The human language is embedded with endearing qualities. It is natural, free-flowing, and easy to comprehend. AI consultants have identified this as an essential addition to making technology more adaptable to humans. Hence, Natural Language Processing (NLP) techniques have emerged as a technological innovation teaching computers to talk to humans in the language they are most comfortable in.
NLP combines the basic principles of linguistics, computer science, and machine learning to process and analyze data in text and speech format. It is built to help computers understand and interpret data and build meaningful and value-added conversations with human users.
NLP - The component breakup
NLP is built on a strong foundation of thoroughly tested techniques that help process and comprehend human language. Artificial Intelligence plays a crucial part in helping your business leverage the relevant technique that works best for you.
Sentiment Analysis
This is an NLP technique that is used in analyzing human sentiments from text data and categorizing them as positive, negative, and neutral. These are usually extracted from social media posts and reviews and help you to identify customer interest in a brand.
Semantic Analysis
This technique helps computers understand the meaning of words and sentences in a specific context through synonyms, antonyms, word sense disambiguation, and so on.
Summarization
This technique is intended for users who are short on time and are unable to read through long articles and texts. It summarizes the long text forms like reports and articles into meaningful nuggets for readers to ingest.
Tokenization
As the name suggests this technique will break down words, characters, or even subwords into smaller units known as tokens. Tokenization is an important step in text pre-processing and must always be performed while using NLP. These tokens are then collated as a Vocabulary or a dataset in NLP terms. Every Vocabulary is assigned an ID. Tokenization is used for word modeling, vocabulary building, and frequent word occurrence.
Named Entity Recognition (NER)
NER is a technique used to extract structured information from unstructured data. It identifies and segregates all entities with names, for example, the name of an organization, names of people, places, and so on.
Chatbots in the World of Business
In the current business landscape, time and money are of the essence. Organizations are cutting down on costs and are constantly driven by a need to achieve competitive excellence within a shorter turnaround time. In this dynamic situation, Chatbots have emerged as the messiah of modern business.
A technology innovation that began its journey as a text-based app has today evolved as an all-encompassing virtual assistant for every business. Organizations of every scale and depth are embracing this essential business tool to help them gain critical customer insight, better revenue margins, and an assured growth trajectory. AI consultants India have helped install chatbots for organizations spanning almost every industry vertical.
Airbnb uses chatbots to help them develop insights into the travel history of their guests, analyze their preferences, and provide customized assistance for an enhanced experience. The personalized chatbot installed by Lyft, the mobility service provider, helps users by storing and sharing their ride history, and location details. This makes it convenient for users to book rides and be aware of estimated arrival times.
The chatbot at H&M, the apparel brand, advises users on the latest fashion trends and ideas. It also recommends clothes that suit their style based on their fashion preferences. Domino’s chatbot makes ordering pizza a seamless experience. It recommends pizza toppings based on the user’s purchase history and taste preferences.
Roadblocks for Chatbots
Despite the growing preference for chatbots and their subsequent installation as a suitable aid for most businesses, these programs do face issues. The challenges stem from their structural and inherent traits and tend to hinder their ability to provide able assistance. Rule-based chatbots head the list. They are built using a predefined set of rules and are comparatively simple to install and maintain. Nevertheless, they are extremely limited when it comes to supporting dynamic or complex conversations.
In the current fast-paced business environment, chatbots must scale up to understand the variations in language, tone, and voice that it is expected to interact with. Its inability to adapt to new and varied user inputs makes it deliver a poor user experience. Moreover, maintaining the large set of rules that it is based on becomes cumbersome and time-consuming. As the complexity of the chatbot grows, the rules need to be updated accordingly. This increases the learning curve and leads to increased training costs.
Rule-based chatbots are always challenged when it comes to handling context-dependent queries. The rules are not built to handle the variations that natural language presents. Consequently, they may provide inaccurate or redundant responses while replying to such queries. While defining rules it is humanly impossible to cover all possible scenarios. Hence Rule-based chatbots often fall short when interpreting slang, cultural dialects, and colloquialisms.
Enabling with NLP
To tide over this rising challenge, the consultants have always recommended NLP-based chatbots. It has been considered as the principal catalyst in helping chatbots evolve from simple rule-based programs to intelligent conversation agents. NLP empowers chatbots by helping them understand and parse sentences, identify keywords, and extract relevant information from text inputs. It makes user messages valuable and meaningful for chatbots.
When message clarity is established, chatbots can easily analyze the intent behind those messages. Understanding the user's purpose helps them deliver an appropriate response that meets the user's requirements. The NER technique in NLP helps it empower chatbots with the ability to identify and recognize entities in the user messages. They can cull out names of locations, dates, events, and people to deliver an intelligent response.
NLP enables chatbots to become more context-aware. With a clear identification of the specific context that the user is referring to, chatbots can ensure a more coherent and relevant interaction. One of the biggest advantages of NLP-based chatbots is their ability to simulate human-like responses. Chatbots can be trained to deliver personalized responses in correct grammatical sentences and proper language.
Delivering multi-turn conversations is an inherent trait in NLP-based chatbots. This helps them recollect earlier messages and respond to them in the most context-aware manner. This also enhances the scope for more dynamic and interactive conversations. In NLP-based chatbots, learning is a given. It can evolve by learning new variations from user messages and deliver better and more innovative responses based on the new learnings.
As part of its inherent attributes, NLP helps chatbots imbibe a natural flow in the conversations they build. The flow is more seamless and includes greetings and other pleasantries. Interruptions are also handled more effectively making it sound and feel more human.
Business of the Future
Social Media has emerged as a formidable business tool. It is ubiquitously present in almost every aspect of business today. AI have helped businesses leverage its benefits in multiple ways. Curating user data on social media to analyze user sentiments is one of the latest and most powerful uses of this tool. With the changing dynamics, sentiment analysis will evolve to adapt to the fluctuating demands.
The future trends imagine the inclusion of audio, video, and images along with text as possible data sources for analyzing customer opinion. It will further evolve to include more subtle human emotions like sarcasm and irony as possible inputs for analysis making it a more context-aware sentiment analysis. Users have always been the reason for businesses to exist. The advent of AI has helped businesses leverage the opinions and emotions of their users to enhance and improve their business models to serve their customers in a better and bigger way.
AI consulting services provide organizations with specialized strategies, knowledgeable direction, cost-effectiveness, rapid deployment, and risk reduction. These advantages result in enhanced decision-making, optimized AI investments, and long-term financial gains. Businesses can consider using AI as a strategic partnership to ensure successful AI adoption and improve productivity, innovation, and overall business performance while minimizing risks and maximizing returns on AI investments.
Conclusion
NLP-powered chatbots have a promising future ahead. As AI and NLP continue to evolve, chatbots will emerge with enhanced sophistication, versatility, and innovation. As NLP introduces newer language techniques, chatbots will begin to engage in more context-aware and intelligent interactions. The future belongs to a tailored approach to delivering services. Users love feeling special. Chatbots will build on this innate human desire and deliver enhanced personalization for improved business generation.
