Using Centroids Of Word Embeddings And Word Mover's Distance For Biomedical Document Retrieval In Question Answering
Georgios-Ioannis Brokos, Prodromos Malakasiotis, Ion Androutsopoulos . Proceedings of the 15th Workshop on Biomedical Natural Language Processing 2016 – 10 citations
[Paper]
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.