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kind of terms of units are discovered (e.g. a tempera-
ture unit, a relative humidity unit, a permeability unit)
and allow an automatic integration of variant or new
units directly in the OTR.
7 CONCLUSIONS AND FUTURE
WORK
The paper presents our overall two-step approach in
order to identify variant terms of units or new units in
unstructured scientific documents. We have success-
fully demonstrated how supervised learning methods
can help reduce the search space of terms of units
drowned in textual information. We have achieved
almost 86% of reducing. For this purpose, we have
used many algorithms and weight-based measures to
prove the relevancy of our approach. We have also
presented our first results to detect and extract vari-
ant terms and new units, using a new string distance
measure adapted to our unit identification issue. As
presented in the paper, this second step needs to be
deepened. Our approach, presented in this paper, ad-
dresses the entire issue: location, extraction and iden-
tification of units. Further work on semantical identi-
fication of variant terms and new units should be ad-
dressed in order to automatically populate the OTR.
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