Query Processing in Different Domains

Abstract

In this modern era, digital content is exploding in every domain. Biomedical domain is also no exception.In this modern era, digital content is exploding in every domain. Biomedical domain is also no exception. Finding potentially relevant medical documents that can help to diagnose a particular disease is a challenging problem with the increase in biomedical documents over time. The medical queries are usually short and often contains just three to four words. The queries usually contain disease name, genetic variant, treatment for the disease.The law queries usually describe a situation and the documents that are retrieved belong to the Prior Cases document collections. Various methods of pre-retrieval query expansion is explored like word embeddings. These word embeddings are made from existing PubMed articles that are provided in the document collection. The set of experiments are performed on TREC 2018 and TREC 2020 datatsets. A detailed description has been provided in the thesis about these experiments and retrieval systems, as well as about the intuition behind the building the models. In this thesis we propose a cross relevance language model which is effective in finding potentially relevant biomedical documents from a biomedical document collection. Experiments on TREC 2018 and 2019 precision medicine track and FIRE AILA 2019 Track show that our proposed cross relevance language model is more effective compared to existing standard relevance language model for medical document retrieval.