32.100, Calls: Comp Ling, Gen Ling, Lang Doc, Text/Corpus Ling/Thailand

[The LINGUIST List]

LINGUIST List: Vol-32-100. Thu Jan 07 2021. ISSN: 1069 - 4875.

Subject: 32.100, Calls: Comp Ling, Gen Ling, Lang Doc, Text/Corpus Ling/Thailand

Moderator: Malgorzata E. Cavar (linguist at linguistlist.org)
Student Moderator: Jeremy Coburn
Managing Editor: Becca Morris
Team: Helen Aristar-Dry, Everett Green, Sarah Robinson, Lauren Perkins, Nils Hjortnaes, Yiwen Zhang, Joshua Sims
Jobs: jobs at linguistlist.org | Conferences: callconf at linguistlist.org | Pubs: pubs at linguistlist.org

Homepage: http://linguistlist.org

Please support the LL editors and operation with a donation at:
           https://funddrive.linguistlist.org/donate/

Editor for this issue: Lauren Perkins <lauren at linguistlist.org>
================================================================


Date: Thu, 07 Jan 2021 16:04:01
From: Ali Hürriyetoğlu [ahurriyetoglu at ku.edu.tr]
Subject: Challenges and Applications of Automated Extraction of Socio-political Events from Text @ACL-IJCNLP

 
Full Title: Challenges and Applications of Automated Extraction of Socio-political Events from Text @ACL-IJCNLP 
Short Title: CASE 

Date: 05-Aug-2021 - 06-Aug-2021
Location: Bangkok, Thailand 
Contact Person: Ali Hürriyetoğlu
Meeting Email: ahurriyetoglu at ku.edu.tr
Web Site: https://emw.ku.edu.tr/case-2021/ 

Linguistic Field(s): Computational Linguistics; General Linguistics; Language Documentation; Text/Corpus Linguistics 

Call Deadline: 26-Apr-2021 

Meeting Description:

Today, 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. The need for precise and high-quality information
about a wide variety of events ranging from political violence, environmental
catastrophes, and conflict, to international economic and health crises has
rapidly escalated (Porta and Diani, 2015; Coleman et al. 2014). Governments,
multilateral organizations, local and global NGOs, and social movements
present an increasing demand for this data 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. For instance, Black Lives Matter
protests[1] and conflict in Syria[2] events are only two examples where we
must understand, analyze, and improve the real-life situations using such
data.

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, and developing algorithmic approaches
(Pustojevsky et al. 2003; Boroş, 2018; Chen et al. 2021). Social and political
scientists have been working to create socio-political event 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) and NLP methods to deal better with
the vast amount and variety of data in this domain (Hürriyetoğlu et al. 2020).
Automation offers scholars not only the opportunity to improve existing
practices, but also to vastly expand the scope of data that can be collected
and studied, thus potentially opening up new research frontiers within the
field of socio-political events, such as political violence & social
movements. But automated approaches as well suffer from major issues like
bias, 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 socio-political event
information collection may not be comparable to each other or not of
sufficient quality (Wang et al. 2016; Schrodt 2020).

Socio-political events are varied and nuanced. Both the political context and
the local language used may affect whether and how they are reported.
Therefore, all steps of information collection (event definition, language
resources, and manual or algorithmic steps) may need to be constantly updated,
leading to a series of challenging questions: Do events related to minority
groups are represented well? Are new types of events covered? Are the event
definitions and their operationalization comparable across systems
(Hürriyetoğlu 2019, 2020a, 2020b)? This workshop aims to seek answers to these
kind of questions, to inspire innovative technological and scientific
solutions for tackling the aforementioned issues, and to quantify the quality
of the automated event extraction systems. Moreover, the workshop will trigger
a deeper understanding of the performance of the computational tools used and
the usability of the resulting socio-political event datasets.


Call for Papers: 

We invite contributions from researchers in computer science, NLP, ML, AI,
socio-political sciences, conflict analysis and forecasting, peace studies, as
well as computational social science scholars involved in the collection and
utilization of socio-political event data. Social and political scientists
will be interested in reporting and discussing their approaches and observe
what the state-of-the-art text processing systems can achieve for their
domain. Computational scholars will have the opportunity to illustrate the
capacity of their approaches in this domain and benefit from being challenged
by real-world use cases. 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 is not a
comparable effort for handling socio-political events. We hope to fill this
gap and contribute to social and political sciences in a similar spirit. We
invite work on all aspects of automated coding of socio-political events from
mono- or multi-lingual text sources. This includes (but is not limited to) the
following topics:
 - Extracting events in and beyond a sentence
 - Training data collection and annotation processes
 - Event coreference detection
 - Event-event relations, e.g., subevents, main events, causal relations
 - Event dataset evaluation in light of reliability and validity metrics
 - Defining, populating, and facilitating event schemas and ontologies
 - Automated tools and pipelines for event collection related tasks
 - Lexical, Syntactic, and pragmatic aspects of event information
manifestation
 - Development and analysis of rule-based, ML, hybrid, and human-in-the-loop
approaches for creating event datasets
 - COVID-19 related socio-political events
 - Applications of event databases
 - Online social movements
 - Bias and fairness of the sources and event datasets
 - Estimating what is missing in event datasets using internal and external
information
 - Novel event detection
 - Release of new event datasets
 - Ethics, misinformation, privacy, and fairness concerns pertaining to event
datasets
 - Copyright issues on event dataset creation, dissemination, and sharing
 - Qualities of the event information on various online and offline platforms

Submissions: 
This call solicits full papers reporting original and unpublished research on
the topics listed above. The papers should emphasize obtained results rather
than intended work and should indicate clearly the state of completion of the
reported results. Submissions should be between 4 and 8 pages in total.

Authors are also invited to submit short papers not exceeding 4 pages (plus
two additional pages for references). Short papers should describe:
 - a small, focused contribution;
 - work in progress; 
 - a negative result;
 - a position paper.
 - a report on shared task participation.

Papers should be submitted on the START page of the workshop (link:TBD) in PDF
format, in compliance with the ACL 2021 author guidelines provided on
https://2021.aclweb.org/calls/papers.

The reviewing process will be double blind and papers should not include the
authors’ names and affiliations. Each submission will be reviewed by at least
three members of the program committee. If you do include any author names on
the title page, your submission will be automatically rejected. In the body of
your submission, you should eliminate all direct references to your own
previous work.

Workshop Proceedings will be published on ACL Anthology.




------------------------------------------------------------------------------

***************************    LINGUIST List Support    ***************************
 The 2020 Fund Drive is under way! Please visit https://funddrive.linguistlist.org
  to find out how to donate and check how your university, country or discipline
     ranks in the fund drive challenges. Or go directly to the donation site:
                   https://crowdfunding.iu.edu/the-linguist-list

                        Let's make this a short fund drive!
                Please feel free to share the link to our campaign:
                    https://funddrive.linguistlist.org/donate/
 


----------------------------------------------------------
LINGUIST List: Vol-32-100	
----------------------------------------------------------