SALLD-2 @ LREC 2022 – 2nd Workshop on Sentiment Analysis & Linguistic Linked Data – Call for Papers

The SALLD-2 half-day workshop will be held in conjunction with LREC 2022 in Marseille, France, on June 24, 2022.
It will provide a discussion forum about usage of Linguistic Linked Data principles in the Sentiment Analysis field, to explore relevant principles, methodologies, resources, tools and applications, and understand the primary approaches, their advantages, limitations, and the available resources and case studies. It follows on SALLD-1, which was held in conjunction with LDK 2021 – 3rd Conference on Language, Data and Knowledge – in Zaragoza, Spain on September 1, 2021. The SALLD series was initiated in the framework of NexusLinguarum COST Action – European network for Web-centred linguistic data science (CA 18209) and has its support.

SALLD-2 comprises an invited talk by Anna Fensel, Associate Professor at the University of Innsbruck, Austria and Wageningen University & Research, the Netherlands, as well as oral presentations, demo session, and concluding panel. The main topics are the following:

Sentiment and Emotion Analysis and Generation
Linguistic Linked (Open) Data
Natural Language Processing and AI
Semantic Web and the Web of Data
Lexical/Textual Resource modelling, annotation, and conversion to Linked Data
Metadata, Categories, Workflows, Keywords, and Standards
Understanding and predicting user and group behavior
Offensive language identification, classification, and interaction with polarity and emotion
Sentiment-based identification of radicalization and misinformation

We invite you to submit a 1,000-word abstract to https://softconf.com/lrec2022/SALLD-2.
Accepted proposals will be invited to submit a full (short or long) paper to be included in the LREC proceedings. Templates and author instructions are available here. Please note these important dates:

April 8, 2022 Abstract submission deadline
April 29, 2022 Notification
May 27, 2022 Full paper submission deadline



Review Committee

Valerio Basile, University of Turin
Paul Buitelaar, NUI Galway
Davide Buscaldi, Université Paris 13 Sorbonne
Erik Cambria, Nanyang Technological University
Sara Carvalho, Universidade de Aveiro
Hugo Gonçalo Oliveira, University of Coimbra
Carlos A. Iglesias, Universidad Politécnica de Madrid
Ilan Kernerman, K Dictionaries – Lexicala
Barbara Lewandowska-Tomaszczyk, State University of Applied Sciences, Konin
Chaya Liebeskind, Jerusalem College of Technology
Francesco Mambrini, Università Cattolica del Sacro Cuore
Kiemute Oyibo, University of Waterloo
Marco Carlo Passarotti, Università Cattolica del Sacro Cuore
Marco Respocher, University of Verona
Rachele Sprugnoli, Universitá Cattolica del Sacro Cuore
Dimitar Trajanov, ss. Cyril and Methodius University, Skopje
Slavko Žitnik, University of Ljubljana
Arkaitz Zubiaga, Queen Mary University of London

Organizers

Ilan Kernerman, K Dictionaries – Lexicala
Sara Carvalho, Universidade de Aveiro
Carlos A. Iglesias, Universidad Politécnica de Madrid

Rachele Sprugnoli, Università Cattolica del Sacro Cuore

Website and Contact
https://salld.org/salld-2
SALLD-2_lrec2022@softconf.com

Identify, Describe, and Share your LRs!
Describing your LRs in the LRE Map is now a normal practice in the submission procedure of LREC (introduced in 2010 and adopted by other conferences). To continue the efforts initiated at LREC 2014 about “Sharing LRs” (data, tools, web-services, etc.), authors will have the possibility, when submitting a paper, to upload LRs in a special LREC repository. This effort of sharing LRs, linked to the LRE Map for their description, may become a new “regular” feature for conferences in our field, thus contributing to creating a common repository where everyone can deposit and share data.
As scientific work requires accurate citations of referenced work so as to allow the community to understand the whole context and also replicate the experiments conducted by other researchers, LREC 2022 endorses the need to uniquely Identify LRs through the use of the International Standard Language Resource Number (ISLRN, www.islrn.org), a Persistent Unique Identifier to be assigned to each Language Resource. The assignment of ISLRNs to LRs cited in LREC papers will be offered at submission time.