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hello everyone my name is chan kang li and today iamp;#39;ll be giving an overview of the false negative denoising framework that we came up with for distantly supervised relation extraction tasks it is a paper coming out in the findings of acl 2021 and itamp;#39;s authored by chen itamp;#39;s rayfu myself and my advisor wayuma at the institute of information science at academia cynica the problems that people often face when trying this off information extraction tasks using deep learning is the lack of label training data and the general high cost that comes with trying to generate their data set of their own distance supervision was proposed as a efficient way to generate large quantity of training data by aligning triples in a knowledge base to a large unannotated corpus distance supervision introduces both false positive and false negative noise the third sentence here is an example of false negative noise it is only labeled negative since the triple is absent in the knowledge
