Ever wondered how the new-gen chatbots can understand what we speak and type?
There was a time when computer systems understood only machine language. This machine language is a complex set of codes with strict syntax. However, with technology evolving, users can now understand natural language too.
According to a recent survey, 88% of web users chatted with chatbots across the web in a single calendar year. This shows the significance and the dependence of the users on chatbots and computer systems.
This article typically revolves around the topic – how do chatbots understand language differently than a programming language. We will learn how to differentiate between natural language and machine language. What’s more, you will learn the impact of natural language on the way businesses are functioning now.
What is a programming language and natural language?
Have you heard of Python, Java, C++, and other such computer terminologies. These are a few common and widely used programming languages. So, we can define programming language as the language of the computers. Computer systems understand programming language or the machine language.
The programming languages have strict rules called syntax. A syntax error happens when there is an error in the code. Such errors hinder the continual functioning of the computer systems. It should be noted here that programming language is case-sensitive, with special attention to punctuation marks as well. This makes the programming language complicated.
Natural language, on the other hand, is the language that we use to converse everyday. This does not have any syntax, and can be filled with idioms and other nuances. Modern chatbots are trained to understand natural language. This means common people can interact with computer systems with ease.
WIth natural language, the interaction and the engagement of the users with system increases. This is because they can converse with the computer system similar to the way they interact with fellow human beings. Additionally, the computer system can respond to users in natural language. This makes the system highly user-friendly and customer-oriented.
How do chatbots understand language differently than a programming language
Now we know that chatbots can understand natural language, but what is the key functionality that enables this feat? Chatbots are trained with Natural Language Processing (NLP). This system allows a chatbot to understand natural language. The following pointers will simplify the method.
Tokenization
When there is an input, the chatbot breaks the message into smaller parts that are called tokens. These tokens can be small words, phrases, or even small parts of a single word. This process is known as tokenization.
Part-of-spech tagging
Once tokenization is complete, the system then analyzes the various parts of the input. This helps the system understand the role and impact of each part. Different parts of the speech are differentiated in the form of noun, verb, adjective, etc.
Sentiment analysis
In this step, the system analyzes the sentiment behind the various phrases. This process is continuously improved with feedback and the utilization of past data sets. The training schedules are based out of a large volume of patterns, cues, and conversation data.
Intent recognition
This step helps the system understand your message and its intent. Various differences between text in the form of question, statement, and exclamation are analyzed and understood by the system. This helps the system to provide the reply with a suitable intent that matches the input.
Context management
Although the above steps help a chatbot to understand natural language and respond effectively to the same, context management helps in maintaining a “memory”. This is essential to understand the trend and pattern of various users interacting with the system.
On the other hand, programming languages do not have such functionalities. They are strict and are dependent on syntax. They do not understand emotions and intent. Also, the programming language is case-sensitive and executes instructions without inferring meaning.
Key functionalities that facilitates chatbots to understand natural language
In the previous section, we learned how chatbots understand natural language. In this section, we will explore the two important systems that enable the process. These powerful systems, in combination with continuous learning and the utilization of a large volume of data, help chatbots understand natural language.
Natural Language Processing (NLP)
NLP utilizes a number of technologies and system to help computers understand human language. This is a combination of computational linguistics, machine learning, and other models to understand incoming questions from humans in natural language.
NLP plays a major role in automating chatbots and other operations by simplifying mission-critical business processes. When you incorporate NLP to your computer system you can improve the productivity of your manual resources and improve your overall savings.
Deep Learning
Deep learning is actually a subset of Machine Learning (ML). This system uses multiple layers of neural networks that work similar to the human nervous system. In comparison with Machine Learning, deep learning has more layers of neural networks that make the decision-making process and other activities similar to humans.
With deep learning, you can effectively create Artificial Intelligence (AI) systems that find numerous applications in our day-to-day life. This technology is helping in the process of the AI transformation at a rapid pace.
