our use of language is filled with ambiguity, and when talking to a chatbot we expect a level of understanding at the level of human being, however this doesn’t happen and everyone who has spoken to Siri knows this. We humans are not patient and if after 2 or 3 attempts the bot is not getting what we are trying to say, we will walk away from the website and we will stop considering purchasing that item or using the proposed service, and this could be a risk for businesses.
As we said these chatbots have artificial intelligence so they can learn from experience, however thousands of interactions will be needed for the bot to show the improvements of the learning.
What is our solution for this problem?
Introducing natural language processing and linguistics while constructing the bot, this way it will be already taught on some of the subtleties of the natural language, improving the learning path of the machine and providing a better experience for the interacting customer, no matter the vocabulary they are using.
How does this work?
By using our proprietary build parser and lemmatization or stemming technologies at Bitext we can help your chatbots. They will not only understand language but understand the structure and the nature of every sentence. Because of this understanding it doesn’t have to make all the decisions based on its learning experience, it will be able to understand people’s messages in a more efficient and human like way.
Would you like to know more about the possible issues that may arise?