1. Improving Keyword-Based Topic Classification in Cancer Patient Forums with Multilingual Transformers.
- Author
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Buonocore, T. M., Parimbelli, E., Sacchi, L., Bellazzi, R., del Campo, L., and Quaglini, S.
- Abstract
Online forums play an important role in connecting people who have crossed paths with cancer. These communities create networks of mutual support that cover different cancer-related topics, containing an extensive amount of heterogeneous information that can be mined to get useful insights. This work presents a case study where users' posts from an Italian cancer patient community have been classified combining both countbased and prediction-based representations to identify discussion topics, with the aim of improving message reviewing and filtering. We demonstrate that pairing simple bag-of-words representations based on keywords matching with pre-trained contextual embeddings significantly improves the overall quality of the predictions and allows the model to handle ambiguities and misspellings. By using non-English real-world data, we also investigated the reusability of pretrained multilingual models like BERT in lower data regimes like many local medical institutions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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