@inproceedings{tuparova-etal-2023-next,
title = "{NEXT}: An Event Schema Extension Approach for Closed-Domain Event Extraction Models",
author = "Tuparova, Elena and
Ivanov, Petar and
Tagarev, Andrey and
Boytcheva, Svetla and
Koychev, Ivan",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Yeniterzi, Reyyan and
Y{\"o}r{\"u}k, Erdem and
Slavcheva, Milena},
booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.case-1.16",
pages = "113--123",
abstract = "Event extraction from textual data is a NLP research task relevant to a plethora of domains. Most approaches aim to recognize events from a predefined event schema, consisting of event types and their corresponding arguments. For domains, such as disinformation, where new event types emerge frequently, there is a need to adapt such fixed event schemas to accommodate for new event types. We present NEXT (New Event eXTraction) - a resource-sparse approach to extending a close-domain model to novel event types, that requires a very small number of annotated samples for fine-tuning performed on a single GPU. Furthermore, our results suggest that this approach is suitable not only for extraction of new event types, but also for recognition of existing event types, as the use of this approach on a new dataset leads to improved recall for all existing events while retaining precision.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tuparova-etal-2023-next">
<titleInfo>
<title>NEXT: An Event Schema Extension Approach for Closed-Domain Event Extraction Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Tuparova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Petar</namePart>
<namePart type="family">Ivanov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrey</namePart>
<namePart type="family">Tagarev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Svetla</namePart>
<namePart type="family">Boytcheva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Koychev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ali</namePart>
<namePart type="family">Hürriyetoğlu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hristo</namePart>
<namePart type="family">Tanev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vanni</namePart>
<namePart type="family">Zavarella</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Reyyan</namePart>
<namePart type="family">Yeniterzi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erdem</namePart>
<namePart type="family">Yörük</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Milena</namePart>
<namePart type="family">Slavcheva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Event extraction from textual data is a NLP research task relevant to a plethora of domains. Most approaches aim to recognize events from a predefined event schema, consisting of event types and their corresponding arguments. For domains, such as disinformation, where new event types emerge frequently, there is a need to adapt such fixed event schemas to accommodate for new event types. We present NEXT (New Event eXTraction) - a resource-sparse approach to extending a close-domain model to novel event types, that requires a very small number of annotated samples for fine-tuning performed on a single GPU. Furthermore, our results suggest that this approach is suitable not only for extraction of new event types, but also for recognition of existing event types, as the use of this approach on a new dataset leads to improved recall for all existing events while retaining precision.</abstract>
<identifier type="citekey">tuparova-etal-2023-next</identifier>
<location>
<url>https://aclanthology.org/2023.case-1.16</url>
</location>
<part>
<date>2023-09</date>
<extent unit="page">
<start>113</start>
<end>123</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NEXT: An Event Schema Extension Approach for Closed-Domain Event Extraction Models
%A Tuparova, Elena
%A Ivanov, Petar
%A Tagarev, Andrey
%A Boytcheva, Svetla
%A Koychev, Ivan
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yeniterzi, Reyyan
%Y Yörük, Erdem
%Y Slavcheva, Milena
%S Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F tuparova-etal-2023-next
%X Event extraction from textual data is a NLP research task relevant to a plethora of domains. Most approaches aim to recognize events from a predefined event schema, consisting of event types and their corresponding arguments. For domains, such as disinformation, where new event types emerge frequently, there is a need to adapt such fixed event schemas to accommodate for new event types. We present NEXT (New Event eXTraction) - a resource-sparse approach to extending a close-domain model to novel event types, that requires a very small number of annotated samples for fine-tuning performed on a single GPU. Furthermore, our results suggest that this approach is suitable not only for extraction of new event types, but also for recognition of existing event types, as the use of this approach on a new dataset leads to improved recall for all existing events while retaining precision.
%U https://aclanthology.org/2023.case-1.16
%P 113-123
Markdown (Informal)
[NEXT: An Event Schema Extension Approach for Closed-Domain Event Extraction Models](https://aclanthology.org/2023.case-1.16) (Tuparova et al., CASE-WS 2023)
ACL