Minimally Supervised Event Causality Identification Quang Do, Yee Seng, and Dan Roth University of Illinois at Urbana-Champaign 1 EMNLP-2011

2 The police arrested him because he killed someone. Event Causality

3 The police arrested him because he killed someone. event trigger Event Causality

4 The police arrested him because he killed someone. causality event trigger We identify causality between event pairs, but not the direction

Event Causality 5 The police arrested him because he killed someone. calculate causality association : co-occurrence counts, pointwise mutual information (PMI)…

Event Causality 6 The police arrested him because he killed someone. contingency discourse relation connective

Event Causality Identify multiple cues to jointly identify event causality:  Distributional association scores  discourse relation predictions 7 The police arrested him because he killed someone. discourse relation prediction distributional association score Distributional Discourse

Cause-Effect Association (CEA) and Discourse Relations We define an event e as: p ( a 1, a 2, …, a n ): 8 association between event predicates association between the predicate of an event and the arguments of the other event association between event arguments [ … … … ] connective [ … … … ] e e e e A connective is associated with two text spans Training on the Penn Discourse Treebank (PDTB), we developed a system that predicts the discourse relations of expressed by the connectives Distributional Discourse

Event Definition We define an event e as: p ( a 1, a 2, …, a n ):  predicate p : the event trigger word  a 1, a 2, …, a n : arguments associated with e Examples:  Verbs: “… he killed someone …”  Nominals: “… the attack by the troops …” 9

Contributions (Event Causality) We identify causality between event pairs in context:  verb-verb, verb-noun, noun-noun triggered event pairs  (prior work usually focus on just verb triggers) A minimally supervised approach to detect event causality using distributional similarity methods Leverage the interactions between event causality prediction and discourse relations prediction 10

Overview (Event Causality) Event causality:  Interaction between event causality and discourse relations  Event predicates: verbs, nominals Cause-Effect Association (CEA) Discourse and Causality:  Discourse relations  Constraints for joint inference with CEA Experiments:  Settings  Evaluation  Analysis Conclusion 11

Overview (Event Causality) Event causality:  Interaction between event causality and discourse relations  Event predicates: verbs, nominals Cause-Effect Association (CEA) Discourse and Causality:  Discourse relations  Constraints for joint inference Experiments:  Settings  Evaluation  Analysis Conclusion 12

Cause-Effect Association (CEA) 13 The police arrested him because he killed someone. CEA: prediction of whether two events are causally related

Cause-Effect Association (CEA) We define an event e as: p ( a 1, a 2, …, a n ):  predicate p : the event trigger word (e.g.: arrested, killed)  a 1, a 2, …, a n : arguments associated with e 14 association between event predicates association between the predicate of an event and the arguments of the other event association between event arguments

Predicate-Predicate Association 15

Predicate-Predicate Association 16 D : total number of documents in the collection N : number of documents that p occurs in

Predicate-Predicate Association 17 awards event pairs that are closer together in the texts (in terms of num# of sentences apart), while penalizing event pairs that are further apart

Predicate-Predicate Association 18 takes into account whether predicates (events) p i and p j appear most frequently with each other

Predicate-Predicate Association 19 takes into account whether predicates (events) p i and p j appear most frequently with each other u i will be maximized if there is no other predicate p k (as compared to p j ) having a higher co-occurrence probability with p i

Predicate-Argument Association 20 Pair up the predicates and arguments across events, calculate the PMI for each link, then average them

Argument-Argument Association 21 calculate the PMI for each possible pairings of the arguments (across the two events), then average them

Cause-Effect Association (CEA) 22 The police arrested him because he killed someone. CEA score: predicts whether the two events are causally related

Overview (Event Causality) Event causality:  Interaction between event causality and discourse relations  Event predicates: verbs, nominals Cause-Effect Association (CEA) Discourse and Causality:  Discourse relations  Constraints for joint inference with CEA Experiments:  Settings  Evaluation  Analysis Conclusion 23

Discourse and Causality Interaction 24 [ … … … ] connective [ … … … ] e e e e Interaction between: Discourse relation evoked by the connective c Relations between ep (event pairs that crosses the two text spans) causal? not-causal?

