Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7827
Title: Semantic Search and Question-Answering Systems
Authors: Pant, Aniruddha
PARMAR, PURVA
Dept. of Data Science
20181081
Keywords: Natural Language Processing
Search Systems
Information Retrieval
Question-Answering
Issue Date: May-2023
Citation: 55
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.
URI: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7827
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