Introducing Natural Language Processing NLP: Building a Basic Chatbot with NLP and Incorporating Hausa Translation by TANIMU ABDULLAHI

It gathers information on customer behaviors with each interaction, compiling it into detailed reports. NLP chatbots can even run ‌predictive analysis to gauge how the industry and your audience may change over time. Adjust to meet these shifting needs and you’ll be ahead of the game while competitors try to catch up. Intel, Twitter, and IBM all employ sentiment analysis technologies to highlight customer concerns and make improvements. When your conference involves important professionals like CEOs, CFOs, and other executives, you need to provide fast, reliable service. NLP chatbots can instantly answer guest questions and even process registrations and bookings.

Instead, the messages may contain a synonym of a word in the training dataset. Answers uses the inbuilt set of synonyms to match the end user’s message with the correct intent. This function analyzes the sentiment of the user input and returns a polarity value that represents the sentiment (positive, negative, or neutral). As mentioned earlier, the ConllExtractor() function is utilized to extract noun phrases from the user input.

What is natural language processing for chatbots?

It reduces the effort and cost of acquiring a new customer each time by increasing loyalty of the existing ones. Chatbots give the customers the time and attention they want to make them feel important and happy. Entities can be fields, data or words related to date, time, place, location, description, a synonym of a word, a person, an item, a number or anything that specifies an object. The chatbots are able to identify words from users, matches the available entities or collects additional entities needed to complete a task. NLP analyses complete sentence through the understanding of the meaning of the words, positioning, conjugation, plurality, and many other factors that human speech can have.

Next, our AI needs to be able to respond to the audio signals that you gave to it. Now, it must process it and come up with suitable responses and be able to give output or response to the human speech interaction. This natural language processing chatbot method ensures that the chatbot will be activated by speaking its name. NLP or Natural Language Processing has a number of subfields as conversation and speech are tough for computers to interpret and respond to.

Generative AI bots: A new era of NLP

This information is valuable data you can use to increase personalization, which improves customer retention. There is a multitude of factors that you need to consider when it comes to making a decision between an AI and rule-based bot. At Maruti Techlabs, we build both types of chatbots, for a myriad of industries across different use cases, at scale. If you’d like to learn more or have any questions, drop us a note on — we’d love to chat.

We explored various NLP libraries such as NLTK, SpaCy, TextBlob, Gensim, and Transformers, which offer a wide range of functionalities for language processing tasks. By leveraging these libraries, we were able to implement sentiment analysis, noun phrase extraction, and translation capabilities in our chatbot. As demonstrated above, the built chatbot accepts user input, extracts noun phrases if present, pluralizes them, and responds based on semantic analysis in both English and Hausa. Discover how AI and keyword chatbots can help you automate key elements of your customer service and deliver measurable impact for your business. And when boosted by NLP, they’ll quickly understand customer questions to provide responses faster than humans can.

Then, give the bots a dataset for each intent to train the software and add them to your website. In terms of the learning algorithms and processes involved, language-learning chatbots rely heavily on machine-learning methods, especially statistical methods. They allow computers to analyze the rules of the structure and meaning of the language from data.

We start with letters, progress to words, then sentences, and finally, entire stories. Similarly, NLP breaks down language into smaller pieces, learns from patterns, and uses this knowledge to interpret or generate new content. NLP chatbots can provide account statuses by recognizing customer intent to instantly provide the information bank clients are looking for. Using chatbots for this improves time to first resolution and first contact resolution, resulting in higher customer satisfaction and contact center productivity. NLP chatbots are effective at gauging employee engagement by conducting surveys using natural language.