Keywords: mamed-entity recognition, search, semantic textual similarity, extractive summarization, smart environment, COVID-19, literature exploration
Abstract: Historically the vast amount of knowledge that experts publish has been increasing in such a pace that keeping up to date and having a full perspective, even in particular topics, has become quite challenging. Such is the case of the current COVID-19 pandemic were there are so many clinical notes, experiments, expert observations around the world that doctors, researchers, and public authorities struggle to explore pieces of related but not explicitly connected knowledge concerning to their respective duties. To simplify the process of exploration of the literature related to COVID-19, we propose a smart literature analysis environment, which includes several NLP-powered components to enable a more efficient reading process. In particular, we propose a semantically-guided transversal reading. We believe that this type of reading can significantly benefit the process of grasping the prominent opinion and state-of-the-art of a particular aspect. Our strategy to provide this feature was to interlink all semantically related sentences by semantic-textual-similarity (STS). Besides, we enrich the literature with named-entity recognition and disambiguation (NERD), using the major life science databases as entity sources, enable named-entity searches, provide network-graphs of the most interconnected publications and, an interactive tool to highlight the most central statements within an article. All these capabilities are embedded in an easy to use web environment.