The open access journal /Information/ is arranging a Special Issue on
“Information Extraction and Language Discourse Processing” topic. As Guest
Editor for this Special Issue, we would like to invite you to contribute a
review or research article.
The submission deadline will be /10 December 2022/.
If you are interested in submitting, the journal office will do its best to
apply for the maximum discount for you.
You can see more details at the link:
https://www.mdpi.com/journal/information/special_issues/WYS02U2GTD
Yours cordially,
Dr. Jennifer D'Souza
Prof. Dr. Chengzhi Zhang
Guest Editors
*** Third Call for Papers ***
36th International Conference on Advanced Information Systems Engineering
(CAiSE'24)
June 3-7, 2024, 5* St. Raphael Resort and Marina, Limassol, Cyprus
https://cyprusconferences.org/caise2024/
(*** Submission Deadline: November 24, 2023 AoE ***)
The CAiSE’24 organization calls for full papers with a special emphasis on the theme of
Information Systems in the Age of Artificial Intelligence. Artificial Intelligence (AI) has emerged
as a transformative technology, revolutionizing various industries, and its significance in
Information Systems cannot be overstated. AI-powered systems have the potential to
streamline operations, enhance decision-making processes, and drive innovation across
organizations. From data analysis to automated processes, AI is reshaping the way we leverage
information in the digital age. The relevance of AI in IS extends beyond internal operations.
AI-powered predictive analytics enables organizations to forecast trends, anticipate customer
needs, and optimize resource allocation. This empowers businesses to adapt swiftly to
changing market dynamics, gain a competitive edge, and make proactive decisions. AI
algorithms can also detect anomalies and patterns that indicate potential security breaches,
contributing to robust cybersecurity measures in information systems. However, while
acknowledging the benefits, it is essential to consider the ethical implications of AI in
information systems. Ensuring data privacy, addressing bias in algorithms, and maintaining
transparency are vital aspects that need to be carefully managed and regulated to foster trust
and accountability.
In addition to offering an exciting scientific program, CAiSE’24 will feature a best paper award,
a journal special issue, and a PhD-thesis award:
• Best Paper Award‚ prize EUR 1000 (sponsored by Springer)
• A small selection of best papers will be invited to submit enhanced versions for
consideration in a special issue of Elsevier Information Systems journal dedicated to this
conference.
• PhD-Thesis Award
• Best PhD thesis of a past CAiSE Doctoral Consortium author (co-sponsored by the CAiSE
Steering Committee and Springer)
Papers should be submitted in PDF format. Submissions must conform to Springer‚ LNCS
format and should not exceed 15 pages, including all text, figures, references, and appendices.
Submissions not conforming to the LNCS format, exceeding 15 pages, or being obviously out
of the scope of the conference, will be rejected without review. See the guidelines here:
https://www.springer.com/comp/lncs/authors.html .
The results described must be unpublished and must not be under review elsewhere. Three to
five keywords characterizing the paper should be listed at the end of the abstract. Each paper
will be reviewed by at least two program committee members and, if positively evaluated, by
one additional program board member. The selected papers will be discussed among the paper
reviewers online and during the program board meeting. As the review process is not blind,
please indicate your name and affiliation on your submission. Accepted papers will be
presented at CAiSE’24 and published in the Springer Lecture Notes in Computer Science (LNCS)
conference proceedings.
We invite three types of original and scientific papers. The type of submission must be
indicated in the submission system. Each contribution should explicitly address the
engineering or the operation of information systems, clearly identify the information systems
problem addressed, the expected impact of the contribution to information system engineering
or operation, and the research method used. We strongly advise authors to clearly emphasize
these aspects in their paper, including the abstract.
Technical papers describe original solutions (theoretical, methodological or conceptual) in the
field of IS Engineering. A technical paper should clearly describe the situation or problem
tackled, the relevant state of the art, the position or solution suggested and its potential‚ as
well as demonstrate the benefits of the contribution through a rigorous evaluation.
Empirical papers evaluate existing problem situations including problems encountered in
practice, or validate proposed solutions with scientific means, i.e., by empirical studies,
experiments, case studies, experience reports, simulations, etc. Scientific reflection on
problems and practices in industry also falls into this category. The topic of the evaluation
presented in the paper as well as its causal or logical properties must be clearly stated. The
research method must be sound and appropriate.
