Papers by Shafiuddin Rehan Ahmed
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
X-AMR Annotation Tool (2024.eacl-demo)
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| Challenge: | X-AMR annotation tool is designed for annotating key corpus-level event semantics. |
| Approach: | They propose a new annotation tool for annotation of key corpus-level event semantics using machine assistance. |
| Outcome: | The proposed tool enhances the user experience and improves annotation efficiency. |
Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)
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Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Reagan, Kristin Wright-Bettner, Martha Palmer, James H. Martin
| Challenge: | Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations. |
| Approach: | They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
| Outcome: | The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)
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| Challenge: | Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult to learn coreference beyond surface matching. |
| Approach: | They propose a heuristic to efficiently filter out a large number of non-coreferent pairs and a training approach on a balanced set of coreferent and non- coreferente mention pairs. |
| Outcome: | The proposed approach significantly reduces compute requirements on two popular ECR datasets while reducing the computational complexity. |
Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing (2024.acl-short)
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| Challenge: | Existing methods for Cross-Document Event Coreference Resolution (CDEC) are biased towards lexical similarities, limiting a crucial avenue of research in event comprehension. |
| Approach: | They propose a lexically rich variant of Event Coref Bank Plus (ECB+) for CDEC on symbolic and metaphoric language. |
| Outcome: | The proposed method avoids the reannotation of expensive coreference links on symbolic and metaphoric language. |
Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles (2024.lrec-main)
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Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, George Arthur Baker, Rahul Ghosh, James H. Martin, Nathaniel Blanchard, Nikhil Krishnaswamy
| Challenge: | Existing methods for cross-document coreference resolution do not provide images for all mentions of events. |
| Approach: | They propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. |
| Outcome: | The proposed method improves on a popular ECB+ and AIDA datasets. |
CAMRA: Copilot for AMR Annotation (2023.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation. |
| Approach: | They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots . |
| Outcome: | The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature. |
On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency? (2025.findings-acl)
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| Challenge: | Existing studies on large language models (LLMs) have not explored their capacity to reason over event structure . et al., 2015, 142: e007-e0027; eugene, 1985; Weiner, 1995; saab, 1985) focus on the role of large language model in decision-making . |
| Approach: | They propose to characterize agents via properties such as "instigation" and "volition" they also examine whether incorporating semantic proto-role labeling context improves SRL performance . |
| Outcome: | The proposed model improves in a zero-shot setting by incorporating proto-role labeling context . the results support previous work showing that LLMs underperform human annotators in complex semantic analysis. |