coreference based event-argument relation extraction on biomedical text katsumasa yoshikawa 1),...
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Coreference Based Event-Argument Relation Extraction on
Biomedical Text
Katsumasa Yoshikawa1), Sebastian Riedel2), Tsutomu Hirao3), Masayuki Asahara1), Yuji Matsumoto1)
1) Nara Institute of Science and Technology, Japan2) University of Massachusetts, Amherst, USA
3) NTT Communication Science Lab. Japan
SMBM 201025th - 26th October, 2010 Hinxton, Cambridge, UK
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Outline Research summary
Related work of event extraction
Proposed coreference based approach
Experimental setup and highlighted data
Conclusion and future work
3
Summary of Our Research
Coreference Based Approach for biomedical event extraction with Markov Logic
Why coreference?– Extraction of valuable event-argument relations in di
scourse structure– Identification of arguments crossing sentence bound
aries
Why Markov Logic?– Implementation of Salience in Discourse and Tran
sitivity in very direct fashion
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We analyzed the effect on the binding and the activity of transcription factors at a regulatory element.
TPA induction inhibits the binding of the transcription factor NF-E2 to this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause ThemeTheme
Theme Theme
S1
S2
S3
Arguments are often related to the other mentions through coreference relations
Event-Argument Relation with Coreference Information
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"this element" in S2 is coreferent to… "a regulatory element" in S1
We analyzed the effect on the binding and the activity of transcription factors at a regulatory element.
Corefer
TPA induction inhibits the binding of the transcription factor NF-E2 to this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause ThemeTheme
Theme Theme
S1
S2
S3
Event-Argument Relation with Coreference Information
6
The true argument (Theme) of binding is "a regulatory element“ and "this element" is just an anaphor of it
Transitivity enables us to extract it
We analyzed the effect on the binding and the activity of transcription factors at a regulatory element.
(B) Corefer(C) Theme
TPA induction inhibits the binding of the transcription factor NF-E2 to this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause ThemeTheme
Theme (A) Theme
S1
S2
S3
Event-Argument Relation with Coreference Information
(A) Theme & (B) Corefer => (C) Theme
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Arguments mentioned over and over again have higher salience in discourse and should be extracted at any cost
Our approach can aggressively extracts such arguments that are valuable in discourse structure
We analyzed the effect on the binding and the activity of transcription factors at a regulatory element.
CoreferTheme
TPA induction inhibits the binding of the transcription factor NF-E2 to this transcriptional control element.
TPA induction increases the binding of AP-1 factors to this element.
Cause ThemeTheme
Theme Theme
Theme
CoreferTheme
S1
S2
S3
Event-Argument Relation with Coreference Information
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Outline Research summary
Related work of event extraction
Proposed coreference based approach
Experimental setup and highlighted data
Conclusion and future work
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Biomedical Event Extraction(BioNLP'09 Task 1)
Extracting events, arguments, and their relations in a document
TPA induction increases the binding of AP-1 factors to this element.
Cause ThemeTheme
Theme
eventevent event
argument argument argumentargument
Main targets : Event-Argument relations (E-As)
argument
Theme
Example
event induction, increases, binding
argument TPA, AP-1 factors, this element, induction, binding
event-argument Theme(induction-TPA), Cause(increases, induction), Theme(increases, binding), Theme(binding, AP-1 factors), Theme(binding, this element)
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Previous Work [in BioNLP’09]
Pairwise pipeline by SVM classifiers [Bjorne et al., 2009]
eventarg1 arg2
1. Identification of events 2. Coupling with proteins and labeling the roles
eventarg1 arg2
Ｎ oTheme
event1arg1 arg2 event2 arg3
Theme
Theme Cause Cause
Collective approach by Markov Logic[Riedel et al., 2009] [Poon et al., 2010]
1. Jointly identify the most probable E-A assignments in a sentence
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Outline Research summary
Related work of event extraction
Proposed coreference based approach
Experimental setup and highlighted data
Conclusion and future work
12
Markov Logic[Richardson and Domingos, 2006]
A Statistical Relational Learning framework An expressive template language of Markov N
etworks Not only hard but also soft constraints A Markov Logic Network (MLN) is a set of pair
s (φ, w) where– φ is a formula in first-order logic– w is a real number weight
Higher weight stronger constraint
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Coreference Based Event Extraction with Markov Logic
Hidden predicate (Query)predicate description
event(i) token i is an event
eventType(i,t) token i is an event with type t
role(i,j,r) token i has an argument j with role r
Observed predicate (Given)predicate description
pos(i,p) token i has part-of-speech p
protein(i) token i is a protein
dep(i,j,d) token i depends on token j
Features are described by combinations of these predicates
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Example of Markov Logic Networks
pos(3,Verb)
event(3)
wa(Verb) wb(regulation, Theme)
role (3,6,Theme)
protein(6)
wc(obj,Theme)
dep(3,6,obj)
Weight Function Weight value
Ground Formula
wa(Verb) 3.1 pos(3,Verb) event(3)⇒
wb(regulation,Theme) -0.9 event(3) ^ eventType(3,regulation) ^ protein(6) role(3,6,Theme)⇒
Feature definition by weighted First-Order Logic
)Theme,6,3()obj,6,3(
otherwise
if
0
116/,13/
roledepf ji
※ all features are binary
)Theme,,()obj,,( jirolejidep
eventType(3,regulation)
grounded
grounding
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Basic Ideas of Proposed Method
Effective employment of coreference information based on discourse structure– Salience in Discourse : aggressive extraction
of valuable E-As
Consider event-argument relations crossing sentence boundaries– Transitivity involving coreference relations
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How to Use Coreference with Markov Logic?
