SubmissionNumber#=%=#8
FinalPaperTitle#=%=#Leveraging Approximate Pattern Matching with BERT for Event Detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#We describe a new weakly supervised method for  sentence-level event detection, based exclusively on  linear prototype patterns like "people got sick" or "a roadside bomb killed people". We propose a new BERT based algorithm for approximate pattern matching to identify event phrases, semantically similar to these prototypes. 

To the best of our knowledge, a similar approach has not been used in the context of event detection. We experimented with two event corpora in the area of disease outbreaks and terrorism and we achieved promising results in sentence level event identification: 0.78 F1 score for new disease cases detection  and 0.68 F1 in detecting terrorist attacks. Results were in line with some state-of-the-art systems.
Author{1}{Firstname}#=%=#Hristo
Author{1}{Lastname}#=%=#Tanev
Author{1}{Username}#=%=#htanev
Author{1}{Email}#=%=#htanev@gmail.com
Author{1}{Affiliation}#=%=#Joint Research Centre, European Commission

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