Temporal Relation Classification
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Temporal Relation Classification is the task that is concerned with classifying the temporal relation between a pair of temporal entities (traditional events and temporal expressions). Initial approaches aimed to classify the temporal relation in thirteen relation types that were depicted by James Allen in his seminal work "Maintaining Knowledge about Temporal Intervals". However, due to the ambiguity in the annotation, recent corpora have been limiting the type of relations to a subset of those relations.
Notice that although Temporal Relation Classification can be thought of as a subtask of Temporal Relation Extraction, the two tasks can be morphed if one adds a label that indicates the absence of a temporal relation between the entities (e.g. "no_relation" or "vague") to Temporal Relation Classification.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
tieval: An Evaluation Framework for Temporal Information Extraction Systems
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Word-Level Loss Extensions for Neural Temporal Relation Classification
Temporal Event Knowledge Acquisition via Identifying Narratives
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.