Papers by Tim Weninger
ChatEL: Entity Linking with Chatbots (2024.lrec-main)
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| Challenge: | Entity Linking (EL) is a challenging task in natural language processing . existing approaches focus on creating elaborate contextual models that are unwieldy and difficult to train . |
| Approach: | They propose a framework to prompt LLMs to return accurate results for Entity Linking . they use a three-step framework to generate a set of EL models that can be open-source . |
| Outcome: | The proposed framework improves the average F1 performance across 10 datasets by more than 2%. |
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)
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| Challenge: | Existing reading comprehension models can over-generate attribute values which hinders precision. |
| Approach: | They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify. |
| Outcome: | The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes. |
Learning from Litigation: Graphs for Retrieval and Reasoning in eDiscovery (2025.acl-industry)
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| Challenge: | Electronic Discovery (eDiscovery) requires identifying relevant documents from vast collections for legal production requests. |
| Approach: | They propose a system that integrates knowledge graphs for enhanced document ranking and classification, augmented by LLM-driven reasoning. |
| Outcome: | The proposed system outperforms baselines in F1-score, precision, and recall across balanced and imbalanced datasets. |
Digital Gatekeepers: Google’s Role in Curating Hashtags and Subreddits (2025.acl-long)
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| Challenge: | This study examines how search engines like Google selectively promote or suppress certain hashtags and subreddits, impacting the flow of information and impacting public conversations. |
| Approach: | They compare search engine results with nonsampled data from Reddit and Twitter/X to examine how search engines curate content through algorithmic curation. |
| Outcome: | The proposed algorithm suppresses subreddits related to sexually explicit material, conspiracy theories, advertisements, and cryptocurrencies while promoting content associated with higher engagement. |
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification (2023.findings-emnlp)
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| Challenge: | Semi-supervised methods for detecting intent generate a large amount of unlabeled data . labeling data requires substantial human effort, and picking an imbalanced set of examples could lead to poor labels. |
| Approach: | They propose a balanced distance-based pseudo-labeling approach for semisupervised intent classification . they use a ranking-based approach to select samples with a model prediction confidence . |
| Outcome: | The proposed method outperforms existing models on popular datasets. |
MedCodER: A Generative AI Assistant for Medical Coding (2025.naacl-industry)
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Krishanu Das Baksi, Elijah Soba, John J Higgins, Ravi Saini, Jaden Wood, Jane Cook, Jack I Scott, Nirmala Pudota, Tim Weninger, Edward Bowen, Sanmitra Bhattacharya
| Challenge: | Medical coding is time-consuming and error-prone due to large label space, lengthy text inputs, and the absence of supporting evidence annotations. |
| Approach: | They propose a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. |
| Outcome: | The proposed framework outperforms existing methods on the International Classification of Diseases (ICD) code prediction scale. |
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)
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| Challenge: | a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read . |
| Approach: | They develop a model to classify user reactions into one of nine types . they also measure the speed and type of reaction for trusted and deceptive news sources . |
| Outcome: | The proposed model classifies user reactions into one of nine types, such as answer, elaboration, and question, etc. |
The Power of Framing: How News Headlines Guide Search Behavior (2025.findings-emnlp)
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| Challenge: | Framing effects on judgment are well documented, but their impact on subsequent search behavior is less understood. |
| Approach: | They conducted a controlled experiment where participants issued queries and selected headlines filtered by specific linguistic frames. |
| Outcome: | The results suggest that even brief exposure to framing can meaningfully alter the direction of users’ information-seeking behavior. |
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER (2020.findings-emnlp)
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| Challenge: | Pre-training a language model by self-supervised tasks on huge datasets and fine-tuning with small labelled data are often inadequate for scientific NER tasks. |
| Approach: | They propose to introduce a "pre-fine tuning" step between pre-training and fine-tuning to construct a corpus by selecting sentences from unlabeled documents that are the most relevant with labelled training data. |
| Outcome: | The proposed approach improves on seven benchmarks on the performance of the proposed model on labelled datasets. |