Relation Extraction

What is relation extraction?

Dan Jurafsky

Extracting relations from text

• Company report: “International Business Machines Corporation (IBM or the company) was incorporated in the State of New York on June 16, 1911, as the Computing-Tabulating-Recording Co. (C-T-R)…”
• Extracted Complex Relation:

Company-Founding

Company IBM

Location New York

Date June 16, 1911

Original-Name Computing-Tabulating-Recording Co.

• But we will focus on the simpler task of extracting relation triples

Founding-year(IBM,1911)

Founding-location(IBM,New York)

Dan Jurafsky

Extracting Relation Triples from Text

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California … near Palo Alto, California… Leland Stanford…founded the university in 1891

Stanford EQ Leland Stanford Junior University

Stanford LOC-IN California

Stanford IS-A research university

Stanford LOC-NEAR Palo Alto

Stanford FOUNDED-IN 1891

Stanford FOUNDER Leland Stanford

Dan Jurafsky

Why Relation Extraction?

• Create new structured knowledge bases, useful for any app
• Augment current knowledge bases
• Adding words to WordNet thesaurus, facts to FreeBase or DBPedia
• Support question answering
• The granddaughter of which actor starred in the movie “E.T.”?

(acted-in ?x “E.T.”)(is-a ?y actor)(granddaughter-of ?x ?y)

• But which relations should we extract?

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Dan Jurafsky

Automated Content Extraction (ACE)

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17 relations from 2008 “Relation Extraction Task”

Dan Jurafsky

Automated Content Extraction (ACE)

• Physical-Located PER-GPE

He was in Tennessee

• Part-Whole-Subsidiary ORG-ORG

XYZ, the parent company of ABC

• Person-Social-Family PER-PER

John’s wife Yoko

• Org-AFF-Founder PER-ORG

Steve Jobs, co-founder of Apple…

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Dan Jurafsky

UMLS: Unified Medical Language System

• 134 entity types, 54 relations

Injury disrupts Physiological Function

Bodily Location location-of Biologic Function

Anatomical Structure part-of Organism

Pharmacologic Substance causes Pathological Function

Pharmacologic Substance treats Pathologic Function

Dan Jurafsky

Extracting UMLS relations from a sentence

Doppler echocardiography can be used to diagnose left anterior descending artery stenosis in patients with type 2 diabetes

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Echocardiography, Doppler DIAGNOSES Acquired stenosis

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Dan Jurafsky

Databases of Wikipedia Relations

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Relations extracted from Infobox

Stanford state California

Stanford motto “Die Luft der Freiheit weht”

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Wikipedia Infobox

Dan Jurafsky

Relation databases �that draw from Wikipedia

• Resource Description Framework (RDF) triples

subject predicate object

Golden Gate Park location San Francisco

dbpedia:Golden_Gate_Park dbpedia-owl:location dbpedia:San_Francisco

• DBPedia: 1 billion RDF triples, 385 from English Wikipedia
• Frequent Freebase relations:

people/person/nationality, location/location/contains

people/person/profession, people/person/place-of-birth

biology/organism_higher_classification film/film/genre

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Dan Jurafsky

Ontological relations

• IS-A (hypernym): subsumption between classes
• Giraffe IS-A ruminant IS-A ungulate IS-A mammal IS-A vertebrate IS-A animal…

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• Instance-of: relation between individual and class
• San Francisco instance-of city

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Examples from the WordNet Thesaurus

Dan Jurafsky

How to build relation extractors

1. Hand-written patterns
2. Supervised machine learning
3. Semi-supervised and unsupervised
• Bootstrapping (using seeds)
• Distant supervision
• Unsupervised learning from the web

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Dan Jurafsky

Relation Extraction

What is relation extraction?

Dan Jurafsky

Relation Extraction

Using patterns to extract relations

Dan Jurafsky

Rules for extracting IS-A relation

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Early intuition from Hearst (1992)

• “Agar is a substance prepared from a mixture of red algae, such as Gelidium, for laboratory or industrial use”
• What does Gelidium mean?
• How do you know?`

Dan Jurafsky

Rules for extracting IS-A relation

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Early intuition from Hearst (1992)

• “Agar is a substance prepared from a mixture of red algae, such as Gelidium, for laboratory or industrial use”
• What does Gelidium mean?
• How do you know?`

Dan Jurafsky

Hearst’s Patterns for extracting IS-A relations

(Hearst, 1992): Automatic Acquisition of Hyponyms

“Y such as X ((, X)* (, and|or) X)”

“such Y as X”

“X or other Y”

“X and other Y”

“Y including X”

“Y, especially X”

Dan Jurafsky

Hearst’s Patterns for extracting IS-A relations

Dan Jurafsky

Extracting Richer Relations Using Rules

• Intuition: relations often hold between specific entities
• located-in (ORGANIZATION, LOCATION)
• founded (PERSON, ORGANIZATION)
• cures (DRUG, DISEASE)
• Start with Named Entity tags to help extract relation!

