Meegan Gower


2023

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Medical Concept Mention Identification in Social Media Posts Using a Small Number of Sample References
Vasudevan Nedumpozhimana | Sneha Rautmare | Meegan Gower | Nishtha Jain | Maja Popović | Patricia Buffini | John Kelleher
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

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Using MT for multilingual covid-19 case load prediction from social media texts
Maja Popovic | Vasudevan Nedumpozhimana | Meegan Gower | Sneha Rautmare | Nishtha Jain | John Kelleher
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

In the context of an epidemiological study involving multilingual social media, this paper reports on the ability of machine translation systems to preserve content relevant for a document classification task designed to determine whether the social media text is related to covid. The results indicate that machine translation does provide a feasible basis for scaling epidemiological social media surveillance to multiple languages. Moreover, a qualitative error analysis revealed that the majority of classification errors are not caused by MT errors.