A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search

Qu, Jiaming; Arguello, Jaime; Wang, Yue

doi:10.1007/978-3-030-72113-8_36

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Framing the task in this way is significant, since the use of an interactive approach allows the user to exercise a more informed judgement regarding both term selection and application within a structured search strategy. More broadly, this approach aligns with the goal of offering state-of-the-art query support in professional search while preserving transparency and interpretability (J Qu et al, 2021).…”

Section: Query Expansionmentioning

confidence: 98%

Interactive query expansion for professional search applications

Russell-Rose

Gooch²,

Kruschwitz

2021

Business Information Review

View full text Add to dashboard Cite

Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues to optimise the balance between precision and recall.

show abstract

Section: Query Expansionmentioning

confidence: 98%

Interactive query expansion for professional search applications

Russell-Rose

Gooch²,

Kruschwitz

2021

Business Information Review

View full text Add to dashboard Cite

show abstract

“…However, despite their success, NRMs suffer from poor interpretability of their results [1]. With the increasing deployment of more complex NRMs, it is essential to explain the retrieval decisions of a "black-box" complex model to its end-users, thereby increasing their trust in the model [18].…”

mentioning

confidence: 99%

Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation

Michael

Ganguly

Bhatia³

et al. 2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. These models, however, suffer from poor interpretability as they do not rely on explicit term matching. As a form of local per-query explanations, we introduce the notion of equivalent queries that are generated by maximizing the similarity between the NRM's results and the result set of a sparse retrieval system with the equivalent query. We then compare this approach with existing methods such as RM3-based query expansion and contrast differences in retrieval effectiveness and in the terms generated by each approach.

show abstract

A systematic review of multidimensional relevance estimation in information retrieval

Peikos,

Pasi

2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

In information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct relevance aspects employed for estimating multidimensional relevance. Moreover, we highlight the approaches used to aggregate scores related to these factors and rank information items. Our insights underline the importance of concise definitions and unified methods for estimating relevance factors within and across domains. Finally, we identify benchmark collections for evaluations based on multiple relevance aspects while underscoring the necessity for new ones. Our findings suggest that large language models hold considerable promise for shaping future research in this field, mainly due to their relevance labeling abilities.This article is categorized under: Application Areas > Science and Technology Technologies > Computational Intelligence

show abstract

A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search

Cited by 3 publications

References 33 publications

Interactive query expansion for professional search applications

Interactive query expansion for professional search applications

Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation

A systematic review of multidimensional relevance estimation in information retrieval

Contact Info

Product

Resources

About