Interactive Extractive Search over Biomedical Corpora

TL;DR

This paper introduces an interactive system for biomedical text search that uses dependency graph patterns and keyword queries, enabling rapid exploration of large scientific corpora without requiring detailed linguistic knowledge.

Contribution

It presents a lightweight, user-friendly query language and an efficient retrieval engine for dependency-based search over biomedical texts, facilitating easier exploration and analysis.

Findings

Supports fast search over large biomedical corpora

Enables pattern-based querying with simple markup

Demonstrated on PubMed and COVID-19 datasets

Abstract

We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at https://allenai.github.io/spike