Does anyone know of a good library for mapping a person's name to his or her sex?

Question

I am looking for a library or database that can provide guesses about whether a person is male or female based on his or her name or nickname. Something like

john => "M",
mary => "F",
alex => "A", #ambiguous

I am looking for something that supports names other than English names (such as Japanese, Indian, etc.).

Before I get another answer along the lines of "you are going to offend people by assuming their sex/gender" let me be clear, my application does not interact with anyone. It does not send emails or contact anyone in anyway. There are no users to ask. In many cases, the person in question is dead, and the only information I have is name, birth date, and date of death. The reason I want to know the sex of the individual is to make the grammar of the output nicer and to aid in possible searches that may come latter.

Answer 1

"I tell ya, life ain't easy for a boy named 'Sue.'"

...So, why make it any harder? If you need to know the sex, just ask... Otherwise, don't worry about it.

Answer 2

Just ask people, and if they are nice they will give you their 'M's or 'F's , and if they are not then give'em an 'A' .

Answer 3

The gender of a name is something that cannot be inferred programmatically in the general case. You need a name database. Here is a free name database from the US Census Bureau.

Answer 4

I think it will be hard to find libraries or databases that covers more than one (or a limited few) cultures. For instance, here in Sweden the name "Maria" is strongly identified as a female name, while (to the best of my understanding) that connection is not at all as strong in spanish-speaking countries.

Answer 5

Dont bother attempting to determine a persons sex from an entered name. When you get it wrong your going to really piss someone off, letting users enter their sex is simple so you may as well let them enter it.

Answer 6

Name-gender maps can work but in multicultural countries it's more like guessing. I can give you one example: Marian in Polish is a typical masculine name, whereas the same name in Great Britain is a female name. In the era of people immigrating all over the world, I'm not sure such database would be very accurate. Good luck!

Answer 7

gender.c is an open source C program that does a good job. It comes with data for 44568 first names from all around the world. There is good documentation and a description of the file format (basically plain text) so it should not be to difficult to read it from your own application.

Here is what the author says:

A few words on quality of data

The dictionary of first names has been prepared with utmost care. For example, the Turkish, Indian and Korean names in this dictionary have all been independently classified by several native speakers. I also took special care to list only those names which can currently be found.

The lesson from this?

Any modifications should be done very cautiously (and they must also adhere to the sorting required by the search algorithm). For example, knowing that "Sascha" is a boy's name in Germany, the author never assumed the English "Sasha" to be a girl's name. Knowing that "Jan" is a boy's name in Germany, I never assumed it to be also a English short form of "Janet". Another case in point is the name "Esra". This is a boy's name in Germany, but a girl's name in Turkey.

The program calculates a probability for the name being male of female. It can do so with the name as input alone or with the name and country of origin, which gives significantly better results.

You can download it from the website of the German computer magazine c't 40 000 Namen. The article is in German but don't worry, all documentation is English. Here is the direct ftp link 0717-182.zip if you are not interested in the article. The zip-File contains the source code, an windows executable, the database and the documentation.

Answer 8

The only thing you'll get from trying to automate it is a bunch of unhappy users. From that census data:

