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http://hdl.handle.net/10603/397690
Title: | Entity based query processing for retrieval and summarization in biomedical domain |
Researcher: | Sankhavara, Jainisha |
Guide(s): | Majumder, Prasenjit |
Keywords: | Engineering and Technology Engineering Engineering Biomedical Database searching Information retrieval Semantic networks (Information theory) |
University: | Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT) |
Completed Date: | 2021 |
Abstract: | quotExponential growth of biomedical literature poses different challenges in searching. To address complex information needs of the users, rigorous semantic processing of biomedical text is required. Biomedical information access emerges out as a new discipline for this reason. Traditional information access methods of matching, ranking, entity processing, entity-entity relationship processing, etc. are challenged in this domain. These are the major building blocks used to frame queries that represent complex information need in the area of biomedical and clinical information access. This thesis aims to do query processing using different IR and bioNLP techniques and to study their effects in retrieval and summarization. Various techniques of biomedical query reformulations are carried out and compared for biomedical document retrieval. Query expansion is one query reformulation technique which was carried out using relevance feedback and pseudo relevance feedback for biomedical document retrieval. Relevance feedback approach uses information regarding actual relevant documents to the query for feedback while pseudo relevance feedback approach does not have such information and uses top retrieved documents for feedback as they are assumed to be relevant to the query. One combined approach of relevance feedback and pseudo relevance feedback has been proposed which is based on feedback document discovery and uses various classification and clustering techniques on biomedical documents newlineto identify good document for feedback. This approach uses relevance feedback for a number of documents and tries to learn relevance for other documents for feedback. This feedback document discovery based query expansion approach shows improvement over relevance feedback based query expansion technique for biomedical document retrieval. newlineAn improved version of this feedback document discovery based query expansion approach where the features of entities are weighted based on the type of the entities and query is also proposed which... |
Pagination: | xi, 115 p. |
URI: | http://hdl.handle.net/10603/397690 |
Appears in Departments: | Department of Information and Communication Technology |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 73.2 kB | Adobe PDF | View/Open |
02_decalaration and certificate.pdf | 96.98 kB | Adobe PDF | View/Open | |
03_acknowledgments.pdf | 47.73 kB | Adobe PDF | View/Open | |
04_contents.pdf | 53.48 kB | Adobe PDF | View/Open | |
05_abstract.pdf | 50.33 kB | Adobe PDF | View/Open | |
06_list of tables and list of figures.pdf | 70.7 kB | Adobe PDF | View/Open | |
07_chapter 1.pdf | 221.25 kB | Adobe PDF | View/Open | |
08_chapter 2.pdf | 316.71 kB | Adobe PDF | View/Open | |
09_chapter 3.pdf | 847.63 kB | Adobe PDF | View/Open | |
10_chapter 4.pdf | 1.36 MB | Adobe PDF | View/Open | |
11_chapter 5.pdf | 205.92 kB | Adobe PDF | View/Open | |
12_chapter 6.pdf | 49.64 kB | Adobe PDF | View/Open | |
13_references.pdf | 114.16 kB | Adobe PDF | View/Open | |
14_list of publications.pdf | 62.88 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 83.07 kB | Adobe PDF | View/Open |
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