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Gene Interaction Extraction from Biomedical Texts by Sentence Skeletonization

The presented paper describes a method of text preprocessing improving the performance of sequential data mining applied in the task of gene interaction extraction from biomedical texts. The need of text preprocessing rises primarily from the fact, that the language encoded by any general word sequence is mostly not sequential. The method involves a number of heuristic language transformations, all together converting sentences into forms with higher degree of sequentiality. The core idea of enhancing sentence sequentiality results from the observation that the components constituting the semantical and grammatical content of sentences are not equally relevant for extracting a highly specific type of information. Experiments employing a simple sequential algorithm confirmed the usability of the proposed text preprocessing in the gene interaction extraction task. Furthermore, limitations identified during the result analysis may be regarded as guidelines for further work exploring the capabilities of the sequential data mining applied on linguistically preprocessed texts.