Dr Vatsala Nundloll has been featured in Research Features, a platform that seeks to break down barriers that exist in the dissemination life cycle from researcher to the mass audience. In this article, Vatsala discusses combining machine learning, natural language processing, and semantic web techniques to bring together information from unstructured and structured sources into a linked data model.
When someone mentions data, we are inclined to think of structured datasets made up of numbers in a spreadsheet. But data comes in many forms, including text, video, audio, and imagery. These unstructured data types don’t adhere to a column-row format, so accessing and analysing the information contained in these unstructured formats can be challenging, and valuable information can remain hidden.
What if we were able to extract key pieces of information and make them available? Vatsala answers this question with a unified model that extracts data and information from unstructured textual documents. She demonstrates this approach in case studies focusing on ecology, conservation science, and flood risk management.
Vatsala has put been busy putting this work into practice with the Bespoke and Emerging Projects Team within the Environment Agency, where she is applying the use of Natural Language Processing (NLP) techniques to extract insight from documents about lessons learnt across Environment Agency projects.
You can read the full article here.
Dr Vatsala Nundloll joined Methods in July 2022 as a Data Engineer. She brings with her a wealth of experience having previously worked at Lancaster University as a Senior Research Associate where she investigated the use of digital technologies to mitigate data challenges faced in the area of environmental science.