One of the fascinating areas of AI is Natural Language Processing (NLP), which has already produced many tools we use daily, including chatbots, voice assistants, translators, and many more.
Despite recent technological advancements, it is still challenging to create systems around Natural Language Processing (NLP) since understanding natural language is difficult. Nevertheless, new technology enables the developing of more intelligent NLP systems with more functional and operational capabilities.
Understanding Natural Language Processing (NLP)
Robotic and computer systems can learn and mimic human language through natural language processing. It aids intelligent systems in understanding human language and mastering communication.
NLP technologies are crucial for companies that deal with massive amounts of unstructured data, including emails, social media interactions, survey replies, and other types of data. Companies might analyze data to automate operations and make business choices to identify trends among the various data sets.
NLP technology is used frequently in sentiment analysis, where computers learn to recognize human emotions like sarcasm to identify fake news online. Text classification organizes unstructured data by making sense of it; chatbots and virtual assistants, which make them smarter and more obedient to commands; and speech recognition and auto-correct software improvements.
Top NLP Trends Of 2022
The trends listed below will significantly impact how NLP systems develop over the coming years and will help the sector gain substantial traction with key stakeholders.
The following are the top NLP trends to look forward to in 2022:
Better Service Desk Responses
In the modern world, when you contact a service desk with a problem, you often receive a response in the form of a ticket that has been opened, and you’ll hear back within a set time limit.
The majority of the tickets, according to studies, are repetitive and can be resolved without human intervention if organizational information is appropriately mined. Here, NLU can be of great assistance and be utilized to rapidly and intervene-free remedy the issue.
According to the subject and content of the email and the ticket’s category, the system will search through previous encounters before deciding on the final resolution method.
The NLP system’s initial response will be to provide the user with a detailed action plan. It will then follow up via email with a virtual assistant who can help them solve their problem immediately. Response emails will become more intelligent, enhancing the company’s consumer experience.
Intent Less AI Assistants
RASA has discussed the five tiers of AI assistants in writing, one of the most popular platforms for creating conversational AI assistants. The synopsis is skillfully illustrated below.
While level 5 conversational AI helpers are still far off, level 3 and level 4 proficiency in the field are advanced. All enterprise stakeholders must be involved in this transition, and a key obstacle to its advancement is an accurate understanding of user goals.
To properly create a response, NLU systems needed a large training sample of user intents. These intents were frequently added to the system without knowledge of their contents.
Conversational AI development platforms are moving away from user intent-driven training, where responses are based on what the user says. The pre-training of intents will take a secondary position with a large sample of training data of human-to-human conversations.
Improvements In Enterprise Search
While it’s still unclear which of the work-from-home or work-from-office models is more productive, businesses are likely to embrace the hybrid model in the years to come fully. The organization’s primary goals under the hybrid work paradigm are to increase staff productivity and find new ways to maintain employee engagement.
Businesses are also updating their internal IT systems to respond to employee questions more logically and sympathetically. The secret to accomplishing this is to take advantage of an organization’s internal systems and eliminate silos so that staff members can quickly and naturally discover answers to their questions.
Brand’s Immersive Presence in Voice-driven Navigation Assistants
The voice control technology is utilized across various product and service categories. The technology is used to help create hand-free capabilities, particularly in cars where drivers rely on an in-car voice assistant for numerous tasks. Setting navigation is one of the main tasks that may be completed with voice control technology.
Hands-free calling, restaurant ordering, car temperature control, windshield wiper activation, door locks, etc. For instance, don’t be shocked if your car’s voice assistant alerts you tomorrow to your favorite restaurant along your path to the destination that is providing a tempting deal on your preferred food! Brands will become fully involved in every step of your trip, and you, the client, will undoubtedly appreciate it.
Enterprise Experimenting NLG
The use of AI to generate narratives from a dataset is known as natural language generation (NLG). NLG employs a mix of five stages. The primary subjects that will be covered after the procedure are first identified to filter the data.
Data interpretation and understanding are accomplished through machine learning. A written plan is made based on the type of data interpreted, assembling the key phrases to provide a summary of the subject. The sentences are recast in a grammatically proper context to create a document that sounds natural.
The user’s choice determines how the final product is produced. Businesses frequently produce repetitious material, whether press releases, product notes, opinions on hot topics, or quarterly financial reports.
A human copywriter’s job will be to provide a unique touch to the tone and tenor of the brand communication, while NLG will find value in producing such repetitious content. In the future, NLG will have more and more information consistently, requiring minor manual editing to conform to organizational needs.
Conclusion
Natural language processing applications are rapidly growing, and NLP is constantly evolving. With so much information at our disposal, it’s essential to understand, monitor, and, in some instances, filter it.
The availability of low-code, no-code tools, and ready-to-use pre-trained models will help NLP grow even more in the upcoming years. Businesses, in particular, will continue to gain from NLP, from enhancing operations and customer happiness to cutting costs and making better decisions.
