Mining temporal footprints from Wikipedia

[email protected] of Computer Science

Dublin, 23/08/2014

presentation 1st AHA! Workshop, COLING 2014

Mining temporal footprints from Wikipedia

Michele Filannino, Goran Nenadic

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introduction

■ Temporal information is crucial for organising

structured and unstructured data

■ Several temporal information extraction (TIE)

systems are nowadays available

● thanks to TempEval challenge series

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URL: http://www.cs.man.ac.uk/~filannim/mantime.html

ManTIME

3

http://www.cs.man.ac.uk/~filannim/mantime.html

/ 23Test with long text 4

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Immanuel Kant, Paul Guyer, and Allen W Wood. 1998. Critique of pure reason. Cambridge University Press.

temporal footprint

A temporal footprint is a

continuous period on the time-

line that temporally defines the

existence of a particular concept.

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problem

■ input: textual description of a concept

■ output: prediction of a temporal

interval

Can we predict temporal footprints from

encyclopaedic descriptions of concepts?

/ 23Examples of temporal footprints 7

Web

Cellphone

Computer

Car

Richard Feynman

Bicycle

Carl Friedrich Gauss

French revolution

Age of Enlightenment

Galileo Galilei

Leonardo Da Vinci

Christopher Columbus

Renaissance

Arming sword

High Middle Ages

Gengis Khan

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Object Person Historical period

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methodology

1. date mention extraction

2. outlier filtering

3. normal distribution fitting

4. prediction

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date mentions extraction

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freq

0.000

0.013

0.025

0.038

0.050

time (in years)1360 1410 1460 1510 1560 1610 1660 1710 1760 1810

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freq

0.000

0.013

0.025

0.038

0.050

time (in years)1360 1410 1460 1510 1560 1610 1660 1710 1760 1810

freq

0.000

0.013

0.025

0.038

0.050

time (in years)1360 1410 1460 1510 1560 1610 1660 1710 1760 1810

Gamma parameter controls the outlier region’s boundaries.

outlier filtering

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γ param.

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Alpha and Beta parameters control the size and offset of the gaussian bell.

normal distribution fitting

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freq

0.000

0.013

0.025

0.038

0.050

time (in years)1360 1410 1460 1510 1560 1610 1660 1710 1760 1810

α param.

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freq

0.000

0.013

0.025

0.038

0.050

time (in years)1360 1410 1460 1510 1560 1610 1660 1710 1760 1810

Alpha and Beta parameters control the size and offset of the gaussian bell.

normal distribution fitting

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β param.

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Fatima De Carvalho. 1996. Histogrammes et indices de proximite en analyse donne es

symboliques. Acyes de l’e cole d’e te sur l’analyse des donne es symboliques. LISE-

CEREMADE, Universite de Paris IX Dauphine, pages 101–127.

error measure

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union overlap

gold

prediction

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Fatima De Carvalho. 1996. Histogrammes et indices de proximite en analyse donne es

symboliques. Acyes de l’e cole d’e te sur l’analyse des donne es symboliques. LISE-

CEREMADE, Universite de Paris IX Dauphine, pages 101–127.

error measure

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union

gold

prediction

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strategies

A. RegEx

B. RegEx + Filtering

C. RegEx + Filtering + Gaussian fitting

D. HeidelTime + Filtering + Gaussian fitting

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evaluation

■ subject: people

■ lived from 1000 AD to 2014

● text from Wikipedia web pages

● year of birth and death from DBpedia

■ 228,824 people collected

■ simple definition of temporal footprint

● birth and death dates

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/ 23People per textual length 18

#p

eo

ple

0

100

200

300

400

500

#words

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750

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aggregate results

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StrategyMean

Distance Error

Standard Deviation

RegEx 0.2636 0.3409

RegEx + Filtering 0.2596 0.3090

RegEx + Filtering + Gaussian fitting 0.3503 0.2430

HeidelTime + Filtering + Gaussian fitting 0.5980 0.2470

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results

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MD

E

0.0

0.2

0.4

0.6

0.8

1.0

#words

1112 3336 5560 7785 10009 12233 14458 16682 18906 21131 23355 25579 27804

RegEx RegEx + FilteringHeidelTime + Filtering + Gaussian fitting RegEx + Filtering + Gaussian fitting

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E: 0.204

results

■ Galileo Galilei (1564-1642), prediction: 1556-1654

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E: 0.159

results

■ Robin Williams (1951 - 2014), prediction: 1953-2006

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Prediction: 1366-2057 (1451-1506), E: 0.92

other types of temporal footprint?

■ Christopher Columbus will die in 2057 ?!

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Prediction: 1366-2057 (1451-1506), E: 0.92

other types of temporal footprint?

■ Christopher Columbus will die in 2057 ?!

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AHA!

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physical existence vs. social coverage

■ Anne Frank’s footprint is shifted in the future

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conclusions

■ how the methodology behaves on different

languages? how on different sources?

■ oracle-like side-effect behaviour:

• Apple Inc. will be closed down this year

• Stanford University will be closed down in 2029

■ Future works

• mixture of normal distributions

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Thank you.

Contact:

[email protected] !Visit:

tinyurl.com/temporal-footprints

?QUESTIONS

Mining temporal footprints from Wikipedia

Science

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