33.1491, Calls: Computational Linguistics/United Arab Emirates
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LINGUIST List: Vol-33-1491. Tue Apr 26 2022. ISSN: 1069 - 4875.
Subject: 33.1491, Calls: Computational Linguistics/United Arab Emirates
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Date: Tue, 26 Apr 2022 22:40:07
From: Ali Hürriyetoğlu [ali.hurriyetoglu at gmail.com]
Subject: Call for Papers and Shared Task Participation (CASE @ EMNLP 2022): Challenges and Applications of Automated Extraction of Socio-political Events from Text
Full Title: Call for Papers and Shared Task Participation (CASE @ EMNLP 2022): Challenges and Applications of Automated Extraction of Socio-political Events from Text
Short Title: CASE @ EMNLP 2022
Date: 07-Dec-2022 - 08-Dec-2022
Location: Abu Dhabi, United Arab Emirates
Contact Person: Ali Hürriyetoğlu
Meeting Email: ali.hurriyetoglu at gmail.com
Web Site: https://emw.ku.edu.tr/case-2022/
Linguistic Field(s): Computational Linguistics
Call Deadline: 07-Sep-2022
Meeting Description:
Nowadays, the unprecedented quantity of easily accessible data on social,
political, and economic processes offers ground-breaking potential in guiding
data-driven analysis in social and human sciences and in driving informed
policy-making processes. Governments, multilateral organizations, and local
and global NGOs present an increasing demand for high-quality information
about a wide variety of events ranging from political violence, environmental
catastrophes, and conflict, to international economic and health crises
(Coleman et al. 2014; Porta and Diani, 2015) to prevent or resolve conflicts,
provide relief for those that are afflicted, or improve the lives of and
protect citizens in a variety of ways. Black Lives Matter protests
(http://protestmap.raceandpolicing.com) and conflicts in Syria
(https://www.cartercenter.org/peace/conflict_resolution/syria-conflict-resolut
ion.html) are only two examples where we must understand, analyze, and improve
real-life situations using such data. Finally, these efforts respond to
“growing public interest in up-to-date information on crowds” as well
(https://sites.google.com/view/crowdcountingconsortium/faqs).
Event extraction has long been a challenge for the natural language processing
(NLP) community as it requires sophisticated methods in defining event
ontologies, creating language resources, domain specific grammars, developing
Machine Learning models and other algorithmic approaches for various
event-detection- specific tasks, such entity detection, semantic labeling,
event classification and clustering and others (Pustojevsky et al. 2003;
Boroş, 2018; Chen et al. 2021). Social and political scientists have been
working to create socio-political event (SPE) databases such as ACLED, EMBERS,
GDELT, ICEWS, MMAD, PHOENIX, POLDEM, SPEED, TERRIER, and UCDP following
similar steps for decades. These projects and the new ones increasingly rely
on machine learning (ML), deep learning (DL), and NLP methods to deal better
with the vast amount and variety of data in this domain (Hürriyetoğlu et al.
2020). Unfortunately, automated approaches suffer from major issues like bias,
limited generalizability, class imbalance, training data limitations, and
ethical issues that have the potential to affect the results and their use
drastically (Lau and Baldwin 2020; Bhatia et al. 2020; Chang et al. 2019).
Moreover, the results of the automated systems for SPE information collection
have neither been comparable to each other nor been of sufficient quality
(Wang et al. 2016; Schrodt 2020). SPEs are varied and nuanced. Both the
political context and the local language used may affect whether and how they
are reported.
Academic workshops specific to tackling event information in general or for
analyzing text in specific domains such as health, law, finance, and
biomedical sciences have significantly accelerated progress in these topics
and fields, respectively. However, there has not been a comparable effort for
handling SPEs. We fill this gap.