Penn Discourse Treebank (PDTB) Relations Discourse relations:  Comparison: Concession, Contrast, Pragmatic-concession, Pragmatic-contrast  Contingency: Cause, Condition, Pragmatic-cause, Pragmatic-condition  Expansion: Alternative, Conjunction, Exception, Instantiation, List, Restatement  Temporal: Asynchronous, Synchronous 25

Discourse Relations Comparison:  Highlights differences between the situations described in the text spans:  Negative evidence for causality Contingency:  The situation described in one text span causally influences the situation in the other:  Positive evidence 26 Contrast : [According to the survey, x% of Chinese Internet users prefer Google] whereas [ y% prefer Baidu]. Cause : [The first priority is search and rescue] because [many people are trapped under the rubble].

Discourse Relations Expansion:  Providing additional information, illustrating alternative situations, etc.:  Negative evidence, except for Conjunction (which connects arbitrary pieces of text spans) Temporal: Temporal precedence of the (cause) event over the (effect) event is a necessary, but not sufficient requisite for causality 27 Conjunction : [Over the past decade, x women were killed] and [ y went missing]. Synchrony : [He was sitting at his home] when [the whole world started to shake].

Discourse and Causality Interaction 28 [ … … … ] connective [ … … … ] e e e e Cause, Condition eiei ejej At least one (crossing) ep is causal 1 eiei ejej Cause, Condition, Temporal, Asynchronous, Synchrony, Conjunction If we have a (crossing) ep which is causal 2 Comparison, Concession, Contrast, Pragmatic-concession, Pragmatic-contrast, Expansion, Alternative, Exception, Instantiation, List, Restatement eiei ejej No (crossing) ep is casual 3

Joint Inference: Discourse & Distributional Causality Objective function: 29 Probability that connective c is predicted with discourse relation dr CEA prediction that event pair ep takes on the causal or not-causal label er discourse relation indicator variable event pair causality indicator variable

Constraints 30 If the connective is predicted with a “Cause” discourse relation, then the CEA system should predict that at least one of the (crossing) event pair is causally related Cause, Condition eiei ejej At least one (crossing) ep is causal 1 [ … … … ] connective [ … … … ] e e e e

Constraints 31 If a (crossing) event pair is predicted by CEA as causally related, then the associated connective should be predicted as having discourse relation; “Cause”, “Condition”, …, “Conjunction” [ … … … ] connective [ … … … ] e e e e eiei ejej Cause, Condition, Temporal, Asynchronous, Synchrony, Conjunction If we have a (crossing) ep which is causal 2

Constraints 32 If the connective is predicted with discourse relation “Comparison”, “Concession”, …, “Restatement”; no (crossing) event pair is causally related { “Comparison”,”Concession”… } [ … … … ] connective [ … … … ] e e e e Comparison, Concession, Contrast, Pragmatic-concession, Pragmatic-contrast, Expansion, Alternative, Exception, Instantiation, List, Restatement eiei ejej No (crossing) ep is casual 3

Overview (Event Causality) Event causality:  Interaction between event causality and discourse relations  Event predicates: verbs, nominals Cause-Effect Association (CEA) Discourse and Causality:  Discourse relations  Constraints for joint inference Experiments:  Settings  Evaluation  Analysis Conclusion 33

Experimental Settings To collect the distributional statistics for measuring CEA: 760K documents in the English Gigaword corpus 25 CNN documents from first three months of 2010:  20 documents for evaluation  5 documents for development 34

Annotation for Causal Event Pairs Annotation guidelines:  The Cause event should temporally precede the Effect event; the Effect event occurs because the Cause event occurs 35

Annotation for Causal Event Pairs 36 … S i-1 S i S i+1 … C (causality) R (relatedness) Drawing links between event predicates:  Event arguments are not annotated, but annotators are free to look at the entire document text  Annotators are not restricted to a fixed sentence window size Document

Annotation for Causal Event Pairs Annotators overlap on 10 evaluation documents. Agreement ratio:  0.67 for C+R  0.58 for C 37 # relationsEvalDev C41471 C+R49292

Performance on Extracting Causality 38

Performance on Extracting Causality and Relatedness 39

Analysis of CEA mistakes 50 (randomly selected) false-positives (precision errors):  56%: CEA assigns a high score to event pairs that are not causal  22%: involves events containing pronouns (“he”, “it”, etc.) as arguments 50 false-negatives (recall errors):  23%: CEA assigns a low score to causal event pairs  19%: involving nominal predicates that are not in our list of event evoking noun types  17%: involving nominal predicates without any argument (less information for CEA)  15%: involves events containing pronouns as arguments 40

Conclusion (Event Causality) Developed a minimally supervised approach to identify event causality Use distributional scores and discourse relations to jointly identify event causality 41