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.

该研究提出了一种从医学文献中的表格中提取数值和文本信息的方法，包括表格检测、功能处理、结构处理、语义标记、语用处理、单元选择和句法处理等七个步骤，F - 度量值范围在 82% - 92% 之间.