TIMERS: Document-level Temporal Relation Extraction
We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18{\%} on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRelation ClassificationRelation ExtractionTemporal Relation ClassificationTemporal Relation ExtractionSimilar Papers 제목 키워드 기반
Structured Learning for Temporal Relation Extraction from Clinical Records
We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We e…
PredictionRelationRelation ExtractionTemporal Information Extraction+1Global Inference to Chinese Temporal Relation Extraction
Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sen…
Question AnsweringRelationRelation ExtractionSentence+2PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the …
Document-level Relation ExtractionRelationRelation ClassificationRelation Extraction+1Selecting Optimal Context Sentences for Event-Event Relation Extraction
Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/ graphs for input documents. To achieve this goal, our work addresses the problems of subevent …
Event Relation ExtractionRelationRelation ClassificationRelation Extraction+2A Masked Image Reconstruction Network for Document-level Relation Extraction
Document-level relation extraction aims to extract relations among entities within a document. Compared with its sentence-level counterpart, Document-level relation extraction requires inference over multiple sentences t…
Document-level Relation ExtractionImage ReconstructionRelationRelation Extraction+1