Top Natural Language Processing Applications for Enterprise Users
Natural Language Processing is being adopted by enterprises on a large scale owing to its potential to facilitate business analytics. This technology is also resulting in reducing time and money invested in data collection and analysis processes. The global Natural Language Processing market valued at USD 13.16 billion in 2020, and it is expected to be worth USD 42.04 billion by 2026, registering a CAGR of 21.5% during 2021-2026.
Hence, with respect to this growing market, we will introduce you to some real-life applications of NLP for enterprise users. NLP has already found its main application in social media marketing. A fact from social media analysis states that over 90% of personal messages contain adverbs and adverbs express 90% of the emotions and feelings in a language. Data extraction, digital marketing, and even customer service have been made easier and safer with NLP. The article will also be covering how NLP is already offering automatic algorithms that can be trained to analyze linguistic preferences of clients, prospects, or employees.
End-to-End Coverage of Image, Video, and Text
With the combination of Computer Vision with NLP, enterprise users can reach the next level of text classification and visual text recognitions. Combining computer vision and NLP into a single, multi-modal enterprise platform offers a unified approach to the end-to-end AI lifecycle.
Clarifai, a leading independent AI company offers such a platform capable of text classification and visual text recognition to detect, understand, and classify blocks of text to extract meaning. The models can be used in a wide variety of contexts, from subject assessments to moderation, online analysis, and performance enhancement. In multi-modal workflows, the merging of text and image-based visual recognition models also come together to simplify enterprise processes.
Finding the Important and Relevant Information
SAS’s Visual Text Analytics uses the combined capacity of NLP, machine learning, and language rules to retrieve useful knowledge from unstructured data. It tackles issues across sectors, including the monitoring and analysis of notes, risk and fraud analyses, and the use of consumer reviews for early issue identification. SAS Visual Text Analytics tools include text processing, text mining, categorization, sentiment analysis, and search in a scalable modern environment.
The platform enables users to plan data for the study, examine the subjects visually, construct text templates and incorporate them in current structures or business processes. Enterprise users would be able to easily analyze large quantities of data using predefined templates and combine text analysis output with other machine learning and prediction methods.
Adding No-code Abilities to Key Initiatives
While the AI assistants and chatbots are helping enterprise users in various tasks, they are limited to a certain set of requests. Hence, they can opt for an AI platform such as Pryon, which reads, organizes, and retrieves information. It can process paper archives, messages, intranet contents, transcripts, and websites in minutes. Then, through text or speech contact, it provides immediate results on natural language problems. With Pryon, current assistants and chatbots can quickly and rapidly be expanded to answer millions of questions. They can also boost the search functionality and include in-source results and additional contexts.
Elevating Customer Experience with Virtual Agents
Virtual Agents equipped with NLP enable enterprises bring a new and more advanced class of service to market. Enterprises can gain a significant advantage by delivering exceptional service through Intelligent Virtual Agents. For this, they can take an assist from a company such as Interference and its Interference Studio. It integrates the most advanced NLP and Conversational AI technologies from Google and IBM, helps enterprise users to eliminate complex IVR menus, and elevates the customer experience beyond simple speech-enabled, directed dialog systems.
With the service like above, enterprise users can deploy self-service applications using NLP to streamline the automated process by simplifying the customer interaction.
AI-Powered Contextual Automation Solutions
Data scientists and IT specialists devote months of complicated manual work to the supply of information to enterprises with huge databases, data lakes, and BI vendors. The related costs, delays, liability, and management problems are tremendous and would only escalate as the amount and sophistication of the data increase.
In such cases, solution providers such as Promethium helps enterprises to bypass these difficulties through AI and ML-driven contextual automation software. The solution goes beyond locating data to presenting it in a form where users can gain a deep understanding of it. Promethium allows industry analysts and other non-technical experts to tap the data of their company for responses to difficult questions using plain language.
Promethium enables enterprise users to instantly gather information via federated queries from many data sources that run through all databases, data lakes, and warehouses, and provide answers in just minutes instead of months. Around the same time, Promethium automates the detection of duplications and the supply of a complete line to validate insights, ensuring that all knowledge is comprehensive, making complying with government and business laws simpler.
Automate Conversations at an Unprecedented Pace
At present, Conversational AI bots demand a lengthy optimization process, carried out by scarce, expensive data science and developer talent. As such, a perfect tool that would optimize bots and upskill their AI roles would be monumental.
In such a case, enterprise users can take an assist from LivePerson’s AI Annotator to flag areas for improvement and suggest solutions in just seconds, all while going about their day-to-day tasks. If the bot of a brand has difficulty knowing what the client uses during the conversation, AI Annotator automatically surfaces the problem to the agents of the brand who can either easily annotate the conversation with a single point-and-click UI or suggest what the bot is meant to do.
As agents have considerable expertise in understanding and managing customers’ requirements, they will provide this continuing input in the manner that best serves consumers. With AI Annotator, consumer information agents will make these improvements quickly so that brands remain on the cutting edge of conversational trading without having to have extra executives to get their job done.