JAMES, JOHN, ROBERT, MICHAEL, WILLIAM, DAVID, RICHARD, CHARLES, JOSEPH, THOMAS, CHRISTOPHER, DANIEL, PAUL, MARK, DONALD, GEORGE, KENNETH, STEVEN, EDWARD, BRIAN, RONALD, ANTHONY, KEVIN, JASON, MATTHEW, GARY, TIMOTHY, JOSE, LARRY, JEFFREY, FRANK, SCOTT, ERIC, STEPHEN, ANDREW, RAYMOND, GREGORY, JOSHUA, JERRY, DENNIS, WALTER, PATRICK, PETER, HAROLD, HENRY, CARL, ARTHUR, RYAN, JOE, JUAN, JACK, ALBERT, JUSTIN, TERRY, GERALD, KEITH, SAMUEL, WILLIE, LAWRENCE, ROY, BRANDON, ADAM, FRED, BILLY, LOUIS, JEREMY, AARON, RANDY, EUGENE, CARLOS, RUSSELL, BOBBY, VICTOR, MARTIN, JESSE, SHAWN, CLARENCE, SEAN, CHRIS, JOHNNY, JIMMY, ANTONIO, TONY, LUIS, MIKE, DALE, CURTIS, NORMAN, ALLEN, GLENN, TRAVIS, LEE, MELVIN, KYLE, FRANCIS, JESUS, RAY, JOEL, EDDIE, TROY, ALEXANDER, MARIO, FRANCISCO, MICHEAL, OSCAR, JAY, ALEX, JON, RONNIE, TOMMY, LEON, LEO, WESLEY, DEAN, DAN, LEWIS, COREY, MAURICE, VERNON, ROBERTO, CLYDE, SHANE, SAM, LESTER, CHARLIE, TYLER, GENE, BRETT, ANGEL, LESLIE, CECIL, ANDRE, ELMER, GABRIEL, MITCHELL, ADRIAN, KARL, CORY, CLAUDE, JAMIE, JESSIE, CHRISTIAN, LONNIE, CODY, JULIO, KELLY, JIMMIE, JORDAN, JAIME, CASEY, JOHNNIE, SIDNEY, JULIAN, DARYL, VIRGIL, MARSHALL, PERRY, MARION, TRACY, RENE, FREDDIE, AUSTIN, JACKIE, JOEY, EVAN, DANA, DONNIE, SHANNON, ANGELO, SHAUN, LYNN, CAMERON, BLAKE, KERRY, JEAN, IRA, RUDY, BENNIE, ROBIN, LOREN, NOEL, DEVIN, KIM, GUADALUPE, CARROLL, SAMMY, MARTY, TAYLOR, ELLIS, DALLAS, LAURENCE, DREW, JODY, FRANKIE, PAT, MERLE, TERRELL, DARNELL, TOMMIE, TOBY, VAN, COURTNEY, JAN, CARY, SANTOS, AUBREY, MORGAN, LOUIE, STACY, MICAH, BILLIE, LOGAN, DEMETRIUS, ROBBIE, KENDALL, ROYCE, MICKEY, DEVON, ASHLEY, CAREY, SON, MARLIN, ALI, SAMMIE, MICHEL, RORY, KRIS, AVERY, ALEXIS, GERRY, STACEY, CARMEN, SHELBY, RICKIE, BOBBIE, OLLIE, DENNY, DION, ODELL, MARY, COLBY, HOLLIS, KIRBY, CRUZ, MERRILL, LANE, CLEO, BLAIR, NUMBERS, CLAIR, BERNIE, JOAN, DOMINIQUE, TRISTAN, JAME, GALE, LAVERNE, ALVA, STEVIE, ERIN, AUGUSTINE, YOUNG, JOHNIE, ARIEL, DUSTY, LINDSEY, TRACEY, SCOTTIE, SANDY, SYDNEY, GAIL, DORIAN, LAVERN, REFUGIO, IVORY, ANDREA, SANG, DEON, CAROL, YONG, BERRY, TRINIDAD, SHIRLEY, MARIA, CHANG, ROSARIO, DANNIE, FRANCES, THANH, CONNIE, TORY, LUPE, DEE, SUNG, CHI, QUINN, MINH, THEO, LOU, CHUNG, VALENTINE, JAMEY, WHITNEY, SOL, CHONG, PARIS, OTHA, LACY, DONG, ANTONIA, KELLEY, CARROL, SHAYNE, VAL, JUDE, BRITT, HONG, LEIGH, GAYLE, JAE, NICKY, LESLEY, MAN, KASEY, JEWELL, PATRICIA, LAUREN, ELISHA, MICHAL, LINDSAY, and JEWEL

are all names that work for both males and females. If a girl's name is Robert and everyone, including your software, keeps on calling her a man, she'd be rather pissed.

Answer 9

Given your stated constraints, your best option is to re-phrase whatever it is you're writing to be gender-neutral unless you know what gender they want to be called in each instance.

If writing in English, remember that singular “they” is grammatically fine as a gender-neutral third-person singular pronoun.

A good example is the title of this question. As is currently:

    … mapping a person's name to his or her sex?

That would be less awkward if written:

    … mapping a person's name to their sex?

Answer 10

This is not really a programming problem - it comes down to getting a probability table.

AFAIK there are no public databases in distilled forms. You could either build this from census data, or buy the data from someone.

For example, this is someone who sells the probability table for Canada.

Answer 11

Some cultures have unisex names - like mine. What do you do then? I think the answer is plain and simple - don't assume - you could cause offence. Just ask if its needed, otherwise gender neutrality.

