NLP, NLG & IE

Natural Language Processing (NLP) enables better customer engagement

In today’s digital word, customers have access to multiple choices of communication channels and touch points. However, they do not have time!

So, customers look for quick yet comprehensive addressal of their queries and resolution of issues in a personalized manner. How can enterprises achieve this and scale up such a customer service?

Advances in NLP such as LSTMs, self-attention networks etc. combined with a multi-modal approach enable collation of inputs from multiple channels such as email, chat, social media to facilitate automated request routing, resolution of most recurring or mundane service requests, customer segmentation, real-time sentiment analysis and targeted messaging.

Thus, enterprises can automatically process more customer information with less resources instead of adding more employees to scale up their customer service operations as business grows.

Natural Language Generation (NLG) facilitates productivity improvement

Regulated industries such as Pharma & Lifesciences, Financial services, Mining & Exploration etc. are required to submit regulatory documentation on a periodic basis. This involves high amount of labor and time.

Outsourcing such activities also exposes enterprises to risk and eventually leads to unnecessary overhead such as monitoring and audits. How can enterprises internally handle such inevitable yet laborious tasks with efficiency while ensuring that costs do not escalate?

Advances such as Robotic Process Automation (RPA) in conjunction with AI advances such as NLG can handle reproducible and repetitive tasks such as collating information from multiple information management systems and applications into pre-defined formats.

Leveraging such capabilities allows workforce to focus on more complex task such as problem solving, nurturing customer relationships, product/service improvements etc.

Information Extraction (IE) enables efficient information retrieval systems

Millions of documents such as land lease agreements, drill logs, surveys, reports, assay data such as core tray images, design manuals, engineering drawings such as Piping & Instrumentation drawings (P&IDs) are stored in physical vaults leaving them vulnerable to theft or accidental damage.

They cannot be queried, organized or filtered unless one reads these documents and organizes the information into databases, a task humanely impossible. How can enterprises unlock the value of such legacy data?

AI lowers the barriers by augmenting human efforts with the right tools. Techniques such as Optical Character Recognition (OCR) speed up information extraction and provide scale. Such efforts can be further enhanced by leveraging NLP for automated keyword extraction and tagging. Iterations with AI in the loop enhance accuracy and productivity of information digitization through continuous learning leading to improvement in accuracy over time.

Chronological sorting and automated classification of documents is made possible through information digitization which further enable efficient information retrieval from a corpus by automatically scoring documents based on relevance to a particular user search criteria. Hence, the value of the data in the document vaults can be unlocked for new explorations and product developments