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‘Microtext’, so that researchers can have a common
ground for future discussion.
Some of the scattered research surveyed in this
paper has provided interesting insights as to the type
of conclusions and methodologies that would be
discovered and catalogued with a more focused
effort. Some of these include: Leveraging an outside
body of knowledge, leveraging non-traditional
language features such as laughs and “uh/ums”, and
treating individual results as less important and
focusing more on less granular trends. Overall, trend
analysis and identification has the most research,
and Information Extraction from microtext is
particularly lacking.
In two different papers, SVMs were a successful
strategy in dealing with informal grammars.
The next step is investigating and more
rigorously quantifying the three attributes in the
microtext definition. This would certainly provide
reusable insights and help catalogue best performing
techniques and unique quirks and advantages of
microtext processing versus text processing. The
goal of this paper is to create an understanding of
microtext within the AI and NLP communities.
ACKNOWLEDGEMENTS
Thanks to the Office of Naval Research and the
Space and Naval Warfare Systems Center Pacific for
their financial support, and Dr. LorRaine Duffy for
inspiration and motivation. This paper is the work of
U.S. Government employees performed in the
course of employment and no copyright subsists
therein.
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ALL ABOUT MICROTEXT - A Working Definition and a Survey of Current Microtext Research within Artificial
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