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Freedom of
Choice:
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| An increasingly important aspect for Information Technology solutions is the
ability for those solutions to be consumed by anyone, any where and on any
platform. Extractor is a patented, Automatic content keyphrase tagging
technology researched and developed to work on
any computing platform. |
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¤ Linux,
¤ Mac O/S
¤ Windows
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| Source code licenses
available for custom compiling of the most
generic C code to meet any project and develery
requirement. |
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| In true cross-platform
consistency, the Extractor Software Development
Kit (SDK) includes supporting API's for these
development languages: |
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¤ C (C,
C++, VC++. C#)
¤ Java
¤ Perl
¤ Python
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Visual Basic
¤
Web Service
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| In addition to the cross
platform flexibility, Extractor's internal
features are fully exposed to the developer for
customizable implementations: |
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| ¤ Generate summaries
automatically |
| ¤ Native file formats support:
Text, HTML, and Email |
| ¤ HTML Tag filtering |
| ¤ Text, HTML and E-mail filters |
| ¤ Document highlighting and
Sentence marking |
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Multi-lingual: English, French, German,
Japanese, Korean & Spanish |
| ¤ Multi-Threaded |
| ¤ Define summary results - set the
number of desired output phrases |
| ¤ Stop Word - list any number of
words for Extractor to ignore |
| ¤ Go Word, Go Phrase - lists of
words/phrases for targeted focus |
| ¤ Frequency Ranking - by ascending
or descending order |
| ¤ Multi-document processing |
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| In terms of computer
automated text summarization there are many
definitions and implementations including
Bayesian, Heurstic or linguistic. Extractor
uses a Genetic approach which in itself provides
an automatic learning process. This is a
critical element for the summarization utility
to be able to move from one subject domain to
another without re-training, as well, human
intervention. Compared to other approaches
which are domain specific and anchored by their
static algorithm, thereby requiring greater
human intervention just to be able to move from
one subject domain to another. For a more
detailed discussion please see "Learning
Algorithms for Keyphrase Extraction" |
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