Multi-Perspective Semantic Information Retrieval
This addresses the challenge of nuanced relevance prediction in biomedical information retrieval, though it appears incremental as it builds on existing methods.
The paper tackles the problem of capturing nuanced relevance in information retrieval by introducing a Multi-Perspective IR system that combines multiple deep learning and traditional models, achieving evaluation on the BioASQ Biomedical IR + QA challenges.
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents or snippets of text) that are relevant to a particular query or need from a large repository of information. While a combination of traditional keyword- and modern BERT-based approaches have been shown to be effective in recent work, there are often nuances in identifying what information is "relevant" to a particular query, which can be difficult to properly capture using these systems. This work introduces the concept of a Multi-Perspective IR system, a novel methodology that combines multiple deep learning and traditional IR models to better predict the relevance of a query-sentence pair, along with a standardized framework for tuning this system. This work is evaluated on the BioASQ Biomedical IR + QA challenges.