Theses and Dissertations

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    Query Processing in Different Domains
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2021) Mishra, Sonal; Majumder, Prasenjit
    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.
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    ItemOpen Access
    Retrieval of legal documents using query expansion
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2014) Agrawal, Madhulika; Majumder, Prasenjit
    Structure of query by a lawyer and a layman is different. Legal content in the layman’s query is very less. Thus pre-processing of these queries is required for better retrieval performance. In this thesis, we used various query expansion techniques and found that increasing query size increases system performance. MAP of 0.5034 was obtained by using BM25 retrieval model with query expansion up to 2550 terms using Bo1 query expansion model. By explicitly adding terms to the query, using topics obtained from topic modelling, a MAP value of 0.4281 was obtained. Further by relevance feedback of documents using topic modelling and only 2 cycles of feedback, we got MAP of 0.3832. Baseline result that we had was MAP value of 0.3799 using In_expC2 retrieval model. We also compared the relevance judgment of a lawyer and a non-lawyer and found out that for relative evaluation of two systems, non-lawyer’s relevance judgment is at par with the lawyer’s judgment.