Minimally Supervised
Event Causality Identification
Quang Do, Yee Seng, and Dan Roth
University of Illinois at Urbana-Champaign
EMNLP-2011
1
Event Causality
The police arrested him because he killed someone.
2
Event Causality
The police arrested him because he killed someone.
event trigger
event trigger
3
Event Causality
causality
The police arrested him because he killed someone.
event trigger
event trigger
• We identify causality between event pairs, but not the direction
4
Event Causality
calculate causality association:
co-occurrence counts, pointwise mutual information (PMI)…
The police arrested him because he killed someone.
5
Event Causality
connective
The police arrested him because he killed someone.
contingency discourse relation
6
Event Causality
distributional association score
Distributional
The police arrested him because he killed someone.
discourse relation prediction
Discourse

Identify multiple cues to jointly identify event causality:

Distributional association scores
 discourse relation predictions
7
Cause-Effect Association (CEA) and
Discourse Relations

We define an event e as: p(a1, a2, …, an):
Distributional
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
association between
event predicates
association between the
predicate of an event and the
arguments of the other event
association between
event arguments
Discourse
[ … e … e … ] connective [ … 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
8
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 …”
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:




Cause-Effect Association (CEA)
Discourse and Causality:



Discourse relations
Constraints for joint inference with CEA
Experiments:




Interaction between event causality and discourse relations
Event predicates: verbs, nominals
Settings
Evaluation
Analysis
Conclusion
11
Overview (Event Causality)

Event causality:




Cause-Effect Association (CEA)
Discourse and Causality:



Discourse relations
Constraints for joint inference
Experiments:




Interaction between event causality and discourse relations
Event predicates: verbs, nominals
Settings
Evaluation
Analysis
Conclusion
12
Cause-Effect Association (CEA)
CEA: prediction of whether two events are causally related
The police arrested him because he killed someone.
13
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
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
association between
event predicates
association between
event arguments
association between the
predicate of an event and the
arguments of the other event
14
Predicate-Predicate Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
spp (ei , e j ) = PMI(pi , p j )´ IDF(pi , p j )´ Dist(pi , p j )´ max(ui , u j )
log
P ( pi , p j )
P(pi )P( p j )
15
Predicate-Predicate Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
spp (ei , e j ) = PMI(pi , p j )´ IDF(pi , p j )´ Dist(pi , p j )´ max(ui , u j )
idf (pi )´idf (p j )´idf (pi , p j )
D
idf (p) = log
1+ N
D: total number of documents in the collection
N: number of documents that p occurs in
16
Predicate-Predicate Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
spp (ei , e j ) = PMI(pi , p j )´ IDF(pi , p j )´ Dist(pi , p j )´ max(ui , u j )
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
17
Predicate-Predicate Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
spp (ei , e j ) = PMI(pi , p j )´ IDF(pi , p j )´ Dist(pi , p j )´ max(ui , u j )
takes into account whether predicates (events) pi and pj
appear most frequently with each other
18
Predicate-Predicate Association
i
j
max(u , u )
takes into account whether predicates (events) pi and pj
appear most frequently with each other
i
j
P(p
,
p
)
i
u =
max k éëP(pi , pk )ùû - P(pi , p j ) + e
ui will be maximized if there is no other predicate pk (as compared to pj)
having a higher co-occurrence probability with pi
i
j
P(p
,
p
)
j
u =
max k éëP(p k , p j )ùû - P(pi , p j ) + e
19
Predicate-Argument Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
i
i
1
i
2
i
n
j
j
1
j
2
j
m
p (a , a ,..., a )
p (a , a ,..., a )
Pair up the predicates and arguments across events,
calculate the PMI for each link, then average them
20
Argument-Argument Association
CEA(ei , e j ) = spp (ei , e j )+ s pa (ei , e j ) + saa (ei , e j )
i
i
1
i
2
i
n
j
j
1
j
2
j
m
p (a , a ,..., a )
p (a , a ,..., a )
calculate the PMI for each possible pairings of the arguments
(across the two events), then average them
21
Cause-Effect Association (CEA)
CEA score: predicts whether the two events are causally related
The police arrested him because he killed someone.
22
Overview (Event Causality)

Event causality:




Cause-Effect Association (CEA)
Discourse and Causality:



Discourse relations
Constraints for joint inference with CEA
Experiments:




Interaction between event causality and discourse relations
Event predicates: verbs, nominals
Settings
Evaluation
Analysis
Conclusion
23
Discourse and Causality Interaction
[ … e … e … ] connective [ … 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?
24
Penn Discourse Treebank (PDTB) Relations

Discourse relations:

Comparison:


Contingency:


Cause, Condition, Pragmatic-cause, Pragmatic-condition
Expansion:


Concession, Contrast, Pragmatic-concession, Pragmatic-contrast
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:
Contrast: [According to the survey, x% of Chinese Internet users prefer
Google] whereas [y% prefer Baidu].


