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NetGender for
.NET Component |
Managed Code |
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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. |
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Gender Determination
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100,000+, multi-ethnic, Name Dictionary for pinpoint accuracy. |
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Name Parsing - Separate full names into five individual components;
split-apart dual names in the same field. |
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Name Standardization - Prefixes and suffixes are abbreviated and standardized to your specifications. |
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Name
Verification -
Name-quality flag to easily identify incomplete or
incorrect names.
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x64 Version Available |
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A Closer Look |
How NetGender Works |
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NetGender for .NET
accepts free-form names and then automatically
splits them into standard components: Prefix, First, Middle, Last and Suffix no
matter what the original format. Next, using a 100,000 Name Dictionary in
combination with an 8,000 word proprietary Company Dictionary, the gender is
determined with unmatched accuracy. For special cases, such as an all-girl
school, you can easily override the built-in system dictionaries.
NetGender can process all styles of names
including inverse, natural order, hyphenated and multi-part last names. Multiple
names in the same field and companies can be easily separated providing you with
powerful formatting control.
As a bonus, when you combine
NetGender with
our NetAddress product, you can
even tackle
the impossible task of identifying data that has been entered free-form. The
names, addresses and C/S/Z either “float” from field to field or you are just
not sure what's in the field. First,
examine the Address_Quality flag returned from
NetAddress. If
set to High or Medium, it's almost certainly an address. If set to Low,
submit the string to NetGender for name verification. Using these
techniques you’ll always be certain of the data you're working with.
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NetGender for .NET starts by carefully identifying each
individual name element based on the Name_Style you’ve selected. Intuitive
algorithms examine the results and a selection is made of the most complete and
correct data. Next, the name elements are split into their standard
components and the
user-specified prefix/suffix abbreviations are applied. Using a 100,000+ Name Dictionary in conjunction with the
pre-programmed rules from the 8,000+ Company Dictionary the correct gender is
determined with pinpoint accuracy. The Name_Quality flag is then set to indicate how
complete and correct the name is. Finally, the standardized name components are
returned to your application along with a complete and cleansed composite name.
Not all gender identification systems are created the same. Most use a simple
table of about 8,000 to 18,000 first names. NetGender uses an extensive 100,000+
Name Dictionary that was specifically created with a rich ethnic diversity.
NetGender then applies the pre-programmed rules from an 8,000+ Company
Name
Dictionary to yield the absolute highest level of gender accuracy.
This unique and powerful strategy produces results unmatched by any other
software.
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Name_In |
MR DE LA ROSA, JUAN R OR
MARIA |
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Name_Out |
Male |
Mr Juan R De La Rosa |
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Name2_Out |
Female |
Maria De La Rosa |
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Name_In |
SMITH-LA BELLA JR, JOHN A &
MARY B, PHD |
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Name_Out |
Male |
John A Smith-La Bella Jr |
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Name2_Out |
Female |
Mary B Smith-La Bella PhD |
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Name_In |
MS MARY MYTH C/O THE SOFTWARE COMPANY |
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Name_Out |
Female |
Ms Mary Myth |
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Name2_Out |
Company |
The
Software Company |
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Name_In |
O’LEARY-VAN HORN, DALE &
MIKE |
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Name_Out |
Neutral |
Dale O’Leary-Van Horn |
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Name2_Out |
Male |
Mike O’Leary-Van Horn |
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Name_In |
MR J JONES & WIFE MAY |
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Name_Out |
Male |
Mr J Jones |
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Name2_Out |
Female |
May Jones |
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The screen shot above was taken from a sample application created using VB.NET. Other samples, including
C#, can be found in the NetGender folder. |
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NetGender's
precise results are driven by a specially encoded, 100,000-name
dictionary that was specially created with a rich ethnic diversity. |
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Identify Gender Makeup of Your List for Target
Marketing |
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Prefixes and Suffixes Are
Standardized to Your Specifications |
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Nicknames Property - Invaluable
for
Finding Duplicate Names
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Proper Case Conversion for More Attractive
Data Presentation |
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User Updatable Tables So Your Applications Never Go Out-of-Date |
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Royalty-Free Runtime
(License
Agreement) |
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Set Proper Salutations for a More Personalized
List |
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Parse Full Names Into 5 Separate Components |
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FREE Upgrades for a Full Year (Maintenance
Agreement) |
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Designed for Use with All .NET Compatible
Programming Languages |
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More Data
Quality
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NetZipCode
for .NET
●
Address Verification
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Address Correction
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Address Parsing
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Unlimited Processing Volume |
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NetAddress
for .NET
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Address Validation
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Address Standardization
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Address Parsing
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No Recurring Charges |
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NetCase
for .NET
● Proper Case
Conversion
● Data Translation
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Common Misspellings
Correction
● User Expandable
Tables |
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