How to Create Effective Bots with Natural Language Processing Technology?

Natural Language Processing and Robots

NLP and bots for befitting retorts.

As we understand that Natural Language Processing (NLP) aids computers to understand human languages, there is very little understanding of how effective bots with NLP are created. In order to understand that, first, we will have to go through NLP tasks before we discuss the challenges in AI bot development. NLP Australia has a refined series of steps to follow for the creation of desirable chatbots.

Tasks in Natural Language Processing

NLP’s responsibilities span significant tasks. To break down human text and audio into measures that can be understood and analyzed well by computers requires a certain series of steps for the betterment of custom software development Australia. Let’s get into them!

1. Speech Recognition

Speech-to-text conversion or speech recognition holds an important place in the matter of speech analysis. Grammatical tagging or speech tagging is just a sub-phase of speech recognition that enables a computer to filter out speech and tag it with an accent, speech definition, or implied context. NLP techniques give extra attention to speech recognition as being the primary course of action for the whole NLP process.

2. Word Sense Disambiguation

Word sense disambiguation, considered one of the most crucial roles of NLP techniques, refers to a semantic analysis that extracts the exact meaning of a word in the given context. Since a word may have multiple meanings in human speech, we cannot have computer machines confused with synonyms or what else and lose clarity and precision in the process. This process further helps the computer to finalize whether a word is a noun or verb.

3. Sentiment Analysis

Undertones and sentiments are present in human speech. NLP performs the task of extracting these sentiments, undertones, and hidden contexts such as joy, fear, sarcasm, or attitude. It is also the toughest task NLP pulls off in the whole process. One of the foremost tasks of NLP Technology is sentiment analysis for the sake of best communication between humans and chatbots.

4. Named Entity Recognition

Named Entity Recognition or NEM figures out phrases and words as identities. For instance, Pakistan is a country name and Ali is a person’s name. It is a crucial task because if the computer cannot identify the entities, the whole thing becomes a useless task, which is why NLP technology cannot afford to use the most significant role of NEM.

Types of Chatbots

Chatbots as a concept are not old and it is only recently they have surged. NLP-driven chatbots are of two main types that we will be discussing.

1. Artificially Intelligent Chatbots

Developed to mimic human-esque responses and traits, Artificially Intelligent Chatbots are setby NLP to understand the undertones and dialects of human conversation. AI fused with NLP develops a really intelligent chatbot that can learn from interactions and answer nuanced questions. Everyone wants their customer service chatbot to be intelligent enough not to just answer queries but add more value to the conversation with relevant suggestions and fun discussion. Such intelligent chatbots have the capacity to have a full-on meaningful conversation with you. Do not underestimate them!

2. Scripted Chatbots

Chatbots that function on pre-programmed scripts are called scripted chatbots. Whenever a user poses a question or wants to discuss something, this kind of chatbot will only reply to what is pre-established and stored within its library. While such chatbots are also helpful in their own way, the lack of a structured manner serves as a thorny con. That makes this kind of chatbot tough to integrate with NLP and therefore they cannot be made into virtual assistants and that is something you would not want from your customer service chatbot.

We might see more types of AI chatbots in the future but at best those will only be the subtypes of these main two. In time, we will see scripted chatbots having their issues fixed and their script getting better in relevancy and accuracy with the passage of time. As far as artificially intelligent bots go, they also have a lot of room for improvement and obviously, they will also be enhanced to the maximum. When it comes to creating effective bots for communication using NLP, do not forget these two main types as you will work hard to create either of the two.

Challenges in the Development of AI Chatbots

Computers are no longer popular for only calculations but the world is eyeing their transformation into human-like machines. NLP exactly aids the machines to procure that level of intelligence. However, of course, it is not a bed of roses but rather a series of hard challenges one needs to overcome. While mimicking a human style, chatbots can present chaotic speech and other key hurdles that make communication difficult and further reliant on custom software Australia. This is a common list of errors chatbots make during their interaction:

  • Misspellings
  • Abbreviations
  • Dialects
  • Accents
  • Speech differences
  • Slang, homonyms, synonyms

A human brain can fix all this in a jiffy but once chatbots start to proceed on them it becomes abothersome repetition. Training an AI chatbot to adopt speech rules and modulations is akin to human learning a new language. The dissimilar meanings along with their own voice modulation, context, and intonation are not easy for an algorithm or machine to process. NLP technologies evolve constantly to develop the finest tech to help computers understand and differentiate among such nuances. Custom software development Australia can fix most of these issues even if it takes some amount of time.

Do not fear these challenges, NLP is being improved to counter them in a faster and smoother manner.


Now you have briefly learned how to create effective bots using NLP technology and it is a very much-needed explanation, given many want to learn more on the subject. The growing need for NLP Australia is attracting more curious minds to help further the influence of this AI subfield. NLP and custom software Australia bind together to deliver the best performance expected of the process. Hopefully, this read has helped you enough.