This shows you the differences between two versions of the page.
Next revision | Previous revision | ||
news:2024:04:17_1713360456 [2024/04/17 15:27] – Created from the form at news:new Seraina Nadig | news:2024:04:17_1713360456 [2024/04/17 18:14] (current) – Seraina Nadig | ||
---|---|---|---|
Line 5: | Line 5: | ||
</ | </ | ||
+ | {{ : | ||
+ | <WRAP round box 60%> | ||
+ | ==== Information on the event: ==== | ||
+ | **Date:** June 10-11, 2024\\ | ||
+ | **Location: | ||
+ | **Early bird registration: | ||
+ | **Website: | ||
+ | </ | ||
+ | \\ | ||
+ | CLARIN-CH is a partner of **SwissText 2024**, an annual conference that brings together text analytics experts from industry and academia. It is organized by the //Swiss Association for Natural Language Processing (SwissNLP)// | ||
- | 9th SwissText | + | The organising committee has now announced the selection of workshops which will be held during |
- | June 10-11, | + | |
- | Fachhochschule Graubünden (FHGR) | + | |
- | Next-Gen Cleantech Solutions: Mining Insights from Media and Patent Data with Natural Language Processing (NLP) and Large Language Models (LLMs) | + | <WRAP round box 80%> |
- | RAG: Unveiling the Power of Retrieval-Augmented Generation | + | ==== Workshops at SwissText 2024 ==== |
- | Grounding generative AI models | + | * [[https:// |
- | AI Support Systems for Academic Research | + | |
- | Simply The Text! NLP Solutions for Accessibility Challenges | + | * [[https:// |
- | Battle of NLP Ideas | + | * [[https:// |
+ | * [[https:// | ||
+ | * [[https:// | ||
+ | ⚠️ Please note that some of the workshops publish a Call for Papers or for \\ Extended Abstracts. The calls also include submission deadlines. ⚠️ | ||
+ | </ | ||
+ | |||
+ | ==== Shared Task at SwissText 2024 ==== | ||
+ | |||
+ | The following Shared Task has been selected: **[[https:// | ||
+ | |||
+ | This task aims to facilitate the identification and analysis of research towards the SDGs using automatic classification systems. It consists of two subtasks: | ||
+ | * **Task 1: Classification at the Level of the 17 SDGs** \\ The primary aim of this Shared Task is to evaluate the capability of automated systems in classifying a scientific abstract under the most appropriate SDG. A particular emphasis is placed on the system’s proficiency in identifying the best fitting SDG when the specific goals are underrepresented in the dataset, thus ensuring a balanced and comprehensive understanding of the research landscape in relation to the SDGs. | ||
+ | |||
+ | * **Task 2: Multi-label Classification at the Level of SDG Targets** \\ This Shared Task is designed to challenge the systems’ abilities in fine-grained classification and to encourage the development of innovative solutions in the field of NLP, particularly in scenarios where extensive labeled data is not available. Accurate prediction of SDG targets in scientific abstracts enhances the specificity and usefulness of research classification in the context of sustainable development, | ||
+ | |||
+ | Additionally, | ||
+ | |||
+ | <WRAP centeralign>< |