Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |
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| Challenge: | Existing methods for event temporal relation extraction ignore meaning of relations and wipe out their intrinsic dependency. |
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DCT-Centered Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Existing work on temporal relation extraction focuses on extracting temporal relations between events . previous work on relation extraction focused on focusing on event-centered tasks . |
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| Challenge: | Existing approaches to analyzing large language models are limited by their pre-trained knowledge of Small Language Models(SLMs). |
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| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
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| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
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Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for event-event temporal relation extraction are sparse on event-time information. |
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| Challenge: | Existing temporal extraction systems that extract temporal relations can be improved by using a resource that provides prior knowledge of the temporal order that events usually follow. |
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Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
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| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
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An Improved Neural Baseline for Temporal Relation Extraction (D19-1)
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| Challenge: | Existing datasets are small and/or have low inter-annotator agreements. |
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