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        <title>Leveraging Approximate Pattern Matching with BERT for Event Detection</title>
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            <title>Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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        <name type="personal">
            <namePart type="given">Gökçe</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Leveraging Approximate Pattern Matching with BERT for Event Detection
%A Tanev, Hristo
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F tanev-2024-leveraging
%X 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.
%U https://aclanthology.org/2024.case-1.4
%P 32-39

Markdown (Informal)

[Leveraging Approximate Pattern Matching with BERT for Event Detection](https://aclanthology.org/2024.case-1.4) (Tanev, CASE-WS 2024)

• Leveraging Approximate Pattern Matching with BERT for Event Detection (Tanev, CASE-WS 2024)

ACL

• Hristo Tanev. 2024. Leveraging Approximate Pattern Matching with BERT for Event Detection. In Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024), pages 32–39, St. Julians, Malta. Association for Computational Linguistics.