Disease-symptom relationships are of primary importance for biomedical informat-ics, but databases that catalog them are incomplete in comparison with the state of the art available in the scientific literature. We propose in this paper a novel method for automatically extracting disease-symptom relationships from text, called SPARE (standing for Syntactic PAttern for Relationship Extraction). This method is composed of 3 successive steps: first, we learn patterns from the dependency graphs; second, we select best patterns based on their respective quality and specificity (their ability to identify only disease-symptom relationships); finally, the patterns are used on new texts for extracting disease-symptom relationships. We experimented SPARE on a corpus of 121,796 abstracts of PubMed related to 457 rare diseases. The quality of the extraction has been evaluated depending on the pattern quality and specificity. The best F-measure obtained is 55.65% (for speci f icity ≥ 0.5 and quality ≥ 0.5). To provide an insight on the novelty of disease-symptom relationship extracted, we compare our results to the content of phenotype databases (OrphaData and OMIM). Our results show the feasibility of automatically extracting disease-symptom relationships, including true relationships that were not already referenced in phenotype databases and may involve complex symptom descriptions.