Dear all,

We are excited to invite you to participate in our upcoming shared task, Software Mention Detection (SOMD) 2025 co-located with the SDP workshop, ACL 2025 in Vienna, Austria. This event is designed to encourage innovation and collaboration in the Information Extraction field, focusing on software mentioned in scholarly articles.

Task Overview: Software plays an essential role in scientific research and is considered one of the crucial entity types in scholarly documents. However, the software is usually not cited formally in academic documents, resulting in various informal software mentions. Automatic identification and disambiguation of software mentions, related attributes, and the purpose of software mentions contributes to the better understanding, accessibility, and reproducibility of research but is a challenging task. This competition invites participants to develop a system that detects software mentions and their attributes as named entities from scholarly texts and classifies the relationships between these entity pairs. The dataset includes sentences from full-text scholarly documents annotated with Named Entities and Relations.

Participation Details: To participate, please register using this link [https://www.codabench.org/competitions/5840/]. All necessary materials, including detailed task guidelines and data, will be provided upon registration.

Competition Timeline Overview

• Competition Registration starts on February 24, 2025 • First phase: Training and Test Dataset release: February 28, 2025 • The first phase ends on: March 18, 2025 • Second phase data release: March 18, 2025 • The competition ends on: April 3, 2025 • Paper submission deadline: April 17, 2025 • Notification of Acceptance: May 1, 2025 • Camera-ready Paper Deadline for Workshop: May 16, 2025. • Workshop Date: July 21-August 1, 2025

Successful entries will be featured in the Proceedings of the Workshop on Scholarly Document Processing (SDP). For more detailed information about the task, including participation guidelines and data access, please visit our competition in codabench or our website and contact us directly. Looking forward to your participation.

Warm Regards, Sharmila Upadhyaya FAIR Data Knowledge Technologies for the Social Sciences (KTS) Web: https://www.gesis.org/en/kts GESIS – Leibniz Institute for the Social Sciences Unter Sachsenhausen 6-8, D-50667 Cologne, Germany Email: sharmila.upadhyaya@gesis.org Phone: +49 (0221) 47694-725