Negative evidence for causality
Contingency:

The situation described in one text span causally influences the
situation in the other:
Cause: [The first priority is search and rescue] because [many people
are trapped under the rubble].

Positive evidence
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Discourse Relations

Expansion:

Providing additional information, illustrating alternative situations,
etc.:
Conjunction: [Over the past decade, x women were killed] and [y went
missing].


Negative evidence, except for Conjunction (which connects
arbitrary pieces of text spans)
Temporal:
Synchrony: [He was sitting at his home] when [the whole world started to
shake].

Temporal precedence of the (cause) event over the (effect) event is a
necessary, but not sufficient requisite for causality
27
Discourse and Causality Interaction
[ … e … e … ] connective [ … e … e … ]
Cause,
Condition
1
ei
ej
At least one (crossing) ep is causal
2
ei
ej
If we have a (crossing) ep which is causal
3
Comparison, Concession, Contrast,
Pragmatic-concession, Pragmatic-contrast,
Expansion, Alternative, Exception, Instantiation,
List, Restatement
Cause, Condition, Temporal,
Asynchronous, Synchrony,
Conjunction
ei
ej
No (crossing) ep is casual
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Joint Inference: Discourse & Distributional Causality

Objective function:


Arg max  LDR   sc (dr )  x c ,dr  LER   sep (er )  y ep ,er 
cC drLDR
epEP erLER


Probability that connective c
is predicted with discourse
relation dr
discourse relation
indicator variable
CEA prediction that event
pair ep takes on the causal
or not-causal label er
event pair causality
indicator variable
29
Constraints
[ … e … e … ] connective [ … e … e … ]
1
Cause,
Condition
ei
ej
At least one (crossing) ep is causal
x c,"Cause" £
å
y ep,"causal"
epÎEPc
x c ,"Cause"  y epi ,"causal "    y ep
j ,"causal "
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
30
Constraints
[ … e … e … ] connective [ … e … e … ]
2
ei
ej
If we have a (crossing) ep which is causal
y ep,"causal" £
å
dra ÎLDRa
Cause, Condition, Temporal,
Asynchronous, Synchrony,
Conjunction
x c,dra
y ep ,"causal "  x c ,"Cause"  x c ,"Condition"   x c ,"Conjunction"
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”
31
Constraints
[ … e … e … ] connective [ … e … e … ]
3
Comparison, Concession, Contrast,
Pragmatic-concession, Pragmatic-contrast,
Expansion, Alternative, Exception, Instantiation,
List, Restatement
ei
ej
No (crossing) ep is casual
x c,drb £ y ep,"Øcausal"
x c,drb Þ y ep,"Øcausal" , "drb Î{“Comparison”,”Concession”…}
If the connective is predicted with discourse relation “Comparison”,
“Concession”, …, “Restatement”; no (crossing) event pair is causally
related
32
Overview (Event Causality)

Event causality:




Cause-Effect Association (CEA)
Discourse and Causality:



Discourse relations
Constraints for joint inference
Experiments:




Interaction between event causality and discourse relations
Event predicates: verbs, nominals
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
…
Si-1
Si
Si+1
…
Document
R (relatedness)
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
36
Annotation for Causal Event Pairs

# relations
Eval
Dev
C
414
71
C+R
492
92
Annotators overlap on 10 evaluation documents.
Agreement ratio:


0.67 for C+R
0.58 for C
37
Performance on Extracting
Causality
70
60
50
PMI
ECD
CEA
CEA+Discourse
40
30
20
10
0
Rec%
Prec%
F1%
38
Performance on Extracting
Causality and Relatedness
70
60
50
PMI
ECD
CEA
CEA+Discourse
40
30
20
10
0
Rec%
Prec%
F1%
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