Dan Jurafsky

Named Entities aren’t quite enough.�Which relations hold between 2 entities?

Drug

Disease

Cure?

Prevent?

Cause?

Dan Jurafsky

What relations hold between 2 entities?

PERSON

ORGANIZATION

Founder?

Investor?

Member?

Employee?

President?

Dan Jurafsky

Extracting Richer Relations Using Rules and�Named Entities

Who holds what office in what organization?

PERSON, POSITION of ORG

• George Marshall, Secretary of State of the United States

PERSON(named|appointed|chose|etc.) PERSON Prep? POSITION

• Truman appointed Marshall Secretary of State

PERSON [be]? (named|appointed|etc.) Prep? ORG POSITION

• George Marshall was named US Secretary of State

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Dan Jurafsky

Hand-built patterns for relations

• Plus:
• Human patterns tend to be high-precision
• Can be tailored to specific domains
• Minus
• Human patterns are often low-recall
• A lot of work to think of all possible patterns!
• Don’t want to have to do this for every relation!
• We’d like better accuracy

Dan Jurafsky

Relation Extraction

Using patterns to extract relations

Dan Jurafsky

Relation Extraction

Supervised relation extraction

Dan Jurafsky

Supervised machine learning for relations

• Choose a set of relations we’d like to extract
• Choose a set of relevant named entities
• Find and label data
• Choose a representative corpus
• Label the named entities in the corpus
• Hand-label the relations between these entities
• Break into training, development, and test
• Train a classifier on the training set

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Dan Jurafsky

How to do classification in supervised relation extraction

1. Find all pairs of named entities (usually in same sentence)
2. Decide if 2 entities are related
3. If yes, classify the relation
1. Why the extra step?
• Faster classification training by eliminating most pairs
• Can use distinct feature-sets appropriate for each task.

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Dan Jurafsky

Automated Content Extraction (ACE)

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17 sub-relations of 6 relations from 2008 “Relation Extraction Task”

Dan Jurafsky

Relation Extraction

Classify the relation between two entities in a sentence

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.

SUBSIDIARY

FAMILY

EMPLOYMENT

NIL

FOUNDER

CITIZEN

INVENTOR

…

Dan Jurafsky

Word Features for Relation Extraction

• Headwords of M1 and M2, and combination

Airlines Wagner Airlines-Wagner

• Bag of words and bigrams in M1 and M2

{American, Airlines, Tim, Wagner, American Airlines, Tim Wagner}

• Words or bigrams in particular positions left and right of M1/M2

M2: -1 spokesman

M2: +1 said

• Bag of words or bigrams between the two entities

{a, AMR, of, immediately, matched, move, spokesman, the, unit}

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said

Mention 1

Mention 2

Dan Jurafsky

Named Entity Type and Mention Level�Features for Relation Extraction

• Named-entity types
• M1: ORG
• M2: PERSON
• Concatenation of the two named-entity types
• ORG-PERSON
• Entity Level of M1 and M2 (NAME, NOMINAL, PRONOUN)
• M1: NAME [it or he would be PRONOUN]
• M2: NAME [the company would be NOMINAL]

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said

Mention 1

Mention 2

Dan Jurafsky

Parse Features for Relation Extraction

• Base syntactic chunk sequence from one to the other

NP NP PP VP NP NP

• Constituent path through the tree from one to the other

NP 🡹 NP 🡹 S 🡹 S 🡻 NP

• Dependency path

Airlines matched Wagner said

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said

Mention 1

Mention 2

Dan Jurafsky

Gazeteer and trigger word features for relation extraction

• Trigger list for family: kinship terms
• parent, wife, husband, grandparent, etc. [from WordNet]
• Gazeteer:
• Lists of useful geo or geopolitical words
• Country name list
• Other sub-entities

Dan Jurafsky

American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.

Dan Jurafsky

Classifiers for supervised methods

• Now you can use any classifier you like
• MaxEnt
• Naïve Bayes
• SVM
• ...
• Train it on the training set, tune on the dev set, test on the test set

Dan Jurafsky

Evaluation of Supervised Relation Extraction

• Compute P/R/F₁ for each relation

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Dan Jurafsky

Summary: Supervised Relation Extraction

+ Can get high accuracies with enough hand-labeled training data, if test similar enough to training

- Labeling a large training set is expensive

- Supervised models are brittle, don’t generalize well to different genres

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Dan Jurafsky

Relation Extraction

Supervised relation extraction

Dan Jurafsky

Relation Extraction

Semi-supervised and unsupervised relation extraction

Dan Jurafsky

Seed-based or bootstrapping approaches to relation extraction

• No training set? Maybe you have:
• A few seed tuples or
• A few high-precision patterns
• Can you use those seeds to do something useful?
• Bootstrapping: use the seeds to directly learn to populate a relation