Exploratory papers describe completely new research positions or approaches, in order to face
a generic situation arising because of new ICT tools, new kinds of activities, or new IS
challenges. They must precisely describe the situation and demonstrate why current methods,
tools, ways of reasoning, or meta-models are inadequate. They must also rigorously present
their approach and demonstrate its pertinence and correctness in addressing the identified
situation.
The topics of contribution include but are not limited to:
• Novel Approaches to IS Engineering
◦ Artificial Intelligence and Machine Learning
◦ Robotic Process Automation (RPA)
◦ Big Data, Data Science and Analytics
◦ Blockchain applications in IS
◦ Simulation and Digital Twins
◦ IS for collaboration and social computing
◦ Virtual reality / Augmented Reality
◦ Context-aware, autonomous and adaptive IS
• Models, Methods and Techniques in IS Engineering
◦ Ontologies and Ontology Engineering
◦ Conceptual modeling, languages and design
◦ Requirements engineering
◦ Process modeling, analysis and improvement
◦ Process automation, mining and monitoring
◦ Models and methods for evolution and reuse
◦ Domain and method engineering
◦ Product lines, variability and configuration management
◦ Compliance and alignment handling
◦ Active and interactive models
◦ Quality of IS models for analysis and design
◦ Visualization techniques in IS
◦ Decision models and business intelligence
◦ Knowledge graphs
◦ Human-centered techniques
• Architectures and Platforms for IS Engineering
◦ Distributed, mobile and open architecture
◦ Big Data architectures
◦ Cloud- and edge-based IS engineering
◦ Service oriented and multi-agent IS engineering
◦ Multi-platform IS engineering
◦ Cyber-physical systems and Internet of Things (IoT)
◦ Workflow and Process Aware Information Systems (PAIS)
◦ Handling of real time data streams
◦ Content management and semantic Web
◦ Crowdsourcing platforms
◦ Conversational agents (chatbots)
◦ Microservices design and deployment
• Domain-specific and Multi-aspect IS Engineering
◦ IT governance
◦ eGovernment
◦ Autonomous and smart systems (smart city management, smart vehicles, etc.)
◦ IS for healthcare
◦ Educational Systems and Learning Analytics
◦ Value and supply chain management
◦ Industry 4.0
◦ Sustainability and social responsibility management
◦ Privacy, security, trust, and safety management
◦ IS in the post-COVID world
Submit your paper using the Easy Chair link:
https://easychair.org/conferences/?conf=caise2024 .
IMPORTANT DATES
• Abstract Submission: 24th November 2023 (AoE)
• Paper Submission: 1st December 2023 (AoE)
• Notification of Acceptance: 23rd February 2024
• Camera-ready Papers: 5th April 2024
• Author registration: 5th April 2024
ORGANISATION
General Chairs
• Haris Mouratidis, University of Essex, UK
• Pnina Soffer, University of Haifa, Israel
Local Organizing and Finance Chair
• George A. Papadopoulos, University of Cyprus, Cyprus
Program Chairs
• Giancarlo Guizzardi, University of Twente, The Netherlands
• Flavia Maria Santoro, University of the State of Rio de Janeiro, Brazil
Other Committee Members
https://cyprusconferences.org/caise2024/committees/
Job posting: 4-year paid full-time PhD Position on Perspectivism and Disagreement in Natural Language Processing
Employer: University of Twente
Location: Enschede, Netherlands
Deadline: 4th December 2023
Application website and more details: https://utwentecareers.nl/en/vacancies/1505/phd-position-on-perspectivism-a…
Do you want to work on Natural Language Processing and aspects of fairness and representation in Machine Learning? We are looking for a highly motivated and curious PhD candidate to join the High-tech Business & Entrepreneurship Department (HBE) at the University of Twente in the Netherlands.
