AI chatbot systems have come a long way. Modern chatbots are elegant and stylish. In fact, they can even feel like humans, thanks to machine learning technologies. This AI-enabled chatbot software utilizes a branch of AI called Natural Language Processing (NLP) to offer enhanced services and user experience. These AI chatbot applications can perform multiple tasks like answering user queries or assisting the user to fill out a form.
So, repetitive, and time-consuming activities can be handled by AI chatbot systems and human agents can concentrate on more complex issues. AI-powered conversational applications can perform simple and structural task more accurately and faster than their human counterpart, but more importantly, they can interact with the users by understanding their intentions, emotions, and need. These AI chatbot systems are getting more complex and sophisticated, as well as more humanlike due to the use of Natural Language Processing technologies.
So, what is Natural Language Processing or NLP?
According to a very popular online knowledgebase, Natural Language Processing or NLP is an area of Computer Science and Artificial Intelligence that is concerned with the interaction between computers and human or natural languages, focussing on how a computer program can efficiently process a massive volume of natural language data.
In simple terms, Natural Language Processing or NLP technologies help to interpret the human language inputs correctly and perform required actions based on the understanding they obtained from these inputs. The more accurate the understanding, the more satisfactory and effective the chatbot will be for the users. So, without NLP, an AI chatbot system that needs natural language input is meaningless. Other than Chatbots and Virtual Assistants, NLP has considerable contribution to optimizing search engine operations, medical research, data mining, and business intelligence.
So, the objective of NLP is to facilitate human-to-machine communication. Machines understand programming languages, and human communicates through natural languages. But an NLP model can enable computers to decode the natural language inputs, and moreover, they can mimic the way humans communicate with each other. Speech and text are no longer a series of symbols in NLP. It also considers the natural language’s hierarchical structure, in which words form phrases, which then lead to sentences and gradually to logical thoughts. NLP solutions utilize pre-programmed or acquired knowledge to understand the intent and meaning of the sentence, phrases, idioms, etc.
NLP consists of two key categories:
Natural Language Understanding (NLU)
NLU or Natural Language Understanding breaks down the inquiry to assist the chatbot in understanding it. It helps machines to understand human commands without any formalized syntax or computer language by using algorithms to perform syntactic and semantic analyses of the text and speech to establish structured ontologies. This enables machines to understand the natural language inputs. There are three key ideas in it:
Entities: An entity is a keyword from the user’s query that the chatbot has identified to determine what the user wants. AI chatbot systems include the concept in their program for entities.
For example, in the question “What is my current bill?” the term “bill” is an entity.
Intents: They aid in determining the action the chatbot should take in the response to user input. For instance, “Do you have white bread? ” and “I want to order white bread” have different intentions. Both “Show me some white bread” and “I want to order one” are the same. One command is triggered by each of these users’ texts, providing them with alternatives for different types of white bread.
Context: An NLU algorithm can struggle to determine the context of a discussion if it does not know the user’s conversation history. It implies that it won’t recall the query, even if it has received the answer to that query. So, the status or state of the chat discussion has to be stored in order to distinguish between the different phases.
Either criteria like “Restaurant” can be flagged or phrases like “Ordering Cakes” can be tagged by the Ai chatbot systems to identify the context. Without needing to know the answer to the prior question, the context makes it simple to relate the intentions of users and it helps AI chatbot software to deliver more accurate responses.
Natural Language Generation (NLG)
Natural Language Generation or NLG concentrates on producing human-like responses by using AI programming to create a natural spoken and written narrative from a data set. A subset of NLP, it uses computational linguistics, hidden Markov model, Recurrent Neural Network, and transformer models to generate dynamic responses in real-time. NLG is related to the NLU and AI chatbot system needs both to communicate with its users. NLG contains six stages:
Content Analysis: Content analysis filter the content and determine what will be included in the response. It helps to identify key topics from the source material and established the relationship between them.
Data Understanding: Machine learning puts these into the right context after interpreting and identifying patterns.
Document Structuring: Depending on the type of interpreted data, the software pays out an appropriate narrative structure and document plan.
Sentence Aggregation: Different sections of sentences combine that can accurately convey the topic.
Grammatical Structuring: The software creates natural-sounding texts with grammatical rules. NLP programs can deduce the syntactical structure of the sentences and can rewrite them correctly.
Language Presentation: Final output comes with a chosen template in a more refined manner.
Now let’s find out how NLP-powered chatbots work
There are generally five important steps to how an AI chatbot system reads, interprets, understands the user input, and next formulates and sends appropriate responses to the users:
Tokenization: Tokenization is the process by which the NLP separates a word string into tokens. These tokens are linguistically symbolic entities, and they serve the program in various other ways.
Entity Recognition: The chatbot program model searches for groups of words, such as the brand name of the product, the user’s name, or the user’s location, depending on the information needed.
Normalization: The chatbot program model examines the text to detect typographical or frequent spelling issues. It makes the chatbot appear more human to the users.
Dependency Parsing: To identify dependent and related terms that users may be attempting to express, the chatbot scans the user’s input for objects and subjects, verbs, nouns, and common phrases.
Response Generation: The chatbot then chooses the most relevant response to deliver to the user after generating several responses using the data gathered through all the previous steps.
Another important function of an AI chatbot system is sentiment analysis. In Sentiment analysis, the algorithm attempts to understand the user’s sentiment from a query posed by the user. The bot analyzes the entities, themes, and topics from the input and tries to decipher the moods and opinions of the users. Voice bots utilized voice analytics to identify different emotions of the users through voice inputs.
The most incredible part of these AI chatbots with NLP technologies is these applications get smarter with each user interaction. Many online chatbot tools and platforms offer NLP engines or there are third-party NLP platforms that can be integrated with AI chatbot systems even if the native platform, on which it was built. may not support NLP technologies. So, AI chatbot software powered with NLP, NLU, NLG, and Machine Learning can help you to connect and engage with your customers better and provide the right responses.