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Kira Radinsky, Sagie Davidovich, Shaul Markovitch Technion - Israel Institute of Technology Learning Causality for News Events Prediction.

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Presentation on theme: "Kira Radinsky, Sagie Davidovich, Shaul Markovitch Technion - Israel Institute of Technology Learning Causality for News Events Prediction."— Presentation transcript:

1 Kira Radinsky, Sagie Davidovich, Shaul Markovitch Technion - Israel Institute of Technology Learning Causality for News Events Prediction

2 “…a rigorous, often quantitative, statement, forecasting what will happen under specific conditions.“ [Wikipedia] What is Prediction? “A description of what one thinks will take place in the future, based on previous knowledge.” [Online Dictionary]

3 Why is News Event Prediction Important? EventPredicted event (Pundit) Al-Qaida demands hostage exchange A country will refuse the demand Volcano erupts in Democratic Republic of Congo Thousands of people flee from Congo 7.0 magnitude earthquake strikes Haitian coast Tsunami-warning is issued China overtakes Germany as world's biggest exporter Wheat price will fall Strategic Intelligence Strategic planning Financial investments

4 Motivation Problem definition Solution Representation Algorithm Evaluation Outline

5 Problem Definition: Events Prediction Prediction Function Ev is a set of events T is discrete representation of time

6 Motivation Problem definition Solution Representation Algorithm Evaluation Outline

7 Causality Mining Process: Overview News Articles acquisition Crawling [NYT ] Modeling & Normalization Causality Pattern Classification Event Extraction Tagging Dependency parsing (Stanford parser) Thematic roles assignment Based on VerbNet Index Thematic roles normalized Base forms URIs assignment (Contextual Disambiguation) Causality Relations extraction Context inference State Inference Causality Graph Building Built on 20 machines 300 million nodes 1 billion edges 13 million news articles in total

8 Motivation Problem definition Solution Representation Algorithm Evaluation Outline

9 Modeling an Event Comparison between events (Canonical) 1.(Lexicon & Syntax) Language & wording independent 2.(Semantic) Non ambiguous Generalization / abstraction Reasoning Comparison between events (Canonical) 1.(Lexicon & Syntax) Language & wording independent 2.(Semantic) Non ambiguous Generalization / abstraction Reasoning Many philosophies Property Exemplification of Events theory (Kim 1993) Conceptual Dependency theory (Schank 1972) Many philosophies Property Exemplification of Events theory (Kim 1993) Conceptual Dependency theory (Schank 1972)

10 Event1 Weapon warehouse bombs US Army 1/2/ :00AM +(2h) Kabul Missiles Location Instrument Theme Action Time- frame Actor Caused Event2 Troops kill 1/2/ :15AM +(3h) Theme Action Time- frame US Army Time Event & Causality Representation Event Representation Causality Representation 5 5 Quantifier Afghan Attribute “US Army bombs a weapon warehouse in Kabul with missiles” “5 Afghan troops were killed”

11 Motivation Problem definition Solution Representation Algorithm Causality Mining Process Evaluation Outline

12 Machine Learning Problem Definition Learning algorithm receives a set of examples Goal function: and produces a hypothesis which is good approximation of

13 Algorithm Outline Learning Phase 1.Generalize events 2.Causality prediction rule generation Prediction Phase 1.Finding similar generalized event 2.Application of causality prediction rule

14 Algorithm Outline Learning Phase 1.Generalize events 1.How do we generalize objects? 2.How do we generalize actions? 3.How do we generalize an event? 2.Causality prediction rule generation

15 Generalizing Objects Russian Federation Eastern Europe China USSR the Russian Federation 643 RUS 185 Russia Rouble (Rub) Name official English ISO3 Code FAOSTAT code DBPedia ID Currency Name UN Code Is in group Land border Is successor of Is predecessor of

16 Ontology – Linked data

17 Generalizing Actions Levin classes (Levin 1993) – 270 classes Class Hit-18.1 Roles and Restrictions: Agent[+int_control] Patient[+concrete] Instrument[+concrete] Members: bang, bash, hit, kick,... Frames: Name ExampleSyntaxSemantics Basic Transitive Paula hit the ball Agent V Patient cause(Agent, E)manner(during(E), directedmotion, Agent) !contact(during(E), Agent, Patient) manner(end(E),forceful, Agent) contact(end(E), Agent, Patient)

18 Generalizing Events: Putting it all together Present Event Army base strikes NATO 1/2/ :00AM +(2h) Baghdad Missiles Location Instrument Theme Action Time-frame Actor US CountryArmy Past Event Weapon warehouse bombs US Army 1/2/ :00AM +(2h) Kabul Location Theme Action Time-frame Actor Similar verb City Military facility rdf:type “NATO strikes an army base in Baghdad” “US Army bombs a weapon warehouse in Kabul with missiles” Actor: [state of Nato] Property: [Hit1.1] Theme: [Military facility] Location: [Arab City] Generalization rule

