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Data Geeks Paris - Cherchez la Femme

Elian CARSENAT
Elian CARSENAT
Elian CARSENATFounder NamSor.com à NamSor™ Applied Onomastics

Presents a new API to extract gender from personal names, combining traditional dictionary approach and a more advanced sociolinguistic approach.

Data Geeks Paris - Cherchez la Femme

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WHAT’S IN A NAME?
- IDENTITY & CULTURE
- GENDER (LE SEXE)
NamSor Applied Onomastics
1
2014-06-26
Où sont les Femmes?
2
Male
86%
Female
14%
Paris DataGeek
Gender Gap
Où sont les Femmes? ex. le CINOCHE
Mining 5M names to assess GENDER*
3
IMDB File THE CINEMATOGRAPHERS LIST
Name Origin Male Female Unknown
France 82% 16% 2%
Tunisia 77% 16% 8%
Morocco 80% 15% 5%
Algeria 86% 11% 3%
Ireland 89% 10% 1% *Using NamSor GendRE API v0.0.13
What’s in a name? What’s a name?
4
 Elena Rossini
 @_Elena (Twitter)
 Elian Carsenat
 @ElianCarsenat (Twitter)
 elian.carsenat@namsor.com
 elian.carsenat@sfr.fr
 tioulpanov (Skype)
 NamSor.com
+ Social Network (LinkedIn, Twitter, FB …) : more names
 Onomastics = the science of proper names
NamSor socio linguistics algorithm
5
FN LN
MetteAndersen
LeneAndersson
EvaArndt-Riise
HeidiAstrup
MieAugustesen
MargotBærentzen
LouiseBager Nørgaard
MarieBagger Rasmussen
YuttaBarding
UllaBarding-Poulsen
FN LN
XianDongmei
ZhengDongmei
JinDongxiang
XuDongxiang
LiDongxiao
QinDongya
LiDongying
HanDuan
LiDuihong
JiangFan
Training set : Athletes
Step 1 – Learn stereotypes
bitao gong
biwang jiang
birgitta agerberth
birgitte l. eriksen
bitao gong
bitten thorengaard
biwang Jiang
birgitta agerberth
birgitte l. eriksen
bitten thorengaard
Data set : Actors
Step 2 – Classify
Decrypting IDENTITY
6
Source: Commonwealth WWI Casualties

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Data Geeks Paris - Cherchez la Femme

  • 1. WHAT’S IN A NAME? - IDENTITY & CULTURE - GENDER (LE SEXE) NamSor Applied Onomastics 1 2014-06-26
  • 2. Où sont les Femmes? 2 Male 86% Female 14% Paris DataGeek Gender Gap
  • 3. Où sont les Femmes? ex. le CINOCHE Mining 5M names to assess GENDER* 3 IMDB File THE CINEMATOGRAPHERS LIST Name Origin Male Female Unknown France 82% 16% 2% Tunisia 77% 16% 8% Morocco 80% 15% 5% Algeria 86% 11% 3% Ireland 89% 10% 1% *Using NamSor GendRE API v0.0.13
  • 4. What’s in a name? What’s a name? 4  Elena Rossini  @_Elena (Twitter)  Elian Carsenat  @ElianCarsenat (Twitter)  elian.carsenat@namsor.com  elian.carsenat@sfr.fr  tioulpanov (Skype)  NamSor.com + Social Network (LinkedIn, Twitter, FB …) : more names  Onomastics = the science of proper names
  • 5. NamSor socio linguistics algorithm 5 FN LN MetteAndersen LeneAndersson EvaArndt-Riise HeidiAstrup MieAugustesen MargotBærentzen LouiseBager Nørgaard MarieBagger Rasmussen YuttaBarding UllaBarding-Poulsen FN LN XianDongmei ZhengDongmei JinDongxiang XuDongxiang LiDongxiao QinDongya LiDongying HanDuan LiDuihong JiangFan Training set : Athletes Step 1 – Learn stereotypes bitao gong biwang jiang birgitta agerberth birgitte l. eriksen bitao gong bitten thorengaard biwang Jiang birgitta agerberth birgitte l. eriksen bitten thorengaard Data set : Actors Step 2 – Classify
  • 7. Mining 3M Geo-Tweets to map FLOWS 7 Source Target Type Id Onoma Weight United Kingdom France Directed 16 Great Britain 37 Spain France Directed 55 Spain 14 United States France Directed 75 Great Britain 12 Turkey France Directed 79 Turkey 11 Brazil France Directed 87 Portugal 10 United Kingdom France Directed 112 Ireland 9 Italy France Directed 152 Italy 7 Switzerland France Directed 226 France 5 Belgium France Directed 247 France 5 United Kingdom France Directed 258 France 5 Mexico France Directed 287 Spain 4 Ireland France Directed 317 Great Britain 4 United Kingdom France Directed 333 Italy 4 United States France Directed 375 France 4 Source: Twitter
  • 8. Isn’t predicting gender SIMPLE? 8  Can you tell:  Andrea/Rossini vs. Andrea/Parker  O./Sokolova  Kjell/Bergqvist  声涛/周  ‫בנימין‬/‫נתניהו‬  ‫معين‬/‫المرعبي‬  Our target, globally for all countries/lang./cultures:  99% precision, 99% recall for both Male & Female
  • 9. We’re getting there, combining classic baby name statistics with our unique algorithm 9 100% of objectives reached for 10 countries 75% of objectives reached for 28 countries Currently, each version brings ~30% improvement!
  • 10. Want to play? 10 Android Gadget RapidMiner Extension
  • 11. 11  Improve your targeting, increase your open and click rates by saying "Hello Sir", "Hello Madam" without mistakes in your emailing
  • 12. Conclusions 12  We recognize names in any language, any place, any database; we can classify and we can sort  Onomastic class is no ‘hard fact’ like a place of birth, a nationality, etc. but it’s accurate and fine-grain  Our sociolinguistic approach surpasses the traditional geo- demographics or ‘dictionary’ approach used in the US/UK  Our unique capability to decrypt identity and gender in high growth / emerging countries (Russia, Africa, India, Indonesia…) can be put to work in a wide range of applications
  • 13. Elian Carsenat  http://fdimagnet.com/  http://namsor.com/ 13 Juillet 2013, Ambassade de Lituanie à Paris  elian.carsenat@namsor.com  +33 6 52 77 99 07  Twitter @NamsSor_com
  • 15. NamSor sorts names : functions, use cases 15 2.Name Transliteration & Matching 3.Named Entity Extraction, Parsing 1.Name Ling. Classification Multilingual Text Mining Control Watch Lists Social Networks Analytics Geo demographics Migration Studies Gender Studies