Chatbots and natural language processing: what you should know about NLP

The purpose of chatbots

Businesses and organizations require chatbots which has the ability to interact and to understand the intentions of a user. The ability of chatbots to understand the intention of the user will depend on the sophistication of the natural language processing which is used with every conversation. This is why chatbots systems make use of sophisticated NLP strategies. During this process, the software program will make use of machine learning engines as well as processes which can determine the meaning of conversations. Only then will it be possible to achieve maximum conversational accuracy which can be achieved even when there has been little or no training. Sophisticated chatbots have the ability to process and understand messages that might contain many sentences. They can understand a whole range of contextual references and multiple intents which is made by consumers. These chatbots will also look at idiomatic sentences, patterns and other things which are used by the consumer. Most of the available NLP engines provides recognition support for many available entities. The system also provides all of the necessary tools which might be required to customize the language understanding of any chatbot.

Chatbots and machine learning

In order to be optimal, effective bot platforms will also provide an engine that makes use of ML or machine learning algorithms and preprogrammed queries. These things are used as training data in order to determine the best match in order to better understand the intent of the user. It will also search for patterns which can be used to tune and train the NLP engine so that it can be even more effective. It is interesting to learn that these engines can allow clients with a huge amount of training data to use this information even during the initial stages. The system is also able to review user histories which allows it to make corrections to improper utterances and false positives. NLP is a powerful tool which is able to recognize many simple but important distinctions in the natural language which is used by people. This is being used to diminish the possibility of misinterpretation. The benefit is that developers will not have to design alternative conversation paths in order to deal with every possible idiomatic variation.

Chatbots and intent recognition

What is interesting about chatbots is the fact that most chatbot conversations can be easily broken down into a few words which will clarify exactly what the user wants to do. Those words will mostly include a noun or a verb. It may involve a command which simply says find a restaurant. Many other things are possible such as the creation of events. It is also possible to send an alert or to search for an item and even to transfer funds. How it works is that the NLP engine has the ability to analyze the structure of every command which is issued by your user and it will be able to identify each word by its position, capitalization, meaning, plurality, conjugation and a range of other factors. This is making it easier for the chatbot to make accurate interpretations and this allows it to understand nonobvious and obvious synonyms which relate to those common action words. There is a lot more behind intent recognition than people may think. It is about more than just matching a particular input with a task. It is also important to match an input with its correctly intended task. All of this is accomplished by matching nouns and verbs with a wide variety of nonobvious and obvious synonyms.

The importance of synonyms and patterns

Most of the sophisticated bot-building platform will provide the user with a built-in synonym library which can be used to better understand common terms. This will include things such as phrases, words and also sentences. There are sophisticated systems available today and it is even possible for users to customize the accuracy of available NLP systems by simply adding additional synonyms for many of the words which are used in the names of most common tasks. This can include many words which are associated with the particular dialog task entity mode of your business. It is even possible to make use of bot-building platforms which would allow users to take many other things into accounts such as metaphors, account slang, and many other idiomatic expressions. This is accomplished by adding task fields, patterns for tasks and even things such as dialog task entity nodes. When referring to a pattern this is simply a sentence which can represent a specific task. However, the sentence might not actually contain any of the words which are included in the name of that task or task field.

Avi Ben Ezra: As the Chief Technology Officer (CTO) and co-founder of SnatchBot and SnatchApp (Snatch Group Limited), Avi Ben Ezra leads the Group’s long-term technology vision and is responsible for running all facets of the tech business which includes being the architect of the platforms and UI interfaces. Avi has proven tech track record and 15+ years of demonstrated career success developing tech initiatives of organizations of varying size and scope. Avi possesses in-depth experience in developing digital market places within Fintech and AI.