Learning to Rank for Biomedical Information Retrieval

With the continuous development of biomedical, the scale of data has also continued to increase, which makes it difficult for researchers to extract information manually. In order to satisfy the researcher's information requirement, information retrieval techniques for the biomedical field have been resolved. Learning to rank is the field of machine learning and information retrieval combination, it has been shown to improve the retrieval efficiency to a large extent. This paper proposes a query optimization technology based on learning to rank. By using learning to rank for technical research on optimizing, a model combining query improvement and query expansion is proposed. Based on the improvement of the query, our method uses LTR to reorder the query expansion words, so that the original query can retrieve the document with higher accuracy. Only by biological field resources, the query expansion can obtain extended words, but it can not accurately describe the degree of relevance between extended words and query words. Introduce LTR methods can fully consider the relevance of extended words and original queries. Improve the inaccurate problem of single-extension. The experimental results show that compared with traditional algorithms, this algorithm improves retrieval efficiency by 3.31% on average.