We present a query-based biomedical information retrieval task across two vastly different genres -newswire and research literaturewhere the goal is to find the research publication that supports the primary claim made in a health-related news article. For this task, we present a new dataset of 5,034 claims from news paired with research abstracts. Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list. We compare the classical IR approach using BM25 with more recent transformerbased models. Our results show that crossgenre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.

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In The ability to visualize our thoughts has always fascinated us. Even more intriguing is the subject of making a computer able to visualize those thoughts, just by understanding the human language. In this paper, a text to scene generation system is proposed for the educational domain where a basic Newtonian physics problem is conveyed to the system in natural language and the scene depicting the problem is generated and displayed to the user. The paper describes the implementation of the system as well as the results obtained. It is based on the integration of advances in NLP and computer graphics technology to generate a virtual environment. It makes it easier for students to visualize the problems and also helps teachers in explaining better.