Your profile
- A background in Natural Language Processing;
- A strong interest in ethical and societal aspects of AI;
- You hold, or will shortly acquire, a master’s degree in Computer Science, Computational Linguistics, Information Systems, or a related discipline;
- Demonstrated programming skills in Python, JavaScript or similar languages;
- First experience with user studies is a plus;
- Capable of conducting independent research and willing to develop writing and publication skills;
- Proficient in written and oral English (C1; above IELTS 7 or equivalent);
- A positive team spirit and enthusiasm for working in an interdisciplinary and internationally oriented environment.
Our offer
We encourage high responsibility and independence, while collaborating with colleagues, researchers, other university staff and partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a full-time 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:
- Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year;
- Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
- 29 holidays per year in case of full-time employment;
- A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
- A green campus with free access to sports facilities and an international scientific community;
- A family-friendly institution that offers parental leave (both paid and unpaid);
- A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.
How to apply
Are you interested in being part of our team? Please submit your application before the 4th of December and include:
A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;
A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications.
Additional information can be acquired via email from d.braun(a)utwente.nl.
Applications can be submitted via https://utwentecareers.nl/en/vacancies/1505/phd-position-on-perspectivism-a…
Dear colleagues,
[Apologies for cross-posting]
In 2024, SIGTYP is hosting a *Shared Task on Word Embedding Evaluation for
Ancient and Historical Languages*: https://sigtyp.github.io/st2024.html The
workshop will be co-located with EACL.
*Summary*
In recent years, sets of downstream tasks called benchmarks have become a
very popular, if not default, method to evaluate general-purpose word and
sentence embeddings. Starting with decaNLP (McCann et al., 2018) and
SentEval (Conneau & Kiela, 2018), multitask benchmarks for NLU keep
appearing and improving every year. However, even the largest multilingual
benchmarks, such as XGLUE, XTREME, XTREME-R or XTREME-UP (Hu et al., 2020;
Liang et al., 2020; Ruder et al., 2021, 2023), only include modern
languages. When it comes to ancient and historical languages, scholars
mostly adapt/translate intrinsic evaluation datasets from modern languages
or create their own diagnostic tests. We argue that there is a need for a
universal evaluation benchmark for embeddings learned from ancient and
historical language data and view this shared task as a proving ground for
it.
The shared task involves solving the following problems for 12+ ancient and
historical languages that belong to 4 language families and use 6 different
scripts. Participants will be invited to describe their system in a paper
for the SIGTYP workshop proceedings. The task organisers will write an
overview paper that describes the task and summarises the different
approaches taken, and analyses their results.
*Subtasks*
For subtask A, participants are not allowed to use any additional data;
however, they can reduce and balance provided training datasets if they see
fit. For subtask B, participants are allowed to use any additional data in
any language, including pre-trained embeddings and LLMs.
A. Constrained
1. POS-tagging
2. Full morphological annotation
3. Lemmatisation
B. Unconstrained
1. POS-tagging
2. Full morphological annotation
3. Lemmatisation
4. Filling the gaps
- Word-level
- Character-level
*Data*
For tasks 1-3, we use Universal Dependencies v. 2.12 data (Zeman et al.,
2023) in 11 ancient and historical languages, complemented by 5 Old
Hungarian codices from the MGTSZ website (HAS Research Institute for
Linguistics, 2018) that are annotated to the same standard as the corpora
available through UD. For task 4, we add historical Irish data from CELT (Ó
Corráin et al., 1997), Corpas Stairiúil na Gaeilge (Acadamh Ríoga na
hÉireann, 2017), and digital editions of the St. Gall glosses (Bauer et
al., 2017) and the Würzburg glosses (Doyle, 2018) as a case study of how
performance may vary on different historical stages of the same language.
