Tuning Text Classification for Hereditary Diseases with Section Weighting

Motivation: Information in life science publications is heterogeneously distributed over various sections. Depending on research questions, different sections cover more or less of the data needed to answer them. Our approach, called section weighting, seeks to make use of information coverage and density found in typical life science publications. We study the impact of section weighting on text classification according to hereditary diseases. Results: Our results indicate that weighting sections can improve text classification. Our systems gain 7% in F1-measure when we add section weighting. Proper composition of features is equally crucial, improving our results by 11%. Combining both techniques, the system yields a performance 18% higher than the baseline classifier. For our research question, favoring the sections Abstract, Introduction, and Materials and Methods yields the best results.