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Learning causality for news events prediction

Published: 16 April 2012 Publication History

Abstract

The problem we tackle in this work is, given a present news event, to generate a plausible future event that can be caused by the given event. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precise labeled causality examples, we mine 150 years of news articles, and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns. For generalization, the model uses a vast amount of world knowledge ontologies mined from LinkedData, containing ~200 datasets with approximately 20 billion relations. Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance.

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cover image ACM Other conferences
WWW '12: Proceedings of the 21st international conference on World Wide Web
April 2012
1078 pages
ISBN:9781450312295
DOI:10.1145/2187836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Univ. de Lyon: Universite de Lyon

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Publication History

Published: 16 April 2012

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Author Tags

  1. future prediction
  2. news prediction
  3. web knowledge for future prediction

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  • Research-article

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WWW 2012
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  • Univ. de Lyon
WWW 2012: 21st World Wide Web Conference 2012
April 16 - 20, 2012
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2024)Research on Entity Representation Bias Adjustment in Causal Relationship ExtractionProceedings of the 2024 10th International Conference on Computing and Artificial Intelligence10.1145/3669754.3669801(307-312)Online publication date: 26-Apr-2024
  • (2024)Knowledge Induced Transformer Network for Causality PredictionCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651531(637-640)Online publication date: 13-May-2024
  • (2024)A Framework to Construct Financial Causality Knowledge Graph from Text2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00015(57-64)Online publication date: 5-Feb-2024
  • (2024)Towards Machine Learning Based Text Categorization in the Financial Domain2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS)10.1109/CITDS62610.2024.10791384(1-6)Online publication date: 26-Aug-2024
  • (2024)An information support framework for chain reaction of major emergencies based on causality eventic graphData Technologies and Applications10.1108/DTA-01-2024-0048Online publication date: 17-Dec-2024
  • (2024)Semantic aware enhanced event causality identificationScientific Reports10.1038/s41598-024-83678-914:1Online publication date: 30-Dec-2024
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  • (2024)2SCE-4SL: a 2-stage causality extraction framework for scientific literatureScientometrics10.1007/s11192-023-04817-z129:11(7175-7195)Online publication date: 1-Nov-2024
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