论文标题
UNIMIB在TREC 2021临床试验轨道
UNIMIB at TREC 2021 Clinical Trials Track
论文作者
论文摘要
这项贡献总结了UNIMIB团队参加TREC 2021临床试验轨道的参与。我们已经研究了不同查询表示的影响以及几个检索模型对检索性能的影响。首先，我们已经实施了一种神经重新级别的方法来研究密集文本表示的有效性。此外，我们还研究了一种新的决策理论模型以进行相关性估计的有效性。最后，将上述两个相关模型与标准检索方法进行了比较。特别是，我们将关键字提取方法与基于BM25模型的标准检索过程和一个决策理论相关模型相结合，该模型利用了此特定搜索任务的特征。获得的结果表明，当与传统或决策理论相关性模型结合使用时，提出的关键字提取方法可改善TREC中位数NDCG@10度量的84％的查询。此外，关于RPEC@10，使用的决策理论模型可改善报告TREC的中位数价值的85％。
This contribution summarizes the participation of the UNIMIB team to the TREC 2021 Clinical Trials Track. We have investigated the effect of different query representations combined with several retrieval models on the retrieval performance. First, we have implemented a neural re-ranking approach to study the effectiveness of dense text representations. Additionally, we have investigated the effectiveness of a novel decision-theoretic model for relevance estimation. Finally, both of the above relevance models have been compared with standard retrieval approaches. In particular, we combined a keyword extraction method with a standard retrieval process based on the BM25 model and a decision-theoretic relevance model that exploits the characteristics of this particular search task. The obtained results show that the proposed keyword extraction method improves 84% of the queries over the TREC's median NDCG@10 measure when combined with either traditional or decision-theoretic relevance models. Moreover, regarding RPEC@10, the employed decision-theoretic model improves 85% of the queries over the reported TREC's median value.
