minimally supervised event causality identification quang do, yee seng, and dan roth university of...
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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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The police arrested him because he killed someone.
event trigger event trigger
Event Causality
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Event Causality
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The police arrested him because he killed someone.
causality
event trigger event trigger
• We identify causality between event pairs, but not the direction
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Event Causality
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The police arrested him because he killed someone.
calculate causality association: co-occurrence counts, pointwise mutual information (PMI)…
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Event Causality
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The police arrested him because he killed someone.
contingency discourse relation
connective
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Event Causality
Identify multiple cues to jointly identify event causality: Distributional association scores discourse relation predictions
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The police arrested him because he killed someone.
discourse relation prediction
distributional association scoreDistributional
Discourse
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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
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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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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
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Cause-Effect Association (CEA)
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The police arrested him because he killed someone.
CEA: prediction of whether two events are causally related
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Cause-Effect Association (CEA)
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
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Predicate-Predicate Association
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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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Predicate-Predicate Association
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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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Predicate-Predicate Association
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takes into account whether predicates (events) pi and pj appear most frequently with each other
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Predicate-Predicate Association
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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
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Predicate-Argument Association
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Pair up the predicates and arguments across events, calculate the PMI for each link, then average them
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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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Cause-Effect Association (CEA)
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The police arrested him because he killed someone.
CEA score: predicts whether the two events are causally related
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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 and Causality Interaction
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[ … … … ] connective [ … … … ]eeee
Interaction between:•Discourse relation evoked by the connective c•Relations between ep (event pairs that crosses the two text spans)
causal? not-causal?
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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].
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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].
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Discourse and Causality Interaction
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[ … … … ] connective [ … … … ]eeee
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
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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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[ … … … ] connective [ … … … ]eeee
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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
[ … … … ] connective [ … … … ]eeee
ei ejCause, Condition, Temporal, Asynchronous, Synchrony, Conjunction
If we have a (crossing) ep which is causal2
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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”…}
[ … … … ] connective [ … … … ]eeee
Comparison, Concession, Contrast, Pragmatic-concession, Pragmatic-contrast, Expansion, Alternative, Exception, Instantiation, List, Restatement
ei ejNo (crossing) ep is casual
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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
Experiments: Settings Evaluation Analysis
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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Annotation for Causal Event Pairs
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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
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Annotation for Causal Event Pairs
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
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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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