Semantic Search and Question-Answering Systems
| dc.contributor.advisor | Pant, Aniruddha | en_US |
| dc.contributor.author | PARMAR, PURVA | en_US |
| dc.contributor.department | Dept. of Data Science | en_US |
| dc.contributor.registration | 20181081 | en_US |
| dc.date.accessioned | 2023-05-12T04:39:07Z | |
| dc.date.available | 2023-05-12T04:39:07Z | |
| dc.date.issued | 2023-05 | en_US |
| dc.description.abstract | Keyword 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.embargo | One Year | en_US |
| dc.identifier.citation | 55 | en_US |
| dc.identifier.uri | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7827 | |
| dc.language.iso | en | en_US |
| dc.subject | Natural Language Processing | en_US |
| dc.subject | Search Systems | en_US |
| dc.subject | Information Retrieval | en_US |
| dc.subject | Question-Answering | en_US |
| dc.title | Semantic Search and Question-Answering Systems | en_US |
| dc.type | Thesis | en_US |
| dc.type.degree | BS-MS | en_US |
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