Visualization for
Statistical Term Network
in Newspaper
Hideki Kawai†, Kazuo Kunieda†, Keiji Yamada†,
Haruka Saito, Masaaki Tsuchida, Hironori Mizuguchi† NEC C&C

Innovation Research Laboratories
NEC Common Platform Software Research Laboratories

1Background Complex relations

between variousproblems
 Causal relations
 Butterfly effect Ripple effectEconomyEconomy Idea Support

 Focusing on

Statistical Terms

Global Warming

Oil Prices?
Hurricane?
?

?

 Global Dynamics
 Global Solution

Environment
Environment?
Crime rate?
?
GlobalGlobalDynamics
Dynamics?
NetworkNetwork?
?

?

SocialSocialProblems
Problems?
?

Food production?

?

Agriculture
Agriculture2Motivation
 Web contains several billions of text pages
 Text contains information like causal relations

Ex1: “the rise in crude oil prices stimulated ethanoldemand”
 Ex2: “farmers to answer to the ethanol demand, leave lessland available to food production”
Food production
Crude oil prices

Food prices
Transportation

Ethanol demand

Riot
Famine3Goal
 Visualize Global Dynamics as a

Statistical Term Network Node：Statistical terms
 Edge：Relationship between terms
・・・reducing payroll

・・・an economic
recession often
leads to highunemploymentrates.・・・

Unemployment rates

taxes is the mostsuccessful in
reducing
unemploymentrates・・・

Doc B
Doc A

Economic recession

Payroll taxes

4What are we looking for?
 Nodes:
Nodes Expressions
 Statistical terms (relative to a movement) Ex. Unemployment rates, birth rates, crime rates, oilprices, corn prices
 Suffixes:
Suffixes Rates, prices, costs
 Events & Facts
 Ex. Hurricanes, riot, war
 Ex. Urbanization, sustainable resources, globalwarning Classes:
Classes natural disasters, social, economics

 Extraction
Extractionwith
withthethehelp
helpofofsuffix
suffix&&classclass
patterns
patterns
5

What are we looking for?
 Edges: Relations
 Link between a cause and its effect In text: Cause & effect are expressions

linked via some verbs.
 For causal relation, in 80% of case: cause& effect are in the same sentence [1] We
We will
will extract
extract sentences
sentences containing
containing aa

couplecouple ofof expressions
expressions[1] Takashi Inui, Creating an Annotated Corpus for the Analysis

of Causal Relations
6

Previous Work [NTCIR-6, 2007]
 Suffix-based

Statistical term
extraction
Education
Education「失業率 (unemploymentrates)」 「原油価格 (oil prices)」 「物価指数 (consumerprice index)」
AgingAging

 Co-occurrence

WelthyWelthynetwork ofstatistical terms


EconomyEconomyWedding
Wedding

Manually
constructed

Environment
Environment

7

Suffix-based Statistical Term
Extraction
1) Find a suffix of statistical terms

○

 ・・・

増え て おり 、 ビール の 出荷 台数 が ・・・ × ・・・ ある 場合 では テレビ の 生産 台数 競争 ・・・2) Scan leftward from the starting point to find the morpheme
which is neither noun nor specific particles
 ・・・

増え て おり 、 ビール の 出荷 台数 が ・・・StartEnd

3) Extract morphemes between starting point to ending point
as statistical terms
 ・・・

増え て おり 、 ビール の 出荷 台数 が ・・・
Statistical Term

8

Semantic Structure of StatisticalTerms
 Base Form

 Modifiers

Shortest sequence ofmorphemes having astatistically valid meaning ObjectWhat is measured

 Beer、PC Unemployment

 Subjectrates, Oil prices

Who measured Kirin、NEC Time Span

Various combinations:「unemployment「unemployment rates」
rates」「domestic「domestic unemploymentunemployment rates」
rates」「American「American unemploymentunemployment rates
rates
inin Mar.
Mar. 1998」
1998」

When was it measured

 1999、Feb.
 Region

Where was it measured

 Japan、America9

Example of Statistical Term
Network
 Too High Density

2 hops from “recycle ratio”

 Difficult to see therelationshipbetween statisticalterms

Combination
異なる粒度のCombination
異なる粒度のofmodifiersof統計量表現
modifiers
統計量表現

 Reasons Hub nodes price、volume ofproduction
 Combination ofmodifiers

HubHub nodes
nodes price、retail price10

Challenges
 Complexity of Network Structure High dense network Clustering generates one big cluster Threshold of co-occurrence does not work because mostco-occurrence of statistical terms are only 1 or 2.

 Complexity of Semantic Structure Appropriate combination of modifiers is not clear
 Can we integrate?

