1. Extracting Complex Biological Events
with Rich Graph­Based Feature Sets
Jari Björne, Juho Heimonen, Filip Ginter, Antti
Airola, Tapio Pahikkala, Tapio Salakoski
BioNLP 2009 Workshop
Farzaneh Sarafraz
18 June 2009

2. BioNLP'09 Task 1
 Events in abstracts
 Given: gene and gene products (proteins)
 Wanted: events
− type
− trigger
− participant(s)
− cause (if applicable)

3. Example
"I kappa B/MAD­3 masks the nuclear localization
signal of NF­kappa B p65 and requires the
transactivation domain to inhibit NF­kappa B
p65 DNA binding. "
Event: negative regulation
Trigger: masks
Theme1: the first p65
Cause: MAD­3

4. Event Types
 Gene expression  Binding
 Transcription  Regulation
 Protein Catabolism  Positive regulation
 Localisation  Negative regulation
 Phosphorylation

5. Training and Test Data
 Training data: 800 abstracts
 Development data: 150 abstracts
 Test data: 260 abstracts

6. The System
 Trigger recognition
− Methods similar to NER
− Classification
 Argument detection
− Graph edge selection
− Classification
 Semantic post­processing
− Rule­based

7. Trigger Detection
 Token labelling (one for each type and one ­)
 92% of triggers are single token
− Adjacent tokens form a trigger if they appear in the
training data
 Triggers that share a token:
− Combined class: gene expression/pos regulation
 A graph node for each trigger
− Not duplicated just yet

8. Classification ­ SVM
 Token features
− Binary: capitalisation, presence of punctuation or
numeric characters
− Stem
− Character bigrams and trigrams
− Token is known triggers in training data
− All the above for linear and dependency
“neighbours”

9. Classification ­ SVM
 Frequency features
− # of named entities
 In sentence
 In a linear window around the token
 Bag­of­words count of token texts in the sentence (?)
 Dependency chains
− Up to depth of 3 from the token are constructed
− At each depth both token and frequency features
− Plus dep type and sequence of dep types in chain

10. Two SVMs
 “Somewhat” different feature sets
 Combined weighted results
“This design should be considered an artifact of
the time­constrained, experiment­driven
development of the system rather than a
principled design”

11. Precision/Recall trade­off
 Undetected trigger ­­> undetected event
 All triggers have events in the training data ­­>
bias towards reporting an event for all detected
triggers
 Adjust P/R explicitly
− multiply the negative class by β
− find β experimentally

12. Edge Detection
 Multi­class SVM
 All potential directed edges
− Event node to named entity
− Event node to event node (nested event)
− Labelled as theme, cause, or negative
 Each edge is predicted independently

13. Feature Set – Central Concept
Shortest undirected
path of syntactic
dependencies in the
Stanford scheme
parse of the
sentence.

14. Feature Set
 Token text, POS, entity/event class,
dependency (subject)
 N­grams: merging the attributes of 2­4
− Consecutive tokens
− Consecutive dependencies
− Each token and two neighbouring dependencies
− Each dependency and two neighbouring tokens
− One bigram showing direction

15. Other Features
 Individual component features
 Semantic node features
 Frequency features

16. Semantic Post­Processing
 Duplicate nodes
− Same class and same trigger
− Combined trigger
 Remove improper arguments
 Remove directed cycles by removing the
weakest link

17. Duplicating Event Nodes
 Task restrictions
− Two causes,
− must have theme,
− etc.
 Several heuristics
 x­th first dependency
in shortest path from
the event for binding

18. Results

19. Compared to Us

20. What Didn't Work/Wasn't Tried
 CRF
 HMM
 Removing strong independence assumption
 Co­reference resolution (4.8%)

21. End.