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Deep Learning Approaches for Question Answering System

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dc.contributor.author Sharma, Yashvardhan
dc.date.accessioned 2023-01-02T11:04:33Z
dc.date.available 2023-01-02T11:04:33Z
dc.date.issued 2018
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S1877050918308226
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8228
dc.description.abstract Question Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Consequently, the field is one of the most researched fields in computer science today. The last few years have seen considerable developments and improvement in the state of the art, much of which can be credited to upcoming of Deep Learning. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Computer Science en_US
dc.subject Coattention en_US
dc.subject Deep Learning en_US
dc.subject Memory nets en_US
dc.subject Neural networks en_US
dc.subject Question answering en_US
dc.title Deep Learning Approaches for Question Answering System en_US
dc.type Article en_US


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