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        <title>Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch</title>
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        <namePart type="given">Thierry</namePart>
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    <name type="personal">
        <namePart type="given">Veronique</namePart>
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    <abstract>A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system. Current typology-based event detection approaches fail to handle the variety of events expressed in real-world situations. To overcome this, we aim to perform event salience classification and explore whether a transformer model is capable of classifying new information into less and more general prominence classes. After comparing a Support Vector Machine (SVM) baseline and our transformer-based classifier performances on several event span formats, we conceived multi-word event spans as syntactic clauses. Those are fed into our prominence classifier which is fine-tuned on pre-trained Dutch BERT word embeddings. On top of that we outperform a pipeline of a Conditional Random Field (CRF) approach to event-trigger word detection and the BERT-based classifier. To the best of our knowledge we present the first event extraction approach that combines an expert-based syntactic parser with a transformer-based classifier for Dutch.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.40</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch
%A Desot, Thierry
%A De Clercq, Orphee
%A Hoste, Veronique
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F desot-etal-2021-event
%X A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system. Current typology-based event detection approaches fail to handle the variety of events expressed in real-world situations. To overcome this, we aim to perform event salience classification and explore whether a transformer model is capable of classifying new information into less and more general prominence classes. After comparing a Support Vector Machine (SVM) baseline and our transformer-based classifier performances on several event span formats, we conceived multi-word event spans as syntactic clauses. Those are fed into our prominence classifier which is fine-tuned on pre-trained Dutch BERT word embeddings. On top of that we outperform a pipeline of a Conditional Random Field (CRF) approach to event-trigger word detection and the BERT-based classifier. To the best of our knowledge we present the first event extraction approach that combines an expert-based syntactic parser with a transformer-based classifier for Dutch.
%U https://aclanthology.org/2021.ranlp-1.40
%P 346-357

Markdown (Informal)

[Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch](https://aclanthology.org/2021.ranlp-1.40) (Desot et al., RANLP 2021)

• Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch (Desot et al., RANLP 2021)

ACL

• Thierry Desot, Orphee De Clercq, and Veronique Hoste. 2021. Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 346–357, Held Online. INCOMA Ltd..