The use of software in acquiring, analyzing, and interpreting research data underscores its role as an essential artifact of scientific inquiry.Understanding and tracing the provenance of software in research helps in reproducible and collaborative research works.In this paper, we present an overview of our second iteration of the Software Mention Detection (SOMD) shared task as a part of the Scholarly Document Processing (SDP) workshop, that will be held in conjunction with ACL in 2025. We intend to foster among participants to brainstorm for optimized software mention detection and additional attributes and relation extraction tasks in the provided gold standard benchmark. Our shared task has two phases of challenges. First, the participants focus on implementing a joint framework for NER and RE for the given dataset. At the same time, the second phase includes the out-of-distribution dataset to evaluate the generalizability of the methods proposed in Phase I. The competition (March-April 2025) attracted 18 participants and spanned two months. Four teams have finished the competition and submitted full system descriptions. Participants applied various approaches, including joint and pipeline models, and explored data augmentation with LLM-generated samples.The evaluation was based on a macro-F1 score for both NER and RE, with the average reported as the SOMD-score.The winning teams achieved a SOMD-score of 0.89 in Phase I and 0.63 in Phase II, demonstrating the challenge of generalization.

Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding. In academic writing, references to machine learning models and datasets are fundamental components of various computer science publications and necessitate accurate models for identification. Despite the advancements in NER, existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types, and consequently, baseline models cannot recognize them as such. In this paper, we release a corpus of 100 manually annotated full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. In order to provide a nuanced understanding of how ML models and datasets are mentioned and utilized, our dataset also contains annotations for informal mentions like “our BERT-based model” or “an image CNN”. You can find the ground truth dataset and code to replicate model training at https://data.gesis.org/gsap/gsap-ner.

In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach. Throughout all steps of our pipeline, we regarded single sentences as our processing unit. In our IR-Component, we searched in the set of all possible Wikipedia introduction sentences without limiting sentences to a fixed number of relevant documents. In the entailment module, we judged every sentence separately and combined the result of the classifier for the top 5 sentences with the help of an ensemble classifier to make a judgment whether the truth of a statement can be derived from the given claim.