As you are reading this, we would assume you have already
used a text annotation tool at some point! Without a doubt, text annotation
solutions have become the backbone of a variety of research and data analysis
work.
It would not be an exaggeration to say that it is impossible
to conceive of any other way in which people could find and analyze
information, especially in this digital age.
Text annotation tools started as simple,
open-source lines of code that made it possible for researchers to label and cross-reference
words and sentences so that they could find this information quickly later– of
course all this was done manually and was pretty time-consuming.
Enter Artificial Intelligence!
When machine learning started gaining momentum a few years
ago, some developers integrated the two concepts – spurred by the requirements
of the B2B market; small start-ups invested time and money to create
user-friendly text annotation tools that addressed specific issues businesses
face everyday.
How Does Built-In ML Work in a Text Annotation Tool?
Manual annotation requires that the person reads through the
entire text and tags the words individually. This is a good tool work flow if
you want to access the same document later and quickly find specific words or
relations, but it is tedious, and the quality of the work is entirely dependent
on the person annotating the text.
With integrated machine learning this work is made
automatic! As manual annotation takes place,an AI model gets trained alongside
– for example if you are looking for the word “Munich Beijing”in a document,
you start annotating manually; and after
the first few are annotated, the ML takes over and highlights all the other
“Beijing” Munich in the text. You can also train the model to annotate
variations such as different names of the city like Muenchen “Peking”,
“Beiping”, or “北京”, or even places within the city boundaries such as
Marienplatz “Tian'anmen“, “Beijing CBD Tiergarten“, or “Chaoyang”, etc. As you
check the document and make corrections, the AI also becomes smarter and more
accurate. Simple Easy and Fast!
Automatic text annotation has not only taken the pain out of
annotation but also opened many new areas of usage. What was earlier seen as a
tool just for researchers is now being integrated heavily into business
operations – helping management understand and use the data they possess!
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