Papers by Derek Ruths
Evaluating Taxonomy Free Character Role Labeling (TF-CRL) in News Stories using Large Language Models (2025.emnlp-main)
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| Challenge: | TF-CRL assigns open-ended narrative role labels to characters in news stories based on their functional role in the narrative. |
| Approach: | They propose a task that assigns open-ended narrative role labels to characters in news stories based on their functional role in the narrative. |
| Outcome: | The proposed task outperforms human annotators across dimensions and shows that it is robust to human preference rankings and ratings. |
“Are you kidding me?”: Detecting Unpalatable Questions on Reddit (2021.eacl-main)
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| Challenge: | Existing methods to detect online abuse focus on the more explicit forms of abuse . existing methods focus on detecting subtler forms of online abuse leaving them unnoticed . |
| Approach: | They propose a task to detect unpalatable questions using reddit data to implement a context-aware dataset and implement 'learning models' they hope future research will address subtle forms of abuse since harm passes unnoticed through existing detection systems. |
| Outcome: | The proposed task is based on a dataset of reddit users and a conversational context. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
An Attribution Relations Corpus for Political News (L18-1)
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| Challenge: | Existing resources for recognizing attributions in context are limited in size and completeness. |
| Approach: | They propose to use the largest and most complete attribution relations corpus to date . they propose to create sophisticated end-to-end solutions for attribution extraction . |
| Outcome: | The political news attribution relations corpus 2016 is the largest and most complete attribution relations corpuse to date. |
Sentiment Analysis: It’s Complicated! (N18-1)
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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths
| Challenge: | a dataset of over 7,000 tweets annotated with 5x coverage is used for sentiment analysis . a "complicated" class of sentiment is used to categorize text based on a predefined notion of sentiment . |
| Approach: | They propose to use a "complicated" class of sentiment to categorize tweets . they build a publicly available tweet sentiment analysis dataset . |
| Outcome: | The proposed classifiers perform better over a new publicly available TSA dataset . the classifier performance is compared with existing methods and improves on existing ones . |
Story Morals: Surfacing value-driven narrative schemas using large language models (2024.emnlp-main)
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| Challenge: | Using large language models, we extract and validate story morals across a diverse set of narrative genres. |
| Approach: | They propose a task of narrative schema labelling based on the concept of "story morals" they use large language models to extract and validate story morals across a diverse set of genres . |
| Outcome: | The proposed method extracts and validates story morals across folktales, novels, movies and TV, personal stories from social media and the news using automated metrics and human assessments. |