We set the upper temporal boundary to 1700 CE and do not include texts
created later than this date in our dataset. List of languages:
- Ancient Greek
- Ancient Hebrew
- Classical Chinese
- Coptic
- Gothic
- Classical, Late & Medieval Latin
- Medieval Icelandic
- Old Church Slavonic
- Old East Slavic
- Old French
- Old Hungarian
- Old, Middle & Early Modern Irish
- Vedic Sanskrit
*Important dates*
*05 Nov 2023*: Release of training and validation data
*02 Jan 2024*: Release of test data
*08 Jan 2024*: Submission of the systems
*13 Jan 2024*: Notification of results
*20 Jan 2024*: Submission of shared task papers
*27 Jan 2024*: Notification of acceptance to authors
*03 Feb 2024*: Camera-ready
*15 Mar 2024*: Video recordings due
*21/22 Mar 2024*: SIGTYP workshop
*Important links*
- *Registration form*
<https://docs.google.com/forms/d/e/1FAIpQLSdINgMfzzZGIZ-uBVQhvyndB6yeaaj-wT7…>
- Data + detailed description: https://github.com/sigtyp/ST2024
*Task organisers*
- Oksana Dereza, Insight SFI Research Centre for Data Analytics, Data
Science Institute, University of Galway
- Priya Rani, SFI Centre for Research and Training in AI, Data Science
Institute, University of Galway
- Atul Kr. Ojha, Insight SFI Research Centre for Data Analytics, Data
Science Institute, University of Galway
- Adrian Doyle, Insight SFI Research Centre for Data Analytics, Data
Science Institute, University of Galway
- Pádraic Moran, School of Languages, Literatures and Cultures, Moore
Institute, University of Galway
- John P. McCrae, Insight SFI Research Centre for Data Analytics, Data
Science Institute, University of Galway
*Contact details*
- Oksana: oksana.dereza(a)insight-centre.org
- Priya: priya.rani(a)insight-centre.org
Best wishes,
Oksana and the organisers
--
[image: https://nuig.insight-centre.org/]
<https://www.insight-centre.org/>
Oksana Dereza | PhD student on the Cardamom
<http://cardamom.insight-centre.org/> project | Unit for Linguistic Data |
Insight Centre for Data Analytics | Data Science Institute | University of
Galway
Oksana Dereza | Iarrthóir PhD ar thionscadal Cardamom
<http://cardamom.insight-centre.org/> | An tAonad um Shonraí Teangeolaíocha
| Insight, Ionad na hAnailísíochta Sonraí | Institiúid Eolaíochta Sonraí |
Ollscoil na Gaillimhe
Dear Colleagues,
I am looking for a PhD student in e-Health and/or Sign Language Translation
at the School of School of Electronics, Electrical Engineering and Computer
Science, Queen's University in Belfast, UK. The details of the project can
be found below:
1.
https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/causa…
2.
https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/endto…
Requirements: The successful candidate is expected to have a solid
background in Machine Learning, Statistics, computer science or related
discipline. The minimum academic requirement for admission to a research
degree programme is normally an Upper Second Class Honours degree from a UK
or ROI HE provider, or an equivalent qualification acceptable to the
University. Refer to the official application process for details.
Application Process: For enquiries, please send an email to Dr. Mohammed
Hasanuzzaman at m.hasanuzzaman(a)qub.ac.uk with your CV and transcript as
well as a brief description of your research interests. Unfortunately, due
to a high volume of inquiries, I may not be able to respond to all emails.
Submit your formal application through the following link.
https://dap.qub.ac.uk/portal/user/u_login.php
Your application should be clearly marked as EEECS/2024/MH1 and/or
EEECS/2024/MH2 to ensure consideration for funding
About Queen's: The Queen's University Belfast, founded almost two centuries
ago, is one of the oldest universities in the United Kingdom. As a member
of the prestigious Russell Group, Queen’s is one of the UK’s 24 leading
research-intensive universities (ranked 13th in the UK for research
intensity). Queen's has 15 subjects in the top 200 in the world (QS World
University Rankings 2023). Five of those subjects are in the World Top 100 (QS
World Rankings by subject 2023). For more details:
https://www.qub.ac.uk/Study/Why-Study-at-Queens/rankings-and-reputation/
*International students are welcome to apply, but additional funding (
https://www.qub.ac.uk/Study/international-students/international-scholarshi…)
or personal finances will be required to cover the difference between home
(UK) and overseas fees.
