Refining Event Extraction through Cross-Document Inference

By Heng Ji, Ralph Grishman. Annual Meeting of the Association for Computational Linguistics, 2008

507 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f98ebc10ce8c48020d21cca041de2a3346ce31d9

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Zhiyuan Liu
3. Mo Yu
4. Mohit Bansal
5. Sijia Wang
6. Yoav Goldberg
7. Thien Huu Nguyen
8. Tianyu Gao

Citations in the corpus

• Event Extraction as Machine Reading Comprehension (2020, methodology): “Traditional EE methods employ manual-designed features, such as the syntactic feature (Ahn, 2006), document-level feature (Ji and Grishman, 2008), entity-level feature (Hong et al., 2011) and other features (Liao and Grishman, 2010; Li et al., 2013) for the task.”
• Document-Level Event Argument Extraction by Conditional Generation (2021, methodology): “For Coref F1, the model is given full credit if the extracted argument is coreferential with the gold-standard argument as used in (Ji and Grishman, 2008).”
• Event Detection and Co-reference with Minimal Supervision (2016, background): “Most earlier work has taken a pipeline approach where local classifiers identify triggers first, and then arguments (Ji and Grishman, 2008; Liao and Table 5: Event Co-reference Results on Gold Event Triggers.”
• Graph Convolutional Networks With Argument-Aware Pooling for Event Detection (2018, methodology): “The early and successful approach for ED has involved the feature-based methods that hand-design feature sets for different statistical models for ED (Ahn 2006; Ji and Grishman 2008; Hong et al. 2011; Li, Ji, and Huang 2013; Venugopal et al. 2014; Li et al. 2015a).”
• Seed-Based Event Trigger Labeling: How far can event descriptions get us? (2015, background): “Most state-of-the-art event trigger labeling approaches (Ji and Grishman, 2008; Liao and Grish-man, 2010b; Hong et al., 2011; Li et al., 2013) follow the standard supervised learning paradigm.”
• Incremental Event Detection via Knowledge Consolidation Networks (2020, methodology): “The proposed models can be divided into feature-based methods (Ahn, 2006; Ji and Grishman, 2008; Liao and Grishman, 2010; Li et al., 2013; Li and Ji, 2014) and neural network-based methods (Chen et al., 2015; Nguyen and Grishman, 2015; Liu et al., 2017, 2018a,b; Lu et al., 2019; Ding et al., 2019).”
• Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, background): “Feature-based approaches include (Ahn 2006) that examined lexical and syntactic features; (Ji and Grishman 2008; Hong et al. 2011; Li, Ji, and Huang 2013) that examined both local and global features and others.”
• The Art of Prompting: Event Detection based on Type Specific Prompts (2022, methodology): “Supervised ED: Most of the existing Event Detection studies follow a supervised learning paradigm (Ji and Grishman, 2008; Liao and Grishman, 2010; McClosky et al., 2011; Li et al., 2013; Chen et al., 2015; Cao et al., 2015; Feng et al., 2016; Yang and Mitchell, 2016; Nguyen et al., 2016; Zhang et al”
• Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021, methodology): “These methods can be divided into: 1) pipeline classification (Ahn, 2006; Ji and Grishman, 2008; Liao and Grishman, 2010; Hong et al., 2011, 2018; Huang and Riloff, 2012; Chen et al., 2015; Sha et al., 2016; Lin et al., 2018; Yang et al., 2019; Wang et al., 2019; Ma et al., 2020; Zhang et al.,…”
• Lifelong Event Detection with Knowledge Transfer (2021, methodology): “Event detection under the traditional setting has been widely studied (Ji and Grishman, 2008; Chen et al., 2015; Feng et al., 2016; Liu et al., 2017, 2018, 2019; Lu et al., 2019; Ding et al., 2019; Yan et al., 2019; Tong et al., 2020).”
• Using Document Level Cross-Event Inference to Improve Event Extraction (2010, methodology): “Ji and Grishman (2008) were inspired from the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995); they extended the scope from a single document to a cluster of topic-related documents and employed a rule-based approach to propagate consistent trigger classification and event arguments across…”
• MAVEN: A Massive General Domain Event Detection Dataset (2020, methodology): “Extensive supervised meth-ods along with various techniques have been developed, including the feature-based models (Ji and Grishman, 2008; Gupta and Ji, 2009; Li et al., 2013; Araki and Mitamura, 2015) and advanced neural models (Chen et al., 2015; Nguyen and Gr-ishman, 2015; Nguyen et al., 2016;…”
• Event Detection and Domain Adaptation with Convolutional Neural Networks (2015, methodology): “Methods F Sentence level in Ji and Grishman (2008) 59.”
• Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms (2017, methodology): “Unfortunately, most existing methods performed event detection individually, where the annotated arguments in training set are totally ignored (Ji and Grishman, 2008; Gupta and Ji, 2009; Hong et al., 2011; Chen et al., 2015; Nguyen and Grishman, 2015; Liu et al., 2016a,b; Nguyen and Grishman, 2016).”