Evaluation of Vector Space Models for Medical Disorders Information Retrieval

Nowadays, consumers often search online to seek medical and health care information that they need. To improve this access, the ShARe/CLEF eHealth Evaluation Lab (SHEL) organized a shared task on information retrieval for Medical Disorders in 2013. This paper describes our participation in this task. In order to detect latent semantic relevance between queries and webpages about disorders, a semantic vector model based on distributional semantics is used as the information retrieval model. Specifically, variants of random indexing are employed to generate document and term representations. In addition, to reduce the lexical lap between different clinical expressions of the same concept, query expansion is also conducted using the UMLS. A baseline information retrieval method using the vector space model (VSM) and semantic vector models with different random indexing building procedures were developed and evaluated with or without query expansion in the shared task. The best performance was achieved by VSM, with MAP of 0.1480, P@10 of 0.3700 and nDCG@10 of 0.3363. Experimental results indicate that VSM and semantic vector model are complementary, and suggest combining these methods may further improve performance.