UNIMIB at TREC 2021 Clinical Trials Track

TL;DR

This paper describes UNIMIB's participation in the TREC 2021 Clinical Trials Track, exploring various query representations and retrieval models, including neural re-ranking and a novel decision-theoretic approach, to enhance clinical trial retrieval performance.

Contribution

The paper introduces a novel decision-theoretic relevance model and demonstrates its effectiveness combined with keyword extraction for clinical trial retrieval.

Findings

Keyword extraction improves 84% of queries over median NDCG@10.

Decision-theoretic model improves 85% of queries over median RPEC@10.

Neural re-ranking enhances retrieval effectiveness.

Abstract

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.