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

TL;DR

This paper introduces a biomedical document retrieval method using centroid-based embeddings and Word Mover's Distance, demonstrating competitive performance and efficiency in question answering tasks.

Contribution

The paper presents a novel retrieval approach combining centroid embeddings with a relaxed Word Mover's Distance for improved biomedical document retrieval.

Findings

Competitive with PUBMED in biomedical retrieval

Fast with top-k approximation

Easily adaptable to other domains and languages

Abstract

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.