Biomedical Question Answering via Weighted Neural Network Passage Retrieval
This work addresses the challenge of efficiently exploiting biomedical literature for question answering, which is incremental as it improves retrieval in an existing pipeline.
The paper tackles the problem of retrieving relevant biomedical passages for question answering by proposing a weighted cosine distance retrieval scheme based on neural network word embeddings, achieving significant performance gains over state-of-the-art models on BioASQ challenge data.
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.