1. Salience in Discourse2. Transitivity 3. Feature Copy
Theme CauseCorefer
Theme
S1
S2
The IRF-2 promoter region contains a CpG island .
The region is inducible by both interferons .
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
predicate description
corefer(i,j) token i is coreferent to token j
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Coreference Based Approach ①（ Salience in Discourse ）
Tokens coreferent to something have higher salience in discourse and are more likely to be arguments of events
ThemeCorefer
S1
S2
The IRF-2 promoter region contains a CpG island .
The region is inducible by both interferons .
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
)(),(.),( predeventarg,rpredrolepredantargcorefer
If "The region" is coreferent to "The IRF-2...", then there is at least one event related to "The region"
・・・（ SiD)
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Coreference Based Approach ②（ Transitivity ）
Transition rules involving coreference relations allow us to extract cross sentential event-arguments with "sentence by sentence" manner
(A) Theme
(B) Corefer(C) Theme
S1
S2
The IRF-2 promoter region contains a CpG island .
The region is inducible by both interferons .
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
)Theme,4,13()4,11()Theme,11,13( rolecoreferrole (A) (B) (C)
),,(),()r,,( rantpredroleantargcoreferargpredrole ・・・（ T)
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Coreference Based Approach③（ Feature Copy ）
If a token coreferent to something, then we exploit the features of antecedents to identify intra sentential E-A relations
Theme
Corefer
S1
S2
The IRF-2 promoter region contains a CpG island .
The region is inducible by both interferons .
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
)Theme,11,13()"2IRF",4()4,11( rolewordcorefer
Copy
)r,,(),(),( argpredrolewantwordantargcorefer ・・・(FC)
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Outline Research summary
Related work of event extraction
Proposed coreference based approach
Experimental setup and highlighted data
Conclusion and future work
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Experimental Setup
Data ： GENIA Event Corpus ver. 0.9 [Kim et al., 2008]
– Preprocess : POS tagging, NE tagging, Parsing Coreference resolver ： pairwise model [Soon et al.,
2001]
– Learning & Inference ： SVM Event extraction:
– Joint Markov Logic model [Riedel et al., 2009]Learning : one-best MIRAInference : ILP solver with CPI [Riedel, 2008]Provided by Markov thebeast
– SVM pipeline [Bjorne et al., 2009]Learning & Inference ： multi-class SVM
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Experimental Result (Summary)
Results of Event Extraction (F1)
We got statistically significant improvements by both models, SVM and MLN
Model Coreference event eventType role
1)
SVM
w/o 77.0 67.8 52.3 ( 0.0)
2)
with resolver 77.0 67.8 53.6 (+1.3)
3)
with gold 77.0 67.8 55.4 (+3.1)
4)
MLN
w/o 80.5 70.6 51.7 ( 0.0)
5)
with resolver 80.8 70.6 53.8 (+2.1)
6)
with gold 81.2 70.8 56.7 (+5.0)
ρ< 0.01 (McNemar’s test, 2-tailed)
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Three Types of E-A Relations
(2) W-ANT (3) NormalCorefer
(1) Cross
S1
S2
The IRF-2 promoter region contains a CpG island .
The region is inducible by both interferons .
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17
Type Description
(1) Cross link E-A relations crossing sentence boundaries
(2) With-Antecedent Intra-sentence E-As with antecedents
(3) Normal Neither Cross-link nor With-Antecedent
Evaluation for the three types of E-A relations
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Experimental Result (E-A Relation)
Results of E-A Relation Extraction (F1)
Both Transitivity and Salience in Discourse work well MLN with gold coreference annotations outperforms SV
M pipeline both on Cross and on W-ANT
Model Coreference Cross-link With-Antecedent Normal
1)
SVM
w/o 0.0 56.0 53.6
2)
with resolver 27.9 57.0 54.3
3)
with gold 54.1 57.3 55.4
4)
MLN
w/o 0.0 49.8 ( 0.0) 53.2
5)
with resolver 39.3 56.5 (+6.7) 54.3
6)
with gold 69.7 66.7(+16.9) 55.3
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Outline Research summary
Related work of event extraction
Proposed coreference based approach
Experimental setup and highlighted data
Conclusion and future work
26
Summary
We proposed a new method for biomedical event extraction with coreference information
Our systems successfully extract cross-sentential E-As by transitivity including coreference relations
The concept of salience in discourse can also help E-A extraction
We got further improvements with gold coreference annotations especially for MLN
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Future Work
Make more effort to coreference resolution– From pairwise model to clustering approach
Full joint approach of event extraction and coreference resolution– Fighting against computational costs– Narrative Event Chains [Chambers et al., 2008]