American unemployment ratio
Unemployment ratio

Male unemployment ratio

11

Unemployment ratio in June

Our Approach Simplify the Network Structure Limit the degree of nodes
 Link only top ω terms
 Main structure can be observed

 Simplify the Semantic Structure Integrate statistical terms which have commonbase form and co-occurring terms
内閣支持率

内閣支持率

国内の失業率
経済成長率

失業率

経済成長率

米国の失業率
犯罪率

犯罪率

12

Experimental Settings Suffix dictionary
 86 statistical terms tagged in MuST corpus

 Mainichi News 1998-1999 (Japanese) Extracted terms：8,600 words
 Degree parameter ω Investigated Maximum cluster size with ω Visualization Tool prefuse (http://prefuse.org/)

13Degree Limitation Parameter ωDegree distribution of statistical terms follows power law
Smaller Limitation Parameter ω divides the original network into
small pieces
We chose ω = 10 for the visualization.
4500
Maximum Cluster Size (nodes)

10000# of Nodes

1000
100
101

4000
3500
3000
2500
2000
1500
1000
500
0110100
Degree

1000

10000

0102030405060

Degree limitation ω (nodes)
14

Result of Semantic Structure
Simplification
Original Expression

Simplified Expression

わが国の温室効果ガスの総排出量
Domestic total greenhouse gasemission
96年の温室効果ガスの総排出量
Total greenhouse gas emission in 1996
12月のパソコン販売台数
PC sales volume in December

パソコン販売台数
PC sales volume

秋葉原の電気街のパソコン販売台数
PC sales volume in Akihabara
埼玉県所沢市の野菜の価格
Vegetable prices in Tokorozawa city
Saitama
すべての野菜の価格
All vegetable prices

温室効果ガスの総排出量
Total greenhouse gas emission

野菜の価格
Vegetable Prices
15

Result of Network Structure
Simplification
Easier to observe
relationships
between terms

2 hops from “Recycle Ratio”

RecycleRecycle Ratio
Ratio

Cliques consist ofstatistical terms related
to a specific topic

16

7 hops from “Recycle Ratio”

RecycleRecycle Ratio
Ratio

17

RecycleRecycle Ratio
Ratio↓↓
EnergyEnergy
Consumption
Consumption↓↓
AmountAmount ofof waste
waste↓↓DioxinDioxin↓↓Incidence
Incidence ofoftesticular
testicular cancer
cancerRecycleRecycle Ratio
Ratio↓↓
Unemployment
Unemployment
Rates
Rates
↓↓
Economic
Economic
GrowthGrowth↓↓Interest
Interest rates
rates
↓
↓
Condominium
Condominium
PricesPricesRecycleRecycle Ratio
Ratio

↓
↓
Resource
Resource
RecoveryRecovery↓↓
Sales
Sales ofof Beer
Beer↓
↓
Sales
Sales ofof PC
PC↓↓
MarketMarket Share
Share

RecycleRecycle Ratio
Ratio

18

Example: Energy Consumption

COCO22 emission
emission
EnergyEnergy consumption
consumption
RecycleRecycle ratio
ratio

CarbonCarbon sinksink
19

AmountAmount ofof waste
waste

Example: Amount of Waste
Price
Price ofof used
used
paper
paper
AmountAmount ofoffactorywastefactory waste

RecycleRecycle ratio
ratio

AmountAmount ofof waste
waste

AmountAmount ofof dioxin
dioxin20

Example: Amount of Dioxin
AmountAmount ofof waste
waste

AmountAmount ofof dioxin
dioxinIncidence
Incidence ofof allergy
allergy

Acceptable
Acceptable intake
intake21

Examples: Unemployment Rates
JobJob openings
openings

Unemployment
Unemployment RatesRates
Consumer
Consumer spending
spending
Labor
Labor force
force population
population

22

Examples: GDP GrowthLandLand Prices
Prices andandStock
Stock Prices
PricesHouse
House PricesPricesGDP
GDP Growth
GrowthUnemployment
Unemployment Rates
Rates

Consumer
Consumer Spending
Spending
Share
Share ofof thethe votes
votes forfor
LiberalLiberal Democratic
Democratic Party
Party
23

Example: Meal Size
BMI
BMI

Cardiac
Cardiac rate
rate

Exercise
Exercise volume
volumeBone
Bone massmass

Meal
Meal Size
Size
Caloric
Caloric intake
intakeBody
Body fatfat
percentage
percentage
24

Conclusion Visualization for Statistical Term Network
 Simplification of Network Structure Degree Limit Parameterω Simplification of Semantic Structure Integrate common base form and co-occurring terms

 Experiment: Visualize a network consists of

8,600 statistical network
 Future Work Exploit syntactic patterns about causal relation

expression and make a direction on thestatistical term network
 Exploit Web corpus

25