There was a need for a comprehensive, end-to-end analysis and intelligence platform that can aggregate online articles, journals, research papers and news and create summarisations out of them. Additionally, this platform also must find out the similarity or difference between authors and articles and measure the authenticity of the article.
Alumnus built an information aggregator platform that can satisfy these requirements. The articles extracted and the analyses on them could be personalised based on preference of the user. Designed as an end-to-end analysis and intelligence platform, it combines state-of-the-art natural language processing and natural language understanding (NLP / NLU) algorithms to analyse text in context. Features include a displaying geographic distribution of extracted articles, extraction of words / tags frequently exhibiting co-occurrence of identified topics like Covid drugs, tag articles and create Concept Networks around the tags.
This platform offers an ecosystem of co-branded third-party text analytics apps for use by end-consumers. For example, one use case was to accumulate a set of biomedical information at one place to reduce the navigation effort of a subscriber.
In a Covid context, this can be a very helpful tool for researchers to accumulate in one place all information and research outcomes, be presented with the summaries and explore different possibilities.