# Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering

arXiv: https://arxiv.org/abs/1608.03905 · HTML: https://mlsanity.com/paper/1608.03905/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2016.

## Built on by (most cited first, 6 of 6)

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- [Question answering systems for health professionals at the point of care -- a systematic review](https://mlsanity.com/paper/2402.01700/) (2024)
- [Estimating Text Similarity based on Semantic Concept Embeddings](https://mlsanity.com/paper/2401.04422/) (2024)
- [A New Parallel Algorithm for Sinkhorn Word-Movers Distance and Its Performance on PIUMA and Xeon CPU](https://mlsanity.com/paper/2107.06433/) (2021)
- [Evaluating the impact of word embeddings on similarity scoring in practical information retrieval](https://mlsanity.com/paper/2602.05734/) (2026)

## Abstract (arXiv)

We propose a document retrieval method for question answering that represents documents and questions as weighted centroids of word embeddings and reranks the retrieved documents with a relaxation of Word Mover's Distance. Using biomedical questions and documents from BIOASQ, we show that our method is competitive with PUBMED. With a top-k approximation, our method is fast, and easily portable to other domains and languages.