Answer 12

It's also poor practice to assume that users must be male or female. There are a small but significant number of "intersex" people, most of whom are heartily sick of not having a box to tick..
bignose: interesting on the "singular they". I didn't realize it had such a long history.

Answer 13

Although databases are probably the most practical solution, if you want to have some fun maybe you could try writing a neural net (or using a neural net library) that takes in the name and outputs one of those 3 options (F,M,A).

You could train it using the datasets that exist in the databases suggested by other answers, as well as with any other data you have.

This solution would allow you to handle names not specifically categorised previously, and also handle different languages. You might want to pass the language (if you know it) as an input to the neural net as well.

I don't know that I can say neural nets (or any other machine learning) would do a good job of categorising though.

Answer 14

IMHO, it is a generally bad idea to determine sex from an individuals name. A lot of names are intersexual (good grief, is this even a word ?? :-), and also they may be one sex in one culture and another in another.

A few stupid examples, just a few that came to mind (from my part of the world, CE)

Vanja - female, in eastern countries from here, mostly male
Alex - intersex (short for Sandra, female, and Sandro, male)
Robin - in western cultures, can be both

In some parts of the world, a persons sex can be determined by looking at how the name ends. For example, Marija, Sandra, Ivana, Petra, Sara, Lucija, Ana - you can see that most of these female names end in "ja" or "ra". There are other examples as well.

Still, I think it's better just to ask the user for sex.

Answer 15

Well, not anymore. IBM patented that idea a while ago.

So if you're looking for any level of flexability (something other than a list of names), you'll either have to (gasp!) ask the user, or simply pay IBM for the rights :)

In any case, such autodetection is annoying for many people who have gender-ambiguous names, or even just mean parents. Let's not make this any harder for them.

Answer 16

It's culture/region dependent: take Andrea, for Italians is only masculine, for Sweden is a female name while Andreas is for men; Shawn is ambiguous in English. If a language has declination, like Latin or Russian, the final letters will change according to grammatical rules,

Another source of ambiguities is Family names identical to Personal names.

In my opinion it's impossibile to solve in general.

Answer 17

I agree that there are two issues with this question:

1. The assumption that better than 50% is good. I'd say ask your average female Chris and male Su whether or not they enjoy being mis-addressed 100% of the time.
2. That there are only two genders in the world. Sure, we've been taught that, but it really doesn't reflect reality.

Answer 18

It's not free, but this is a nice library that I have used before:

NetGender for .NET allows you to quickly and easily build Name Verification, Parsing and Gender Determination into your custom applications. Accurately verify whether a particular field contains a valid individual or company. NetGender uses a 100,000+, ethnically diverse, Name Dictionary in combination with an 8,000+ Company Name Dictionary to ensure precise gender determination.

http://www.softwarecompany.com/dotnet/netgender.htm

Answer 19

Here are two oddball approaches that may not even work, and likely wouldn't work en masse without violating the terms of a license:

1. Use the Facebook API (which I know virtually nothing about, it may not even be possible) to perform two searches: one for FB male users with that first name, and one for female. Use the two numbers to decide the probability of gender.

2. Much looser but more scalable, use the Google API and search for the name plus the gender-specific pronouns, and compare the numbers. For instance, there are 592,000,000 results for searching for "Richard his" (not as a phrase), but only 179,000,000 for "Richard her".

Answer 20

Got this from hacker news discussion about this

Answer 21

Interesting. Don't think anyone has tried to do something like this. And like it has been suggested in previous answers - it must be country specific too.

Answer 22

I haven't used it, but IBM has a Global Name Analytics library (for a price!) that seems pretty comprehensive.

Answer 23

It's interesting that you say you have birth date. That could help. I've seen databases of histories of name popularity.

In the film Splash (1984), it was funny that Darryl Hannah's character chooses the name "Madison" from a Madison Avenue street sign, because obviously "Madison" is not a girl's name.

24 years later, Madison is the 4th most popular name for girl babies!

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Name history from the gov't. (Check out Mary's sad decline through the last 100 years.)

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When I wrote to the White House as a child, Richard Nixon (or, perhaps a secretary) responded to me with some photos of the historic place, addressed to "Miss Rhett Anderson." "Miss Rhett?" It doesn't even make sense! Can we REALLY not tell the difference between Clark Gable's Rhett (with a mustache, in Gone With The Wind!) and Vivian Lee's Scarlett? I shall never forgive him, despite Neil Young's assurance that "even Richard Nixon has got soul."