| 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. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
| Outcome: | The proposed method predicts start and end-points without intermediate step of prediction of temporal relations . it evades phase 2 because there are n 2 possible entity pairs in the extraction phase . |
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| Challenge: | Existing methods for event-event temporal relation extraction are sparse on event-time information. |
| Approach: | They propose a model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. |
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Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)
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| Challenge: | Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification. |
| Approach: | They propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. |
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Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (N18-1)
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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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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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Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
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Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
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TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles (2023.emnlp-main)
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| Challenge: | Existing temporal relation extraction models have low inter-annotator agreement due to lack of specificity of annotation guidelines . authors propose a method for annotating all temporal relations, including long-distance ones, which automates the process . |
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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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Inducing Temporal Relations from Time Anchor Annotation (N18-1)
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| Challenge: | Existing methods for judging temporal relations are limited to “salient” event pairs or on pairs in a fixed window of sentences. |
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Context-Aware Neural Model for Temporal Information Extraction (P18-1)
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| Challenge: | Existing temporal information extraction systems rely on statistical learning with feature-engineered task-specific models. |
| Approach: | They propose a context-aware neural network model for temporal information extraction using a global context layer. |
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Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)
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| Challenge: | Temporal relationship extraction is crucial for understanding complex events and reasoning over them. |
| Approach: | They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees. |
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