# A framework for information extraction from tables in biomedical literature

arXiv: https://arxiv.org/abs/1902.10031 · HTML: https://mlsanity.com/paper/1902.10031/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 4 of 4)

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- [Uni-SMART: Universal Science Multimodal Analysis and Research Transformer](https://mlsanity.com/paper/2403.10301/) (2024)
- [Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarians](https://mlsanity.com/paper/2112.00962/) (2021)
- [Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers](https://mlsanity.com/paper/2102.00819/) (2021)

## Abstract (arXiv)

The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications. Text mining provided in the past methods to retrieve and extract information from text; however, most of these approaches ignored tables and figures. The research done in mining table data still does not have an integrated approach for mining that would consider all complexities and challenges of a table. Our research is examining the methods for extracting numerical (number of patients, age, gender distribution) and textual (adverse reactions) information from tables in the clinical literature. We present a requirement analysis template and an integral methodology for information extraction from tables in clinical domain that contains 7 steps: (1) table detection, (2) functional processing, (3) structural processing, (4) semantic tagging, (5) pragmatic processing, (6) cell selection and (7) syntactic processing and extraction. Our approach performed with the F-measure ranged between 82 and 92%, depending on the variable, task and its complexity.