Event Argument Extraction using Causal Knowledge Structures (original) (raw)

Inducing Rich Interaction Structures Between Words for Document-Level Event Argument Extraction

Quan Tran

Advances in Knowledge Discovery and Data Mining, 2021

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The Causal News Corpus: Annotating Causal Relations in Event Sentences from News

ali hürriyetoğlu

2022

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EDeR: A Dataset for Exploring Dependency Relations Between Events

leyang cui

arXiv (Cornell University), 2023

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Resource-Enhanced Neural Model for Event Argument Extraction

Rishita Anubhai

Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

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Event extraction as dependency parsing

Chris Manning

2011

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Modeling Document-level Causal Structures for Event Causal Relation Identification

Ruihong Huang

Proceedings of the 2019 Conference of the North

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ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

Sudeshna Sarkar

2021

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Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction

Trần Diệu

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021

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Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022

Farhana Liza

arXiv (Cornell University), 2022

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Exploiting Rich Event Representation to Improve Event Causality Recognition

Yanhui Gu

Intelligent Automation & Soft Computing, 2021

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Causal BERT: Language Models for Causality Detection Between Events Expressed in Text

Shubhashis Sengupta

Lecture Notes in Networks and Systems, 2021

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Building a Cross-document Event-Event Relation Corpus

Sharone Horowit-Hendler

Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016), 2016

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Multi-Sentence Argument Linking

Seth Ebner

arXiv (Cornell University), 2019

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COfEE: A Comprehensive Ontology for Event Extraction from text, with an online annotation tool

Ali Balali

ArXiv, 2021

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A survey on extraction of causal relations from natural language text

Josiah Poon

Knowledge and Information Systems, 2022

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Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks

Ali Balali

2020

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Generating Event Causality Hypotheses through Semantic Relations

Julien Kloetzer

Proceedings of the AAAI Conference on Artificial Intelligence

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A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Lipika Dey

IOS Press eBooks, 2022

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Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

Lipika Dey

Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, 2018

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Causal relation extraction

Nuria Castell, Dan Moldovan

Proceedings of the Sixth …, 2008

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Event extraction from textual data

Desmond B A L A Bisandu

The Journal of Computer Science and Its Application, 2019

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Selecting Optimal Context Sentences for Event-Event Relation Extraction

Hieu Man

Proceedings of the AAAI Conference on Artificial Intelligence

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Annotating Event Mentions in Text with Modality, Focus, and Source Information

Kentaro Inui

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Cofee: A Comprehensive Ontology for Event Extraction from Text

Masoud Asadpour

SSRN Electronic Journal

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Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey

Nabiha Asghar

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Extracting Events and Temporal Expressions from Text

Naushad Uzzaman

2010 IEEE Fourth International Conference on Semantic Computing, 2010

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Two-phased event relation acquisition: coupling the relation-oriented and argument-oriented approaches

Kentaro Inui

2008

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Rusu et al - Unsupervised Techniques for Extracting and Clustering Complex Events in News(2013)

anthony kimball

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A Lightweight Tool for Automatically Extracting Causal Relationships from Text

M. Huhns

Proceedings of the IEEE SoutheastCon 2006, 2006

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Evaluating Dependency Representations for Event Extraction

Junichi Tsujii

Coling, 2010

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Causal graph extraction from news: a comparative study of time-series causality learning techniques

Mariano Maisonnave

PeerJ Computer Science

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