Comprehensive Annotation of Various Types of Temporal Information on the Time Axis (L18-1)
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| Challenge: | Existing studies linking event and time information have been conducted to train and evaluate models. |
| Approach: | They propose an annotation scheme that anchors expressions in text to the time axis comprehensively. |
| Outcome: | The proposed scheme can be utilized for integrated information analysis of events, entities and time. |
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A Multi-Axis Annotation Scheme for Event Temporal Relations (P18-1)
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| Challenge: | Existing temporal relation (TempRel) annotation schemes have low inter-annotator agreements even between experts, suggesting that the current annotation task needs a better definition. |
| Approach: | They propose to annotate temporal relation (TempRel) annotation schemes based on event start-points instead of a conventional 60’s-80’s model. |
| Outcome: | The proposed model improves IAA from the conventional 60’s to 80’s and can be used by crowdsourcing to alleviate labor intensity. |
NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
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| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
| Outcome: | The proposed framework achieves full coverage of all possible TLINKs in a text. |
Structured Interpretation of Temporal Relations (L18-1)
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| Challenge: | Temporal relations between events and time expressions are often modeled in an unstructured manner, resulting in inconsistent and incomplete annotation and computational modeling. |
| Approach: | They propose an annotation approach where events and time expressions form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora. |
| Outcome: | The proposed approach annotates 235 documents in news and narratives with 48 doubly annotated documents. |
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. |
| Approach: | They propose a new method to obtain temporal relations from absolute time value (a.k.a. time anchors) they start from time anchor for events and time expressions and induced temporal relation annotations automatically . |
| Outcome: | The proposed method shows that it requires less annotation effort and induces inter-sentence relations easily. |
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 . |
| Approach: | They propose a new annotation scheme that defines criteria for temporal relations to be annotated . scheme includes events even if they are not expressed as verbs, they argue . |
| Outcome: | The proposed method reduces time and manual effort on the part of annotators. |
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. |
| 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 . |
Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024.lrec-main)
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| Challenge: | Existing methods for annotating multilingual, multimedia data are limited by the availability of multilingual corpora for schema-based event representation. |
| Approach: | They propose a new approach to event annotation to promote whole-corpus understanding of complex events in multilingual, multimedia data. |
| Outcome: | The proposed method is part of the DARPA Knowledge-directed Artificial Intelligence Reasoning Over Schemas (KAIROS) Program. |
Event Time Extraction and Propagation via Graph Attention Networks (2021.naacl-main)
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| Challenge: | Existing work on grounding events into a precise timeline has been limited due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. |
| Approach: | They propose a 4-tuple temporal representation for entity slot filling to ground events into a timeline using a graph attention network approach. |
| Outcome: | The proposed approach yields 7.0% match rate over contextualized embedding approaches and 16.3% higher match rate compared to sentence-level manual event time argument annotation. |
Uncovering Temporal Framing in the News (2026.acl-long)
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Tarek Mahmoud, Veronika Solopova, Premtim Sahitaj, Ariana Sahitaj, Max Upravitelev, Mervat Abassy, Hana Fatima Shaikh, Neda Foroutan, Vera Schmitt, Preslav Nakov
| Challenge: | Temporal language is used to structure meaning rather than report chronology in news discourse . a recent study focused on temporal expression extraction and temporal reasoning . |
| Approach: | They propose a taxonomy of eight temporal frames grounded in prior work on time and framing . they analyze frame prevalence, co-occurrence patterns, and lexical cues from a news corpus . |
| Outcome: | The proposed taxonomy outperforms zero-shot models at the sentence level . it shows that temporal framing is learnable at the sentences level compared to other methods . |
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. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain. |