Baseline and extensions approach to information retrieval of complex medical data: Poznan's approach to the bioCADDIE 2016

Abstract

Information retrieval from biomedical repositories has become a challenging task because of their increasing size and complexity. To facilitate the research aimed at improving the search for relevant documents, various information retrieval challenges have been launched. In this article, we present the improved medical information retrieval systems designed by Poznan University of Technology and Poznan University of Medical Sciences as a contribution to the bioCADDIE 2016 challenge—a task focusing on information retrieval from a collection of 794 992 datasets generated from 20 biomedical repositories. The system developed by our team utilizes the Terrier 4.2 search platform enhanced by a query expansion method using word embeddings. This approach, after post-challenge modifications and improvements (with particular regard to assigning proper weights for original and expanded terms), allowed us achieving the second best infNDCG measure (0.4539) compared with the challenge results and infAP 0.3978. This demonstrates that proper utilization of word embeddings can be a valuable addition to the information retrieval process. Some analysis is provided on related work involving other bioCADDIE contributions. We discuss the possibility of improving our results by using better word embedding schemes to find candidates for query expansion. Database URL: https://biocaddie.org/benchmark-data

Learning Domain‐specific Semantic Representation from Weakly Supervised Data to Improve Research Dataset Retrieval

“…Wei et al (2018) sent the query to Google and expanded it with the returned snippets. Cieslewicz et al (2018) used the pseudo-relevant feedback to expand the query with terms from the initial dataset search results. Search results-based query expansion does not rely on manually constructed resources and can adapt to different application fields.…”

“…Query expansion based on search results mainly utilizes words in the search results from external web search engines (Wei et al, 2018) or internal dataset retrieval systems (Cieslewicz et al, 2018) to expand queries. Wei et al (2018) sent the query to Google and expanded it with the returned snippets.…”