Template-Based Event Extraction

Kevin Reschke – Aug 15th 2013

Martin Jankowiak, Mihai Surdeanu, Dan Jurafsky, Christopher Manning

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Outline• Recap from last time

• Distant supervision• Plane crash dataset

• Current work• Fully supervised setting• MUC4 terrorism dataset

Underlying theme: Joint Inference Models

Goal: Knowledge Base Population

“… Delta Flight 14

crashed in Mississippikilling 40 …”

…

<Plane Crash>

<Flight Number = Flight 14>

<Operator = Delta>

<Fatalities = 40>

<Crash Site = Mississippi> …

News Corpus Knowledge Base

Distant SupervisionUse known events to automatically label training data.

Training Knowledge-Base

<Plane crash>

<Flight Number = Flight 11>

<Operator = USAir>

<Fatalities = 200>

<Crash Site = Toronto>

One year after [USAir]Operator [Flight 11]FlightNumber

crashed in [Toronto]CrashSite, families of the [200]Fatalities victims attended a memorial service in [Vancouver]NIL.

Plane Crash Dataset80 plane crashes from Wikipedia infoboxes. Training set: 32; Dev set: 8; Test set: 40

Corpus: Newswire data from 1989 – present.

Extraction Models• Local Model

• Train and classify each mention independently.

• Pipeline Model• Classify sequentially; use previous label as

feature.• Captures dependencies between labels.

• E.g., Passengers and Crew go together:“4 crew and 200 passengers were on board.”

• Joint Model• Searn Algorithm (Daumé III et al., 2009).• Jointly models all mentions in a sentence.

Results

Precision Recall F1 score

Baseline (Maj. Class)

0.026 0.237 0.047

Local Model 0.159 0.407 0.229

Pipeline Model 0.154 0.422 0.226

Joint Model 0.213 0.422 0.283

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Fully Supervised Setting:MUC4 Terrorism Dataset

• 4th Message Understanding Conference (1992).

• Terrorist activities in Latin America.

• 1700 docs ( train / dev / test = 1300 / 200 / 200 ).

• 50/50 mix of relevant and irrelevant doc.

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MUC4 Task• 5 slots types:

• Perpetrator Individual (PerpInd)• Perpetrator Organization (PerpOrg)• Physical Target (Target)• Victim (Victim)• Weapon (Weapon)

• Task: Identify all slot fills in each document.• Don’t worry about differentiating multiple

events.

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MUC4 Example

THE ARCE BATTALION COMMAND HAS REPORTED

THAT ABOUT 50 PEASANTS OF VARIOUS AGES

HAVE BEEN KIDNAPPED BY TERRORISTS OF THE

FARABUNDO MARTI NATIONAL LIBERATION FRONT [FMLN] IN

SAN MIGUEL DEPARTMENT.

Victim

PerpInd

PerpOrg

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MUC4 Example

THE ARCE BATTALION COMMAND HAS REPORTED

THAT ABOUT 50 PEASANTS OF VARIOUS AGES

HAVE BEEN KIDNAPPED BY TERRORISTS OF THE

FARABUNDO MARTI NATIONAL LIBERATION FRONT [FMLN] IN

SAN MIGUEL DEPARTMENT.

PerpInd

PerpOrg

NIL

NIL

Victim

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Baseline Results• Local Mention Model

• Multiclass logistic regression.• Pipeline Mention Model

• Previous non-NIL label (or “none”) is feature for current mention.

Precision Recall F1 scoreLocal 0.522 0.448 0.478Pipeline 0.578 0.405 0.471

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Observation 1:• Local context is insufficient.• Need sentence-level measure. (Patwardhan & Riloff,

2009)

Two bridges were destroyed

. . . in Baghdad last night in a resurgence ofbomb attacks in the capital city.

. . . and $50 million in damage was caused bya hurricane that hit Miami on Friday.

. . . to make way for modern, safer bridgesthat will be constructed early next year.

✓

✗

✗

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Baseline Models + Sentence Relevance• Binary relevance classifier – unigram / bigram features• HardSent:

• Discard all mentions in irrelevant sentences.• SoftSent:

• Sentence relevance is feature for mention classification.

Precision Recall F1 score

Local 0.522 0.448 0.478Local w/ HardSent 0.770 0.241 0.451Local w/ SoftSent 0.527 0.446 0.478Pipeline 0.578 0.405 0.471Pipeline w/ SoftSent 0.613 0.429 0.500

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Observation 2:• Sentence relevance depends on surrounding

context. (Huang & Riloff, 2012)

“Obama was attacked.” (political attack vs. terrorist attack)

“He use a gun.” (weapon in terrorist event?)

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Joint Inference Models• Idea: Model sentence relevance and mention

labels jointly – yield globally optimal decisions.

• Machinery: Conditional Random Fields (CRFs).• Model joint probability of relevance labels and mention

labels conditioned on input features.• Encode dependencies among labels.

• Software: Factorie (http://factorie.cs.umass.edu)• Flexibly design CRF graph structures.• Learning / Classification algorithms with exact and

approximate inference.

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First Pass• Fully joint model.

S

M M M

• Approximate inference a likely culprit.

Precision Recall F1 score

Mention Pipeline w/ SoftSent

0.613 0.429 0.500

Fully Joint Model 0.54 0.39 0.45

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Second Pass• Two linear-chain CRFs with relevance

threshold.

S S S

M M MPrecision Recall F1 score

Mention Pipeline w/ SoftSent

0.613 0.429 0.500

Fully Joint Model 0.54 0.39 0.45CRF: Mention Chain Only 0.485 0.470 0.470CRF: Sentence Chain Only 0.535 0.422 0.463CRF: Both Chains 0.513 0.470 0.481

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Analysis• Many errors are reasonable extractions, but

come from irrelevant documents.

• Learned CRF model weights:RelLabel<+,<NIL>> = -0.071687RelLabel<+,Vict> = 0.716669RelLabel<-,Vict> = -1.688919 ...

RelRel<+, +> = -0.609790 RelRel<+, -> = -0.469663 RelRel<-, +> = -0.634649 RelRel<-, -> = 0.572855

The kidnappers were accused of kidnapping several businessmen for high sums of Money.

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Possibilities for improvement• Label-specific relevance thresholds.

• Leverage Coref (Skip Chain CRFs).

• Incorporate doc-level relevance signal.

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State of the art• Huang & Riloff (2012)

• P / R / F1 : 0.58 / 0.60 / 0.59

• CRF sentence model with local mention classifiers.

• Textual cohesion features to model sentence chains.

• Multiple binary mention classifiers (SVMs).

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Future Work• Apply CRF models to plane crash dataset.

• New terrorism dataset from Wikipedia.

• Hybrid models: combine supervised MUC4 data with distant supervision on Wikipedia data.

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Thanks!