| dc.description.abstract | In today's society, the spread of incorrect information is becoming an increasingly serious issue. Verifying information using unstructured data, such as a text corpus, can be difficult for all claims since they lack semantic understanding and are difficult to justify. As a result, it is important to validate the claim using a knowledge base that can offer logic and is trustworthy. In this thesis, we suggest a baseline and advanced method to verify claims from a knowledge base to prevent the problem of lack of semantic knowledge. Pre-trained deep learning models, like T5, are fine-tuned to verify the claims using knowledge bases. Lastly, analysis and results obtained from the predictions made by the models are discussed. | |