Biomedical Question Answering via Weighted Neural Network Passage Retrieval

TL;DR

This paper introduces a weighted neural network-based retrieval method for biomedical question answering, significantly improving document and passage retrieval performance on BioASQ datasets.

Contribution

It proposes a novel weighted cosine distance retrieval scheme leveraging neural embeddings, enhancing biomedical QA systems' effectiveness.

Findings

Significant performance improvements over state-of-the-art models.

Effective retrieval of relevant biomedical documents and passages.

Validated on BioASQ challenge datasets.

Abstract

The amount of publicly available biomedical literature has been growing rapidly in recent years, yet question answering systems still struggle to exploit the full potential of this source of data. In a preliminary processing step, many question answering systems rely on retrieval models for identifying relevant documents and passages. This paper proposes a weighted cosine distance retrieval scheme based on neural network word embeddings. Our experiments are based on publicly available data and tasks from the BioASQ biomedical question answering challenge and demonstrate significant performance gains over a wide range of state-of-the-art models.