Semantic Search and Question-Answering Systems

dc.contributor.advisorPant, Aniruddhaen_US
dc.contributor.authorPARMAR, PURVAen_US
dc.contributor.departmentDept. of Data Scienceen_US
dc.contributor.registration20181081en_US
dc.date.accessioned2023-05-12T04:39:07Z
dc.date.available2023-05-12T04:39:07Z
dc.date.issued2023-05en_US
dc.description.abstractKeyword Search Systems have been in wide use since the availability of computers themselves. Keyword search returns the relevant results which contain the exact keywords used in a search query. But in today’s world of extensive information, a simple keyword search is bound to miss a lot of other relevant results which are phrased or written differently. This leads us to develop Semantic Search systems, which can also consider the context and meaning behind a search query, and return results which may not have the exact keywords, but still are relevant because of the intended meaning. This can be taken one-step further by introducing (Extractive) Question-Answering Systems, which if given a question-like query, can directly extract the short answer from the massive wealth of existing information. We explore the development and implementation of such a Semantic Search and Question-Answering system in the domain of finance for a financial product, AlgoFabric, at AlgoAnalytics Pvt. Ltd.en_US
dc.description.embargoOne Yearen_US
dc.identifier.citation55en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7827
dc.language.isoenen_US
dc.subjectNatural Language Processingen_US
dc.subjectSearch Systemsen_US
dc.subjectInformation Retrievalen_US
dc.subjectQuestion-Answeringen_US
dc.titleSemantic Search and Question-Answering Systemsen_US
dc.typeThesisen_US
dc.type.degreeBS-MSen_US

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