https://scholars.lib.ntu.edu.tw/handle/123456789/638458
標題: | Hybrid Re-ranking for Biomedical Information Retrieval at the TREC 2021 Clinical Trials Track | 作者: | Shi, Ming Xuan Pan, Tsung Hsuan Huang, Hen Hsen HSIN-HSI CHEN |
公開日期: | 1-一月-2021 | 來源出版物: | 30th Text REtrieval Conference, TREC 2021 - Proceedings | 摘要: | This paper presents our methodology for the task of TREC 2021 Clinical Trials Track, which requires a system to retrieve and return the most relevant biomedical articles after giving queries. We propose a novel approach to biomedical information retrieval by leveraging the term-based and the embedding-based retrieval models with a re-ranking strategy. In our hybrid framework, all the documents will be indexed by using a term-based, efficient search engine. For the given query, a smaller set of candidate results are retrieved from the search engine. The ranking is determined not only by the term-based ranking score but also by the term relationships labeled by the Amazon Comprehend service1 for refinement. Then, the candidate results are further scored by using the embedding-based model. We represent the document and the query with bioBERT and compute the cosine similarity between a pair of the document embedding and the query embedding as their relevance score. The final score is a linear combination of the term-based and the embedding-based scores. Experimental results show that our hybrid re-ranking method improves both Precision@k and R-precision scores on the 2016 Clinical Decision Support Track and 2021 Clinical Trials Track dataset. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638458 |
顯示於: | 資訊工程學系 |
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