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NetGender for
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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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Name
Validation -
Name-quality flag to easily identify incomplete or
incorrect names. |
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Name Standardization - Prefixes and suffixes are abbreviated and standardized to your specifications. |
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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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Gender Determination
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100,000+, multi-ethnic, name dictionary for pinpoint accuracy. |
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gender dictionary was compiled from several accurate, frequency-based models
including sources such as DMV and US census. |
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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.
Combining NetAddress with our NetGender
product will give you a powerful one-two punch to tackle even the most daunting
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 of what's
been entered in the field. Using the NetAddress/NetGender combination, you'll
always be confident of the type of 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 unparalleled results.
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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.
This, plus a C# sample, 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
specifically created with a rich ethnic diversity. |
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Identify the gender makeup of your list for target
marketing
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Nicknames property - invaluable
for
finding duplicate names
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User updatable tables so your applications never
go out-of-date
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Set proper salutations for a more personalized
list
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Free upgrades for a full year
(License
&
Maintenance agreements)
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Prefixes and suffixes are
standardized to your specifications
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Proper case conversion for
more attractive data presentation
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Parse full names into 5 separate components
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Designed for use with C#,
VB.NET and other .NET compatible
programming languages
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More Data
Quality
Tools for .NET |
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NetZipCode
for .NET
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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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Spelling
Correction
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Field Length Control |
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