Dan Jurafsky

Relation Bootstrapping (Hearst 1992)

• Gather a set of seed pairs that have relation R
• Iterate:
1. Find sentences with these pairs
2. Look at the context between or around the pair and generalize the context to create patterns
3. Use the patterns for grep for more pairs

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Dan Jurafsky

Bootstrapping

• <Mark Twain, Elmira> Seed tuple
• Grep (google) for the environments of the seed tuple

“Mark Twain is buried in Elmira, NY.”

X is buried in Y

“The grave of Mark Twain is in Elmira”

The grave of X is in Y

“Elmira is Mark Twain’s final resting place”

Y is X’s final resting place.

• Use those patterns to grep for new tuples
• Iterate

Dan Jurafsky

Dipre: Extract <author,book> pairs

• Start with 5 seeds:

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• Find Instances:

The Comedy of Errors, by William Shakespeare, was

The Comedy of Errors, by William Shakespeare, is

The Comedy of Errors, one of William Shakespeare's earliest attempts

The Comedy of Errors, one of William Shakespeare's most

• Extract patterns (group by middle, take longest common prefix/suffix)

?x , by ?y , ?x , one of ?y ‘s

• Now iterate, finding new seeds that match the pattern

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Brin, Sergei. 1998. Extracting Patterns and Relations from the World Wide Web.

Dan Jurafsky

�Snowball

• Similar iterative algorithm

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• Group instances w/similar prefix, middle, suffix, extract patterns
• But require that X and Y be named entities
• And compute a confidence for each pattern

{’s, in, headquarters}

{in, based}

ORGANIZATION

LOCATION

E. Agichtein and L. Gravano 2000. Snowball: Extracting Relations

from Large Plain-Text Collections. ICDL

ORGANIZATION

LOCATION

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Dan Jurafsky

Distant Supervision

• Combine bootstrapping with supervised learning
• Instead of 5 seeds,
• Use a large database to get huge # of seed examples
• Create lots of features from all these examples
• Combine in a supervised classifier

Snow, Jurafsky, Ng. 2005. Learning syntactic patterns for automatic hypernym discovery. NIPS 17

Fei Wu and Daniel S. Weld. 2007. Autonomously Semantifying Wikipeida. CIKM 2007

Mintz, Bills, Snow, Jurafsky. 2009. Distant supervision for relation extraction without labeled data. ACL09

Dan Jurafsky

Distant supervision paradigm

• Like supervised classification:
• Uses a classifier with lots of features
• Supervised by detailed hand-created knowledge
• Doesn’t require iteratively expanding patterns
• Like unsupervised classification:
• Uses very large amounts of unlabeled data
• Not sensitive to genre issues in training corpus

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Dan Jurafsky

Distantly supervised learning �of relation extraction patterns

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For each relation

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For each tuple in big database

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Find sentences in large corpus with both entities

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Extract frequent features (parse, words, etc)

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Train supervised classifier using thousands of patterns

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PER was born in LOC

PER, born (XXXX), LOC

PER’s birthplace in LOC

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<Edwin Hubble, Marshfield>

<Albert Einstein, Ulm>

Born-In

Hubble was born in Marshfield

Einstein, born (1879), Ulm

Hubble’s birthplace in Marshfield

P(born-in | f₁,f₂,f₃,…,f₇₀₀₀₀)

Dan Jurafsky

Unsupervised relation extraction

• Open Information Extraction:
• extract relations from the web with no training data, no list of relations

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1. Use parsed data to train a “trustworthy tuple” classifier
2. Single-pass extract all relations between NPs, keep if trustworthy
3. Assessor ranks relations based on text redundancy

(FCI, specializes in, software development)

(Tesla, invented, coil transformer)

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M. Banko, M. Cararella, S. Soderland, M. Broadhead, and O. Etzioni. 2007. Open information extraction from the web. IJCAI

Dan Jurafsky

Evaluation of Semi-supervised and�Unsupervised Relation Extraction

• Since it extracts totally new relations from the web
• There is no gold set of correct instances of relations!
• Can’t compute precision (don’t know which ones are correct)
• Can’t compute recall (don’t know which ones were missed)
• Instead, we can approximate precision (only)
• Draw a random sample of relations from output, check precision manually

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• Can also compute precision at different levels of recall.
• Precision for top 1000 new relations, top 10,000 new relations, top 100,000
• In each case taking a random sample of that set
• But no way to evaluate recall

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Dan Jurafsky

Relation Extraction

Semi-supervised and unsupervised relation extraction

Dan Jurafsky