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Semantic Bot: A Semantic Question Answering System for Raw Documents

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dc.contributor.author Anchelo, Adrian
dc.date.accessioned 2024-03-14T04:06:17Z
dc.date.available 2024-03-14T04:06:17Z
dc.date.issued 2023
dc.identifier.citation Anchelo, Adrian (2023) Semantic Bot: A Semantic Question Answering System for Raw Documents. BSc. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 20191176
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/1898
dc.description.abstract "Semantic searches allow users to retrieve relevant information from raw data. Advancement in embeddings such as transformer models have made the semantic retrieval more efficient. However, the use of transformers requires more resources than a lexical search. And retrieval systems with lexical search tend to be inaccurate. Therefore, creating a retrieval pipeline for a raw document with a decent speed and accuracy is difficult. Sentence transformers are the advancement in transformers that are more suited for semantic search. Combination of several transformer architectures will further improve the accuracy of retrieval. Integrating sentence encoders along with word-based architectures will Improve the speed of retrieval architectures. This research presents ensembled semantic retrieval technique to search a raw PDF with low latency. A novel combination of word embeddings with sentence encoders to retrieve information is introduced by this research." en_US
dc.language.iso en en_US
dc.publisher IIT en_US
dc.subject Question Answering System en_US
dc.subject Semantic Textual Similarity en_US
dc.subject Sentence Encoders en_US
dc.title Semantic Bot: A Semantic Question Answering System for Raw Documents en_US
dc.type Thesis en_US


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