Understanding and Analyzing Human Languages
For enterprises, NLP solutions are essential to improve customer experience, optimize the operations and allow data-driven approach for decision making.
This set of technologies gives the ability to process and understand human language at a scale that can revolutionize the way one interacts with customers, achieving automation for specific business objectives.
NLP solutions are no longer limited to simple keyword matching; they combine machine learning, deep learning, statistical methods and linguistic rules for businesses to begin to draw from the depth of human communication by analyzing concepts within Semantics (understanding), Syntax (word ordering) and Pragmatics (intent interpreting).
Types of NLP technologies have seen a significant uptick in deployment post the introduction of OpenAI’s GPT-3 model with ChatGPT being one amongst them. Primary application areas we have got hands-on experience with include:
Companies use Machine Learning, Deep Learning & NLP models to understand the emotional intensity of text data gathered from emails, feedback forms or social media comments to measure customer delight and overall mood reflected by their customers.
Further, these techniques have also been used to automatically detect and classify the entities from a large text corpus like name, organizations, locationsor dates etc. Works by businesses like search, categorization of emails, knowledge graphs and so on.
By utilizingmachine learning, particularly deep learning models, businesses generate content, to craft human-quality text formats like emails, articles, or even creative content. Text summarization, on the other hand, is used on automatically generating concise summaries of lengthy documents by extracting key information and sentences using extractive summarization or abstractive summarizationtechniques.
NLP technologies are now vastly used to extract specificor relevantinformation from various text sources like documents, emails, social media posts, and even hand-written notes. Businessesseek solutions to automatically pullkey datafrom large volumesof files in a simple conversational way like interacting with ChatGPT.
This is one of the primary use cases of NLP technologies. The main goal is to develop chatbots which can understand and respond to user queries in a humanized, conversational way. By integrating custom LLMs like GPT-4, these chatbots go beyond rule based or knowledge-basedapplications.
IDP is a very commonuse case in Finance, Recruitment and Legal industries. With this solution, enterprises automate tasks like document classification, key data extraction, and form processing. This streamlines workflows, reduces manual effort, and improves data accuracy& security.Evenby seeing the growing demand for IDP, we have built a generative AI-powered intelligent document processing platform.
