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.

探究了不同的查询表示方法结合不同的检索模型对检索性能的影响，同时提出了一种基于关键词提取方法的检索模型，并通过实验表明，该方法结合传统或基于决策论的相关性模型，可以显著提高查询效果。