Best regards,
Mohammed
------------------------------------------------------------------------------------------------------
*Dr. Mohammed HasanuzzamanLecturer/Assistant Professor**Queen's University
Belfast <https://www.qub.ac.uk/>, UK *
*&Munster Technological University <https://www.mtu.ie/>, Ireland*
*Funded Investigator, ADAPT Centre- <https://www.adaptcentre.ie/> A
<https://www.adaptcentre.ie/>* World-Leading SFI Research Centre
<https://www.adaptcentre.ie/>
*C**hercheur Associé*, GREYC UMR CNRS 6072 Research Centre, France
<https://www.greyc.fr/en/home/>
*Associate Editor:*
* IEEE Transactions on Affective Computing, Nature Scientific Reports,
IEEE Transactions on Computational Social Systems, ACM TALLIP, PLOS One,
Computer Speech and Language**Website:
**https://mohammedhasanuzzaman.github.io/
<https://mohammedhasanuzzaman.github.io/>*
[image: Mailtrack]
<https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…>
Sender
notified by
Mailtrack
<https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…>
10/11/23,
14:20:50
Hi there,
Could you please distribute the following job offer? Thanks.
Best,
Pascal
-------------------------------------------------------------------------------------
We invite applications for a 3-year PhD position co-funded by Inria,
the French national research institute in Computer Science and Applied
Mathematics, and LexisNexis France, leader of legal information in
France and subsidiary of the RELX Group.
The overall objective of this project is to develop an automated
system for detecting argumentation structures in French legal
decisions, using recent machine learning-based approaches (i.e. deep
learning approaches). In the general case, these structures take the
form of a directed labeled graph, whose nodes are the elements of the
text (propositions or groups of propositions, not necessarily
contiguous) which serve as components of the argument, and edges are
relations that signal the argumentative connection between them (e.g.,
support, offensive). By revealing the argumentation structure behind
legal decisions, such a system will provide a crucial milestone
towards their detailed understanding, their use by legal
professionals, and above all contributes to greater transparency of
justice.
The main challenges and milestones of this project start with the
creation and release of a large-scale dataset of French legal
decisions annotated with argumentation structures. To minimize the
manual annotation effort, we will resort to semi-supervised and
transfer learning techniques to leverage existing argument mining
corpora, such as the European Court of Human Rights (ECHR) corpus, as
well as annotations already started by LexisNexis. Another promising
research direction, which is likely to improve over state-of-the-art
approaches, is to better model the dependencies between the different
sub-tasks (argument span detection, argument typing, etc.) instead of
learning these tasks independently. A third research avenue is to find
innovative ways to inject the domain knowledge (in particular the rich
legal ontology developed by LexisNexis) to enrich enrich the
representations used in these models. Finally, we would like to take
advantage of other discourse structures, such as coreference and
rhetorical relations, conceived as auxiliary tasks in a multi-tasking
architecture.
The successful candidate holds a Master's degree in computational
linguistics, natural language processing, machine learning, ideally
with prior experience in legal document processing and discourse
processing. Furthermore, the candidate will provide strong programming
skills, expertise in machine learning approaches and is eager to work
at the interplay between academia and industry.
The position is affiliated with the MAGNET [1], a research group at
Inria, Lille, which has expertise in Machine Learning and Natural
Language Processing, in particular Discourse Processing. The PhD
student will also work in close collaboration with the R&D team at
LexisNexis France, who will provide their expertise in the legal
domain and the data they have collected.
Applications will be considered until the position is filled. However,
you are encouraged to apply early as we shall start processing the
applications as and when they are received. Applications, written in
English or French, should include a brief cover letter with research
interests and vision, a CV (including your contact address, work
experience, publications), and contact information for at least 2
referees. Applications (and questions) should be sent to Pascal Denis
(pascal.denis(a)inria.fr).
The starting date of the position is 1 November 2022 or soon
thereafter, for a total of 3 full years.
Best regards,
Pascal Denis
[1] https://team.inria.fr/magnet/
[2] https://www.lexisnexis.fr/
--
Pascal
----
Pour une évaluation indépendante, transparente et rigoureuse !
Je soutiens la Commission d'Évaluation de l'Inria.
