Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria

The biomedical knowledge about transcriptional regulation in bacteria is rapidly published in scientific articles, so keeping biological databases up to date by manual curation is rather than impossible. Despite the efforts in biomedical text mining, there are still challenges in extracting regulatory interactions (RIs) between transcription factors and genes from text documents. One of them is produced by text extraction from PDF files. We have observed that the extraction of RIs from text lines that comes from tables of the original PDF article produces false positives. Here, we address the problem of automatically separating this text lines from those that are regular sentences by using automatic classification. Our best model was a Support Vector Classifier trained with n-grams of characters of tags of parts of speech, numbers, symbols, punctuation, brackets, and hyphens. Despite a significant imbalanced data, our classifier archived a positive class F1-score of 0.87. Our best classifier will be coupled eventually to a preprocessing pipeline for the automatic generation of transcriptional regulatory networks of bacteria by discarding text lines that comes from tables of the original PDF.