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Learning causality for news events prediction

Kira Radinsky, Sagie Davidovich, Shaul Markovitch
2012 Proceedings of the 21st international conference on World Wide Web - WWW '12  
We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques.  ...  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.  ...  Furthermore, the algorithms developed for causality extraction are aimed at detection of causality pairs and cannot be used for causality prediction, i.e., given an event, generating new events that it  ... 
doi:10.1145/2187836.2187958 dblp:conf/www/RadinskyDM12 fatcat:iaqx4boqb5h7xkklb3ndf3x5gq

Constructing and Embedding Abstract Event Causality Networks from Text Snippets

Sendong Zhao, Quan Wang, Sean Massung, Bing Qin, Ting Liu, Bin Wang, ChengXiang Zhai
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction.  ...  state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement  ...  Wangxiang Che, Jing Liu, Jiang Guo and the anonymous reviewers for their insightful comments and suggestions.  ... 
doi:10.1145/3018661.3018707 dblp:conf/wsdm/ZhaoWMQLWZ17 fatcat:4t255l3p5ndglefvp7zmunzrsy

Causal Knowledge Guided Societal Event Forecasting [article]

Songgaojun Deng, Huzefa Rangwala, Yue Ning
2021 arXiv   pre-print
We then incorporate the learned event-related causal information into event prediction as prior knowledge.  ...  We evaluate the proposed causal inference model on real-world event datasets and validate the effectiveness of proposed robust learning modules in event prediction by feeding learned causal information  ...  Utilizing causal effects to assist in event prediction is a new challenge.  ... 
arXiv:2112.05695v1 fatcat:dgwtdrjjvrhqddch7wxb654eb4

Temporal Sentiment Analysis and Causal Rules Extraction from Tweets for Event Prediction

P.G. Preethi, V. Uma, Ajit kumar
2015 Procedia Computer Science  
Causal relation is useful for identifying cause and effect of events and is also useful for event prediction.  ...  The proposed work introduces a generalized prediction model based on temporal sentiment analysis of tweet to identify the causal relation between the events which can be used to predict the event sentiment  ...  Uses support and confidence for causal rule identification and uses the causal rule for future events prediction Approximate prediction of time period between the events.  ... 
doi:10.1016/j.procs.2015.04.154 fatcat:qlv55rhzcvcg5h2uhcbdf7i5yq

Learning to Predict from Textual Data

K. Radinsky, S. Davidovich, S. Markovitch
2012 The Journal of Artificial Intelligence Research  
We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques.  ...  Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause.  ...  Learning and Predicting Causality In this section, we describe the Pundit algorithm for learning and predicting causality. We start with an overview of the learning and prediction process.  ... 
doi:10.1613/jair.3865 fatcat:woxyrlyeenaabbss6qgdpmveu4

Beyond the Information Given

Michael R. Waldmann, York Hagmayer, Aaron P. Blaisdell
2006 Current Directions in Psychological Science  
We report a number of recent studies that demonstrate that people and rats do not stick to the superficial level of event covariations but reason and learn on the basis of deeper causal representations  ...  If we only acquired knowledge about statistical covariations between observed events without accessing deeper information about causality, we would be unable to understand the differences between causal  ...  According to many learning theories, causal predictions are driven by associative relations that have been learned on the basis of observed covariations between events.  ... 
doi:10.1111/j.1467-8721.2006.00458.x fatcat:3bzg4zk6lvfrnat6dt6kivtzp4

Learning of contingent relationships

Lorraine G. Allan
2005 Animal Learning and Behavior  
They explore new predictions of the revised RW model and provide new data for the evaluation of these predictions.  ...  Vadillo et al. show that the pattern of ratings is different for causal, predictive-value, and predictive questions: Causal and predictive-value ratings depend on cue-outcome contingency, whereas predictive  ... 
doi:10.3758/bf03196057 pmid:16075833 fatcat:u6bs33rvund4riwk4v2wvgjl5q

Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis

York Hagmayer, Björn Meder, Momme von Sydow, Michael R. Waldmann
2011 Cognitive Science  
Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks.  ...  This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous  ...  We also thank Anne Meier-Credner, Dennis Golm, and Almut Hagner for running the experiments.  ... 
doi:10.1111/j.1551-6709.2011.01179.x pmid:21609354 fatcat:b43dzssuqnfrfo2rgox4lztrw4

Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning [chapter]

Michael J. Pazzani
1987 Proceedings of the Fourth International Workshop on MACHINE LEARNING  
In the approach to learning prcsentcd in this papa and implcmcntcd in a computer program called OCC~M, a theory of causality constrams tbc starch for causal hypotheses.  ...  We present an approach to lwming to predict and explain UK outcome oi events which lies between similaritybased methods and explanation-based methods.  ...  is a program which learns to predict and explain the outcome of events.  ... 
doi:10.1016/b978-0-934613-41-5.50027-1 fatcat:ttsuregfz5bilm3rqdji23o4ca

Contrasting predictive and causal values of predictors and of causes

Oskar Pineño, James C. Denniston, Tom Beckers, Helena Matute, Ralph R. Miller
2005 Animal Learning and Behavior  
in causal than predictive attribution.  ...  Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition  ...  Learning to predict the occurrence of an event on the basis of the occurrence of another event is essential for survival.  ... 
doi:10.3758/bf03196062 pmid:16075838 fatcat:pdrnvmlpezh23atd6nzkgxdf6m

Learning to Learn Causal Models

Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
2010 Cognitive Science  
Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models.  ...  A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments.  ...  We thank Bobby Han for collecting the data for Experiment 4, and Art Markman and several reviewers for valuable suggestions.  ... 
doi:10.1111/j.1551-6709.2010.01128.x pmid:21564248 fatcat:fbnmbz5s7nhelid3m4vcvc56u4

Explainable Agency in Reinforcement Learning Agents

Prashan Madumal
As humans, we build causal models to encode cause-effect relations of events and use these to explain why events happen.  ...  This thesis explores how reinforcement learning (RL) agents can provide explanations for their actions and behaviours.  ...  Causal Models for Explanation In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen by referring to  ... 
doi:10.1609/aaai.v34i10.7134 fatcat:iv3swpcy4zhzfhloxfsrmc5h2q

The tight coupling between category and causal learning

Michael R. Waldmann, Björn Meder, Momme von Sydow, York Hagmayer
2009 Cognitive Processing  
set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events.  ...  In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new  ...  might opt for inducing new, more predictable categories in Phase 2.  ... 
doi:10.1007/s10339-009-0267-x pmid:19562395 pmcid:PMC2860093 fatcat:kqv44zs2bngvdkou3xvzxnds7m

Seeing Versus Doing: Two Modes of Accessing Causal Knowledge

Michael R. Waldmann, York Hagmayer
2005 Journal of Experimental Psychology. Learning, Memory and Cognition  
The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning.  ...  We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational  ...  Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions.  ... 
doi:10.1037/0278-7393.31.2.216 pmid:15755240 fatcat:h7kjpnwqofb45ktotn4f4yzkde

Knowledge-Based Causal Induction [chapter]

Michael R. Waldmann
1996 The psychology of learning and motivation  
Introduction Our ability to acquire causal knowledge is central for our survival. Causal knowledge allows us to predict future events and to plan actions to achieve goals.  ...  For example, the difficulty of predictive and diagnostic learning probably differs.  ... 
doi:10.1016/s0079-7421(08)60558-7 fatcat:deh2vfb2frcvfck5uqasjr62h4
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