Papers by Scott Friedman
The Lexometer: A Shiny Application for Exploratory Analysis and Visualization of Corpus Data (2022.lrec-1)
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| Challenge: | Lexometer is a data science application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Approach: | They propose a Shiny application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Outcome: | The Lexometer integrates numerous data analysis and visualization functions into an easy-to-use graphical user interface. |
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results (2021.emnlp-main)
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| Challenge: | Existing approaches focus on high-level description of how research is carried out . instead, we focus on the subtleties of how experimental associations are presented . |
| Approach: | They propose a transformer-based approach to relational scientific information extraction that captures associations over experimental variables and their qualifications, subtypes, and evidence. |
| Outcome: | The proposed schema captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with qualifications, subtypes, and evidence. |
Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation (2024.lrec-main)
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| Challenge: | Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets. |
| Approach: | They propose to augment training data with norm-based language templates derived from previous language resources to reduce biases in NLP models. |
| Outcome: | The proposed model reduces topical bias to less than half while maintaining prediction quality on held-out test sets. |
Recognizing Value Resonance with Resonance-Tuned RoBERTa Task Definition, Experimental Validation, and Robust Modeling (2024.lrec-main)
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Noam K. Benkler, Scott Friedman, Sonja Schmer-Galunder, Drisana Marissa Mosaphir, Robert P. Goldman, Ruta Wheelock, Vasanth Sarathy, Pavan Kantharaju, Matthew D. McLure
| Challenge: | Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts is a fundamental goal of social anthropology. |
| Approach: | They propose to use a novel hand-annotated dataset and a transformer-based model to model the NLP task of Recognizing Value Resonance (RVR) they extend existing work by refining the task definition and releasing the WVC dataset. |
| Outcome: | The proposed models outperform top-performing Recognizing Textual Entailment models in recognizing value resonance and zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. |