Minimally Supervised Event Causality Identification

Minimally Supervised Event Causality Identification

Quang Do, Yee Seng, and Dan Roth

University of Illinois at Urbana-Champaign

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EMNLP-2011

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The police arrested him because he killed someone.

Event Causality

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event trigger event trigger

Event Causality

Event Causality

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causality

event trigger event trigger

• We identify causality between event pairs, but not the direction

Event Causality

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calculate causality association: co-occurrence counts, pointwise mutual information (PMI)…

Event Causality

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contingency discourse relation

connective

Event Causality

Identify multiple cues to jointly identify event causality: Distributional association scores discourse relation predictions

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discourse relation prediction

distributional association scoreDistributional

Discourse

Cause-Effect Association (CEA) andDiscourse Relations

We define an event e as: p(a1, a2, …, an):

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association between event predicates

association between the predicate of an event and the arguments of the other event

association between event arguments

[ … … … ] connective [ … … … ]eeee

• 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(a1, a2, …, an): predicate p: the event trigger word

a1, a2, …, an: arguments associated with e

Examples: Verbs: “… he killed someone …” Nominals: “… the attack by the troops …”

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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

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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

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Discourse relations Constraints for joint inference


Conclusion

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Cause-Effect Association (CEA)

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CEA: prediction of whether two events are causally related


We define an event e as: p(a1, a2, …, an): predicate p: the event trigger word (e.g.: arrested, killed)

a1, a2, …, an: arguments associated with e

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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

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D: total number of documents in the collectionN: number of documents that p occurs in


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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


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takes into account whether predicates (events) pi and pj appear most frequently with each other


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takes into account whether predicates (events) pi and pj appear most frequently with each other

ui will be maximized if there is no other predicate pk (as compared to pj) having a higher co-occurrence probability with pi

Predicate-Argument Association

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Pair up the predicates and arguments across events, calculate the PMI for each link, then average them

Argument-Argument Association

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


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CEA score: predicts whether the two events are causally related




Discourse relations Constraints for joint inference with CEA


Conclusion

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Discourse and Causality Interaction

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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

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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

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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

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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

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Cause, Condition

ei ejAt least one (crossing) ep is causal

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ei ejCause, Condition, Temporal, Asynchronous, Synchrony, Conjunction

If we have a (crossing) ep which is causal2

Comparison, Concession, Contrast, Pragmatic-concession, Pragmatic-contrast, Expansion, Alternative, Exception, Instantiation, List, Restatement

ei ejNo (crossing) ep is casual

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Joint Inference: Discourse & Distributional Causality

Objective function:

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Cc Ldr EPep LererepepERdrccDR

DR ER

yersLxdrsLArg ,, )()(max

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

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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

"","","", causalepcausalepCausec jiyyx

Cause, Condition

ei ejAt least one (crossing) ep is causal

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Constraints

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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”

"","","","", nConjunctiocConditioncCauseccausalep xxxy


ei ejCause, Condition, Temporal, Asynchronous, Synchrony, Conjunction

If we have a (crossing) ep which is causal2

Constraints

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If the connective is predicted with discourse relation “Comparison”, “Concession”, …, “Restatement”; no (crossing) event pair is causally related

{“Comparison”,”Concession”…}


Comparison, Concession, Contrast, Pragmatic-concession, Pragmatic-contrast, Expansion, Alternative, Exception, Instantiation, List, Restatement

ei ejNo (crossing) ep is casual

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Discourse relations Constraints for joint inference


Conclusion

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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

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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

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…

Si-1

Si

Si+1

…

…

C (causality)

R (relatedness)

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


Annotators overlap on 10 evaluation documents. Agreement ratio: 0.67 for C+R 0.58 for C

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# relations Eval Dev

C 414 71

C+R 492 92

Performance on Extracting Causality

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Performance on Extracting Causality and Relatedness

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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

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Conclusion (Event Causality)

Developed a minimally supervised approach to identify event causality

Use distributional scores and discourse relations to jointly identify event causality

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Minimally Supervised Event Causality Identification

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