Background: Genomic functional information is valuable for biomedical research. However, such information frequently needs to be extracted from the scientific literature and structured in order to be exploited by automatic systems. Natural language processing is increasingly used for this purpose although it inherently involves errors. A postprocessing strategy that selects relations most likely to be correct is proposed and evaluated on the output of SemGen, a system that extracts semantic predications on the etiology of genetic diseases. Based on the number of intervening phrases between an argument and its predicate, we defined a heuristic strategy to filter the extracted semantic relations according to their likelihood of being correct. We also applied this strategy to relations identified with co-occurrence processing. Finally, we exploited postprocessed SemGen predications to investigate the genetic basis of Parkinson's disease.
REF propose a heuristic post-processing strategy for SemGen that aims at selecting the semantic relations that are most likely to be correct.
Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease
BMC Bioinformatics
BMC Bioinformatics
Medicine Computer Science