# Interactive Extractive Search over Biomedical Corpora

arXiv: https://arxiv.org/abs/2006.04148 · HTML: https://mlsanity.com/paper/2006.04148/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [A Search Engine for Discovery of Scientific Challenges and Directions](https://mlsanity.com/paper/2108.13751/) (2021)
- [A Dataset for N-ary Relation Extraction of Drug Combinations](https://mlsanity.com/paper/2205.02289/) (2022)
- [Comparing the Framing Effect in Humans and LLMs on Naturally Occurring Texts](https://mlsanity.com/paper/2502.17091/) (2025)
- [Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current Practices](https://mlsanity.com/paper/2106.01105/) (2021)
- [Neural Extractive Search](https://mlsanity.com/paper/2106.04612/) (2021)
- [Two Kinds of Recall](https://mlsanity.com/paper/2303.10527/) (2023)
- [Descriptive Knowledge Graph in Biomedical Domain](https://mlsanity.com/paper/2310.11681/) (2023)
- [Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation](https://mlsanity.com/paper/2309.10057/) (2023)

## Abstract (arXiv)

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