Papers by Gerold Schneider
NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries (2024.emnlp-main)
Copied to clipboard
Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen
| Challenge: | Despite substantial investment, developing new treatments for neurological conditions is a challenging and often unsuccessful endeavour. |
| Approach: | They propose a corpus for named entity recognition that is annotated clinical trial summaries from ClinicalTrials.gov. |
| Outcome: | The proposed corpus is annotated for neurological diseases, therapeutic interventions, and control treatments and achieves a close-to-human performance. |
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive (2025.emnlp-demos)
Copied to clipboard
Yingqiang Gao, Fabian Winiger, Patrick Montjourides, Anastassia Shaitarova, Nianlong Gu, Simon Peng-Keller, Gerold Schneider
| Challenge: | Religion and spirituality (R/S) are complex and domain-dependent concepts that have long confounded researchers and policymakers. |
| Approach: | They propose an interactive question-answering system based on Retrieval-Augmented Generation (RAG) SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of large datasets . |
| Outcome: | SpiritRAG is an interactive Q&A system based on Retrieval-Augmented Generation (RAG) built using 7,500 UN resolution documents related to religion and spirituality in the domains of health and education. |
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing biomarkers for AD diagnosis can only be applied to relatively small sample sizes due to limited availability, excessive costs and invasive nature. |
| Approach: | They compare automatic speech recognition systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. |
| Outcome: | The proposed method improves classification performance by replacing manual transcriptions with ASR output. |
Using Multilingual Resources to Evaluate CEFRLex for Learner Applications (2020.lrec-1)
Copied to clipboard
| Challenge: | The Common European Framework of Reference for Languages defines six levels of learner proficiency and links them to particular communicative abilities. |
| Approach: | They propose to compile lexical resources that link single words and multi-word expressions to specific CEFR levels. |
| Outcome: | The results show that the English CEFRLex resource is in accordance with external resources that are gold standard. |
Robust Native Language Identification through Agentic Decomposition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) often achieve high performance by leveraging superficial contextual clues rather than the underlying linguistic patterns indicative of native language (L1) influence. |
| Approach: | They propose an agentic NLI pipeline where specialized agents accumulate and categorize diverse linguistic evidence before an independent final assessment. |
| Outcome: | The proposed pipeline significantly improves robustness against misleading contextual clues and performance consistency compared to standard prompting methods. |
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset (2024.naacl-long)
Copied to clipboard
| Challenge: | Hate speech detection models are only as good as the data they are trained on, but adversarial datasets are slow and costly . data sourced from social media suffer from systematic gaps and biases, leading to unreliable models with simplistic decision boundaries. |
| Approach: | They propose a German Adversarial Hate speech Dataset comprising 11k examples . they explore new strategies for supporting annotators and provide manual analysis of disagreements for each strategy . |
| Outcome: | The proposed dataset is challenging even for state-of-the-art hate speech detection models and it significantly improves model robustness. |
Linguistic Features Extracted by GPT-4 Improve Alzheimer’s Disease Detection based on Spontaneous Speech (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have enabled powerful new possibilities for semantic text analysis. |
| Approach: | They leverage GPT-4 to extract five semantic features from transcripts of spontaneous patient speech. |
| Outcome: | The proposed model significantly improves detection of AD in manually transcribed and automatically generated transcripts. |