Here are some resources that will help you understand the importance of chatbots in different businesses:
Insurance Chatbot Guide: Benefits, Use Cases & Examples
Best Chatbot for travel agency – Use cases & Examples
The Ultimate Guide to Real Estate Chatbot: Benefits & Use Cases
Differences between chatbot language and programming languages
Now that we have discussed the difference in the system and the build of the programming language and the natural language, let us now see how this affects human perception. Since users are not bothered about the system that you are using in the system, these pointers should be taken into consideration while choosing an effective chatbot for your business.
Flexibility
Chatbots having the ability to understand natural language have higher flexibility than the computer system, which requires programming language. Users can interact easily with the system in natural language, where there are no issues related to spelling mistakes and idioms.
However, with programming languages, users have to be conscious of the syntax, punctuation, and the case of the statements. This makes this language only suitable for specialized experts. Basically, the programming language does not offer you with any flexibility.
Scalability
WIth continuous learning, chatbots have the ability to quickly adapt and learn new trends. With natural language understanding, your systems can interact with users at ease and not hold back on developments. The growth and development is quick, and users can feel the improving technology every time they interact with the system.
On the other hand, programming language is stringent and does not support quick and continuous growth. It takes a long time to plan and implement changes and add new features. This hinders scalability and results in slow growth of the business.
Continuous learning
Chatbots to accumulate data and past conversations with different users. This data is used to improve the performance of the computer system. By analyzing past trends and user behavior, chatbots can provide responses that match the intent of the users.
Computer systems using programming language, however, have a fixed set of guidelines. There are no improvements or continuous feedback mechanisms for enhancing the language. This fixed system results in minimal chances of development.
Handling errors
Natural language input for chatbots does not have any defined syntax. This means you can make inputs with ease and not worry about rules. You can converse with the system in any intent. The chatbots also can respond to your queries with a similar intent. In case you make a spelling mistake in the incoming query, those errors are ignored, and the system still provides you with the desired response.
Programming language, on the other hand, cannot understand the emotions and intent. Also, if there are any errors in your query, the system fails to operate and provides an error output.
Tendency to perform human-like
Chatbots can respond to users with the same intent as the incoming query. This means users get to feel emotions and can develop a long-lasting connection with the system. When you develop a relationship with your users, you can effectively improve the engagement and interaction.
Programming languages, however, do not have such ability. Their response looks robotic and uses a set of pre-defined codes for responses. Further, not everyone can interact with systems using programming language.
Conclusion
In this article, we tried to answer the query – how do chatbots understand language differently than a programming language? The key difference lies in the method of interaction. Chatbots understand natural language, and therefore, users can interact with the system directly. However, programming language requires syntax and follows strict rules. This makes specially skilled people only to interact with the system.
WIth the development of technology and the development of powerful systems such as ML, NLP, deep learning, and AI, users can interact with computer systems with ease. Further, these chatbots also have the ability to respond to queries with the same intent as the input. This increases the engagement and interaction rate of the various users with the computer systems.
Frequently Asked Questions
Chatbots understand natural language, which is unstructured using natural language processing (NLP) techniques. However, programming languages are structured, with strict syntax and semantics that computers follow to execute specific tasks.
Programming languages are written with precise syntax and rules that leave little room for interpretation. Computers follow these rules exactly to execute commands. Natural language, however, is full of nuances, idioms, and context-dependent meanings, making it more complex for chatbots to understand.
Performance is improved through ongoing training with diverse and large volume of datasets, fine-tuning algorithms, incorporating user feedback, and using advanced NLP techniques like deep learning models and continuous feedback systems.
About the Author
Aaron Jebin
Aaron Jebin is an enthusiastic SAAS technical content writer interested in writing for new and existing technologies, platforms, and tools. With an experience of over 4 years in technical writing, he is keenly focused on developing articles to provide readers with complete solutions to the common problems that arise in the everyday workplace. His writing mostly focused on team building, work ethics, business analysis, project management, automation, AI, customer and employee engagement methodologies. He has an interest in baking cakes and making stained glass art. He is currently honing his drifting skills.