----
+++++++++++++++++++++++++++++++++++++++++++++++
Pascal Denis
Equipe MAGNET, INRIA Lille Nord Europe
Bâtiment B, Avenue Heloïse
Parc scientifique de la Haute Borne
59650 Villeneuve d'Ascq
Tel: ++33 3 59 35 87 24
Url: http://researchers.lille.inria.fr/~pdenis/
+++++++++++++++++++++++++++++++++++++++++++++++
[apologies for x-posting]
Postdoctoral position in computational linguistics with specialisation in language grounding to vision, robotics, and beyond
University of Gothenburg, Sweden
Project Description: The broad focus of this position is computational modelling of language (computational linguistics, natural language processing or language technology) in the context of data from other modalities such as vision, perception and action from the perspective of human-centred AI. The topics relevant for this position are computational modelling of language and perception, human-robot interaction, situated spoken dialogue systems, and computational representation of meaning (semantics).
The work will be done within the Cognitive Systems group (lead by Simon Dobnik, https://www.gu.se/en/about/find-staff/simondobnik ) which one of the four research groups within The Centre for Linguistic Theory and Studies in Probability (CLASP), https://www.gu.se/clasp and is devoted to research and advanced training in the application of probabilistic modelling and machine learning methods to core issues in linguistic theory and cognition.
The postdoctoral candidate will will have an opportunity further their scientific skills and advance the scientific filed by conducting research in collaboration with the research group by connecting ideas from several of the following areas:
- Computational semantics,
- Grounding language in action and perception,
- Generation and understanding of spatial language,
- Generation of image descriptions, visual question answering, visual dialogue
- Referring in situated dialogue,
- Situated agents / robots and instruction generation and following,
- Machine learning with neural networks,
- Cross-domain model transfer,
- Learning from small data,
- Combining top-down (expert-driven) and bottom-up (dataset-driven) models,
- Reasoning and inference including Bayesian inference,
- Model interpretation, testing and evaluation of unwanted social bias,
- Crowd-sourcing for collection and evaluation of research data.
Application Deadline: November 21, 2023 (end of the day, GMT+1).
Formal announcement and application procedure: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89… (English) and https://web103.reachmee.com/ext/I005/1035/job?site=6&lang=SE&validator=3038… (Swedish)
Contact: For more information about the research / project focus relevant to the position,
send an email to Simon Dobnik, Professor of Computational Linguistics, simon.dobnik(a)gu.se <mailto:simon.dobnik@gu.se>
For other questions,
please contact Sharid Loáiciga, Associate Senior Lecturer, +46(0) 31-786 59 42, sharid.loaiciga(a)gu.se <mailto:sharid.loaiciga@gu.se>
—
Simon Dobnik
Professor of Computational Linguistics
CLASP & FLoV, University of Gothenburg
https://www.gu.se/en/about/find-staff/simondobnik
* Apologies for cross-posting *
We are happy to announce the third UnImplicit workshop, which will be co-located with EACL 2024.
Workshop: March 21 or 22, 2024 (TBD on which of the two days)
EACL Conference: March 17-22, 2024
Website: https://unimplicit2024.github.io/
Paper submission: https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UnImplicit
* Paper submission deadline: December 18, 2023 *
* Paper submission deadline for papers with ARR Reviews: January 17, 2024 *
Real language is underspecified, vague, and ambiguous. Indeed, past work (Zipf, 1949; Piantadosi, 2012) has suggested that ambiguity may be an inextricable feature of natural language, resulting from competing communicative pressures. Resolving the meaning of language is a never-ending process of making inferences based on implicit knowledge. For example, we know that ``the girl saw the man with the telescope'' is ambiguous and could refer to two situations, while ``the girl saw the man with the hamburger'' is not, or that ``near'' in ``the house near the airport'' and ``the ant near the crumb'' does not refer to the same distance. Being able to capture this kind of knowledge is central to building systems with a human-like understanding of language, as well as providing a full account of natural language itself.
We welcome submissions related to, but not limited to, the following topics:
* Creating corpora or new annotations for underspecified, vague, or ambiguous language
* Studies of annotator disagreement
* Methods of resolving underspecification, vagueness, or ambiguity
* Studies of how multimodal settings interact with underspecification in language
* Ambiguities in non-linguistic domains, like images or videos
* Perspectives on the role of vagueness and ambiguity in NLP
Similar to the first two editions, we would accept theoretical and practical contributions (long, short, and non-archival) on all aspects related to the workshop topic.