19 Generalizing Events: HAC algorithm

20 Generalizing Events: Event distance metric Present Event Army base strikes NATO 1/2/ :00AM +(2h) Baghdad Missiles Location Instrument Theme Action Time-frame Actor US CountryArmy Past Event Weapon warehouse bombs US Army 1/2/ :00AM +(2h) Kabul Location Theme Time-frame Actor Similar verb City Military facility rdf:type “NATO strikes an army base in Baghdad” “US Army bombs a weapon warehouse in Kabul with missiles” Action

21 Learning Phase 1.Generalize events 2.Causality prediction rule generation Learning Phase 1.Generalize events 2.Causality prediction rule generation Algorithm Outline

22 Cause Event Weapon warehouse bombs US Army 1/2/ :00AM +(2h) Kabul Missiles Location Instrument Theme Action Time- frame Actor Caused Effect Event Troops kill 1/2/ :15AM +(3h) Theme Action Time- frame US Country Army Type Time Prediction Rule Generation 5 5 Quantifier Afghan Attribute “US Army bombs a weapon warehouse in Kabul with missiles” “5 Afghan troops were killed” Afghanistan Nationality Country Effect  Theme  Attribute = Cause  Location  Country  Nationality Effect  Action=kill Effect  Theme=Troops

23 Algorithm Outline Prediction Phase 1.Finding similar generalized event 2.Application of causality prediction rule

24 Finding Similar Generalized Event “Baghdad bombing”

25 Input Event Theme1 bomb Actor1 T1T1 Location1 Instrument1 Location Instrument Theme Action Time- frame Actor Caused Predicted Effect Event Troops kill T 1 + ∆ Theme Action Time- frame Time Prediction Rule Application Attribute Nationality Country Effect  Theme  Attribute = Cause  Location  Country  Nationality Effect  Action=kill Effect  Theme=Troops

26 Motivation Problem definition Solution Representation Algorithm Evaluation Outline

27 Prediction Evaluation Human Group 1: Mark events E that can cause other events. Human Group 2: Given: Random sample of events from E, predictions and time of events Search the web and give estimation on the prediction accuracy

28 Prediction Accuracy Results Highly certainCertain Algorithm Humans

29 Causality Evaluation Human Group 1: Mark events E for test for the second two control groups and the algorithm. Human Group 2: Given: Random sample of events from E. State what you think would happen following this event. Human Group 3: Given: algorithm predictions + human (2 nd group) predictions Evaluate the quality of the predictions

30 Causality Results The results are statistically significant [0,1)[1-2)[2-3)[3,4]Avg. RankAvg. Accuracy Algorithm % Humans %

31 EventPredicted Event (Human)Predicted event (Pundit) Al-Qaida demands hostage exchange Al-Qaida exchanges hostage A country will refuse the demand Volcano erupts in Democratic Republic of Congo Scientists in Republic of Congo investigate lava beds Thousands of people flee from Congo 7.0 magnitude earthquake strikes Haitian coast Tsunami in Haiti effects coast Tsunami-warning is issued 2 Palestinians reportedly shot dead by Israeli troops Israeli citizens protest against Palestinian leaders War will be waged Professor of Tehran University killed in bombing Tehran students remember slain professor in memorial service Professor funeral will be held Alleged drug kingpin arrested in Mexico Mafia kills people with guns in town Kingpin will be sent to prison UK bans Islamist groupIslamist group would adopt another name in the UK Group will grow China overtakes Germany as world's biggest exporter German officials suspend tariffs Wheat price will fall

32 Accuracy of Extraction ActionActorObjectInstrumentLocationTime 93%74%76%79% 100% ActorObjectInstrumentLocation 84%83%79%89% Extraction Evaluation Entity Ontology Matching

33 Related work Causality Information Extraction Goal: Extract causality relations from a text Techniques: 1.Usage of handcrafted domain-specific patterns [Kaplan and Berry-Rogghe, 1991] 2.Usage of handcrafted linguistic patterns [Garcia 1997],[Khoo, Chan, &Niu 2000], [Girju &Moldovan 2002] 3.Semi-Supervised pattern learning approaches, based on text features [Blanco, Castell, &Moldovan 2008], [Sil & Huang & Yates 2010] 4.Supervised pattern learning approaches based on text features [Riloff 1996],[Riloff & Jones 1999], [Agichtein & Gravano, 2000; Lin & Pantel, 2001]

34 Related work Temporal Information Extraction Goal: Predicting the temporal order of events or time expressions described in text Technique: learn classifiers that predict a temporal order of a pair of events based on a predefined features of the pair. [Ling & Weld, 2010; Mani, Schiffman, & Zhang, 2003; Lapata & Lascarides,2006; Chambers, Wang, & Jurafsky, 2007; Tatu & Srikanth, 2008; Yoshikawa, Riedel, Asahara, & Matsumoto, 2009]

35 Future work Going beyond human tagged examples Incorporating time into the equation When will correlation mean causality? Using other sources than news Incorporating real time data (Twitter, Facebook) Incorporating numerical data (Stocks, Weather, Forex) Can we predict general facts? Can a machine predict better than an expert?

36 Summary Canonical event representation Machine learning algorithm for events prediction Leveraging world knowledge for generalization Using text as human tagged examples Causality mining from text Contribution to machine common-sense understanding “The best way to predict the future is to invent it” [Alan Kay]


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