Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)
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| Challenge: | Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies. |
| Approach: | They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents. |
| Outcome: | The proposed model outperforms baseline models on English and Chinese datasets by large margins. |
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
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Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)
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| Challenge: | Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines. |
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| Challenge: | a novel approach for event coreference resolution models correlations between event chains and document topical structures. |
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WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)
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| Challenge: | Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event . |
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Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (2021.emnlp-main)
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| Challenge: | Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. |
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Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
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Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)
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| Challenge: | Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge. |
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Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)
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| Challenge: | Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection. |
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Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
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Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)
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| Challenge: | Existing methods for event coreference resolution do not identify paraphrase relations between events. |
| Approach: | They propose a new event-specific paraphrase and argument-aware semantic Embedding model for event coreference resolution based on event-related paraphrases and argument embeddings . EPASE recognizes deep paraphrase relations in an event- specific context of sentences and can cover event paraphrase of more situations . |
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