If you are interested, you can check out the last two UnImplicit workshops held at ACL 2021<https://unimplicit.github.io/#> and NAACL 2022<https://unimplicit2022.github.io>.
Important Dates
=============
Dec. 18, 2023: Workshop paper deadline (OpenReview)
Jan. 17, 2024: Deadline to commit papers with ARR Reviews (OpenReview)
Jan. 20, 2024: Notification of Acceptance
Jan. 30, 2024: Camera-ready papers due
Mar. 21-22, 2024: Workshop Dates (TBD on which of the two days)
All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).
Submissions
==========
We invite two types of submissions:
1. Archival: long (up to 8 pages) or short (up to 4 pages) papers, with unlimited references. These papers should report on complete, original, and unpublished research and cannot be 'under submission' elsewhere. If accepted, archival papers will appear in the workshop proceedings.
2. Non-archival: Extended abstracts (up to 2 pages) or copy of submission/publication, which can take two forms: Works in progress, that are not yet mature enough for a full submission. Or already published work, or work currently under submission elsewhere, which can be submitted in the form of the original abstract and a copy of the submission/publication.
We are not enforcing any anonymity period. The workshop will run its review process, and papers can be submitted directly to OpenReview (https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UnImplicit) on Dec. 18th, 2023. It is also possible to submit a paper accompanied by reviews from the ACL Rolling Review system, or a paper that has been rejected from EACL, or a Findings paper looking for a presentation slot, by Jan. 17th, 2024 (please use the following form for this type of submission: ).
Both papers and extended abstracts must follow the EACL 2024 format.
Accepted papers and extended abstracts must be presented at the workshop and at least one author must be registered for the workshop.
Workshop organizers
==========
Valentina Pyatkin, AI2 and University of Washington
Elias Stengel-Eskin, UNC Chapel Hill
Alisa Liu, University of Washington
Sandro Pezzelle, University of Amsterdam
Daniel Fried, Carnegie Mellon University
Sandro -- on behalf of the organizing team
---
Sandro Pezzelle
ILLC - Institute for Logic, Language & Computation
University of Amsterdam
sandropezzelle.github.io<http://sandropezzelle.github.io/>
Dear Colleagues
Apologies for cross-posting.
This is Michal Ptaszynski from Kitami Institute of Technology, Japan.
We are accepting papers for the Applied Sciences journal (Impact Factor: 2.7) special issue on "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions". The new deadline for manuscript submission is March 31, 2024, but your paper will be sent for review as soon as it is submitted and will be published shortly after being accepted.
We hope you will consider submitting your paper.
https://www.mdpi.com/journal/applsci/special_issues/NTMDOE41MY
Best regards,
Michal PTASZYNSKI, Ph.D., Associate Professor
Department of Computer Science
Kitami Institute of Technology,
165 Koen-cho, Kitami, 090-8507, Japan
TEL/FAX: +81-157-26-9327
michal(a)mail.kitami-it.ac.jp
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Applied Sciences (Impact Factor: 2.7)
Special Issue on "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions"
Special Issue Information
During recent years, social infrastructure has become irreversibly linked to the Internet through its everyday manifestations, such as social networking services (Twitter, Facebook, etc.). Every second this new tangible information-based reality provides large amounts of data filled with 1) emotional expressions; 2) people's opinions on various topics; and 3) their reasoning, revealing their decision-making processes. As these three categories are also closely interrelated with each other, they should be studied together to obtain a more robust view on all of the topics involved. This, as never before, provides an opportunity for the development and application of natural language processing methods, in particular those regarding such topics as emotion processing, decision-making, and opinion mining.
For this issue, we invite high-quality papers from researchers with an interest in knowing more about those topics and their connection to the world we live in through opinion and sentiment analysis, recommendation systems, web mining, automated decision-making, etc. We also invite papers on the topic of using Natural Language Processing tools and methods to process emotions, metaphors, ethics, or other phenomena related to human activities.
