Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

ASSET

Affiliation

TWLog: Task Workflow-Based Log Anomaly Detection task workflow and?log events. Based on the basic task workflow from log message,?we extract the semantic information from raw log messages as vector representations. These vectors are then fed into a Transformer-based model which can capture the contextual information from?task workflow-based log s

護身符

剝削

一條卷發(fā)

俗艷

Glycogen

MIIGraph: Multi-granularity Information Integration Graph for?Document-Level Event Extraction representation of?the document through contrastive learning. Then, we construct?a heterogeneous graph to capture the complex interactions between entities, sentences, and global theme. Finally, we conducted extensive experiments to evaluate MIIGraph on two widely used?DEE benchmarks. The results sh

巧辦法

MIIGraph: Multi-granularity Information Integration Graph for?Document-Level Event Extraction representation of?the document through contrastive learning. Then, we construct?a heterogeneous graph to capture the complex interactions between entities, sentences, and global theme. Finally, we conducted extensive experiments to evaluate MIIGraph on two widely used?DEE benchmarks. The results sh

defeatist

Multi-granularity Neural Networks for?Document-Level Relation Extractionence-level feature vectors into document-level semantic features. Finally, entity representation and document representation are combined into a holistic representation?for relation prediction. Extensive experiments are conducted on?the DocRED dataset against state-of-the-art methods, and the compar

CLOUT

Multi-granularity Neural Networks for?Document-Level Relation Extractionence-level feature vectors into document-level semantic features. Finally, entity representation and document representation are combined into a holistic representation?for relation prediction. Extensive experiments are conducted on?the DocRED dataset against state-of-the-art methods, and the compar