*** Keynotes ***
Three prominent scholars have accepted our invitation as keynote speakers:
i) J. Craig Jenkins (https://sociology.osu.edu/people/jenkins.12) is Academy
Professor Emeritus of Sociology at The Ohio State University. He directed the
Mershon Center for International Security Studies from 2011 to 2015 and is now
senior research scientist.
ii) Scott Althaus (https://pol.illinois.edu/directory/profile/salthaus) is
Merriam Professor of Political Science, Professor of Communication, and
Director of the Cline Center for Advanced Social Research at the University of
Illinois Urbana-Champaign.
iii) Thien Huu Nguyen (https://ix.cs.uoregon.edu/~thien/) is an assistant
professor in the Department of Computer and Information Science at the
University of Oregon. Thien is the director of the NSF IUCRC Center for Big
Learning (CBL) at the University of Oregon.
Call for Papers:
We invite contributions from researchers in computer science, NLP, ML, DL, AI,
socio-political sciences, conflict analysis and forecasting, peace studies, as
well as computational social science scholars involved in the collection and
utilization of SPE data.
This includes (but is not limited to) the following topics
1) Extracting events in and beyond a sentence, event coreference resolution,
2) New datasets, training data collection, and annotation for event
information,
3) Event-event relations, e.g., subevents, main events, causal relations,
4) Event dataset evaluation in light of reliability and validity metrics,
5) Defining, populating, and facilitating event schemas and ontologies,
6) Automated tools and pipelines for event collection related tasks,
7) Lexical, syntactic, discursive, and pragmatic aspects of event
manifestation,
8) Methodologies for development, evaluation, and analysis of event datasets,
9) Applications of event databases, e.g. early warning, conflict prediction,
policymaking,
10) Estimating what is missing in event datasets using internal and external
information,
11) Detection of new SPE types, e.g. creative protests, cyberactivism, COVID19
related,
12) Release of new event datasets,
13) Bias and fairness of the sources and event datasets,
14) Ethics, misinformation, privacy, and fairness concerns pertaining to event
datasets, and
15) Copyright issues on event dataset creation, dissemination, and sharing.
16) We encourage submissions of new system description papers on our available
benchmarks (ProtestNews @ CLEF 2019, AESPEN @ LREC 2020, and CASE @ 2021).
Please contact the organizers if you would like to access the data.
The proceedings of the previous editions should be indicative of what we
cover: ProtestNews @ CLEF 2019 (http://ceur-ws.org/Vol-2380/), AESPEN @ ACL
2020 (https://aclanthology.org/volumes/2020.aespen-1/), CASE @ ACL-IJCNLP 2021
(https://aclanthology.org/volumes/2021.case-1/).
**** Shared tasks ****
Task 1- Multilingual protest news detection: This is the same shared task
organized at CASE 2021 (For more info:
https://aclanthology.org/2021.case-1.11/) But this time there will be
additional data and languages at the evaluation stage. Contact person: Ali
Hürriyetoğlu (ali.hurriyetoglu at gmail.com). Github:
https://github.com/emerging-welfare/case-2022-multilingual-event
Task 2- Automatically replicating manually created event datasets: The
participants of Task 1 will be invited to run the systems they will develop to
tackle Task 1 on a news archive (For more info
https://aclanthology.org/2021.case-1.27/). Contact person: Hristo Tanev
(htanev at gmail.com). Github:
https://github.com/emerging-welfare/case-2022-multilingual-event
Task 3- Event causality identification: Causality is a core cognitive concept
and appears in many natural language processing (NLP) works that aim to tackle
inference and understanding. We are interested to study event causality in
news, and therefore, introduce the Causal News Corpus. The Causal News Corpus
consists of 3,559 event sentences, extracted from protest event news, that
have been annotated with sequence labels on whether it contains causal
relations or not. Subsequently, causal sentences are also annotated with
Cause, Effect, and Signal spans. Our two subtasks (Sequence Classification and
Span Detection) work on the Causal News Corpus, and we hope that accurate,
automated solutions may be proposed for the detection and extraction of causal
events in news. Contact person: Fiona Anting Tan (tan.f at u.nus.edu). Github:
https://github.com/tanfiona/CausalNewsCorpus
Please follow the workshop page for the updates or contact the contact person
related to the task you are interested in. The important dates for the tasks
are i) Training and validation data available: April 15, 2022, ii) Validation
labels and test data available: August 01, 2022, iii) Test period: August
01-15, 2022.
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