List of Topics
The Special Issue will invite papers on topics listed, but not limited to the following:
- opinion mining
- decision support systems
- emotion detection
- sentiment analysis
- natural language processing
- computational linguistics
- NLP applications
- natural language generation
- emotional language processing
- humor and joke processing
- deceptive language detection
- emoticon processing
- automatic cyberbullying detection
- fake news detection
- abusive language processing
- story generation
- poetry generation
- cognitive agents
Guest Editors
Dr. Pawel Dybala
Dr. Rafal Rzepka
Dr. Michal Ptaszynski
Michal Ptaszynski
michal.ptaszynski(a)gmail.com
We invite the community to participate in the shared task we organize and
consider working on data from our previous shared tasks in the scope of the
CASE workshop @ EACL 2024 (https://emw.ku.edu.tr/case-2024/).
Recent & Active Shared task:
*T1: Climate Activism Stance and Hate Event Detection*
Hate speech detection and stance detection are some of the most important
aspects of event identification during climate change activism events. In
the case of hate speech detection, the event is the occurrence of hate
speech, the entity is the target of the hate speech, and the relationship
is the connection between the two. The hate speech event has targets to
which hate is directed. Identification of targets is an important task
within hate speech event detection. Additionally, stance event detection is
an important part of assessing the dynamics of protests and activisms for
climate change. This helps to understand whether the activist movements and
protests are being supported or opposed. This task will have three subtasks
(i) Hate speech identification (ii) Targets of Hate Speech Identification
(iii) Stance Detection.
*Codalab Link:* https://codalab.lisn.upsaclay.fr/competitions/16206
<https://codalab.lisn.upsaclay.fr/competitions/16206>
Registration: In order to register for the shared task, please send a
request in Codalab. The organizers will approve requests on a daily basis.
*GitHub Page:* https://github.com/therealthapa/case2024-climate
<https://github.com/therealthapa/case2024-climate>
*Timeline*:
Training & Evaluation data available: Nov 1, 2023
Test data available: Nov 30, 2023
Test start: Nov 30, 2023
Test end: Jan 5, 2024
System Description Paper submissions due: Jan 12, 2024
Notification to authors after review: Jan 26, 2024
Camera ready: Jan 30, 2024
CASE Workshop: 21-22 Mar, 2024
Previous shared tasks for working on regular papers (no official
competition), please see the regular paper submission timeline:
PT1: MULTILINGUAL PROTEST NEWS DETECTION
The performance of an automated system depends on the target event type as
it may be broad or potentially the event trigger(s) can be ambiguous. The
context of the trigger occurrence may need to be handled as well. For
instance, the ‘protest’ event type may be synonymous with ‘demonstration’
or not in a specific context. Moreover, the hypothetical cases such as
future protest plans may need to be excluded from the results. Finally, the
relevance of a protest depends on the actors as in a contentious political
event only citizen-led events are in the scope. This challenge becomes even
harder in a cross-lingual and zero-shot setting in case training data are
not available in new languages. We tackle the task in four steps and hope
state-of-the-art approaches will yield optimal results.
Contact person: Ali Hürriyetoğlu (ali.hurriyetoglu(a)gmail.com)
Github: https://github.com/emerging-welfare/case-2022-multilingual-event
PT2: 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 in studying event causality in the news and, therefore,
introduce the Causal News Corpus. The Causal News Corpus consists of 3,767
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 subtasks 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(a)u.nus.edu)
Github: https://github.com/tanfiona/CausalNewsCorpus
PT3: MULTIMODAL HATE SPEECH EVENT DETECTION
Hate speech detection is one of the most important aspects of event
identification during political events like invasions. In the case of hate
speech detection, the event is the occurrence of hate speech, the entity is
the target of the hate speech, and the relationship is the connection
between the two. Since multimodal content is widely prevalent across the
internet, the detection of hate speech in text-embedded images is very
important. Given a text-embedded image, this task aims to automatically
identify the hate speech and its targets. This task will have two subtasks.
Contact person: Surendrabikram Thapa (surendrabikram(a)vt.edu)
Codalab page: https://codalab.lisn.upsaclay.fr/competitions/16203
Github: https://github.com/therealthapa/case2023_task4
Note: The organizers follows a specific timeline. Please see the Codalab
page.