  In the last five years there has been a flurry of work on information
extraction from clinical documents, i.e., on algorithms capable of extracting,
from the informal and unstructured texts that are generated during everyday
clinical practice, mentions of concepts relevant to such practice. Most of this
literature is about methods based on supervised learning, i.e., methods for
training an information extraction system from manually annotated examples.
While a lot of work has been devoted to devising learning methods that generate
more and more accurate information extractors, no work has been devoted to
investigating the effect of the quality of training data on the learning
process. Low quality in training data often derives from the fact that the
person who has annotated the data is different from the one against whose
judgment the automatically annotated data must be evaluated. In this paper we
test the impact of such data quality issues on the accuracy of information
extraction systems as applied to the clinical domain. We do this by comparing
the accuracy deriving from training data annotated by the authoritative coder
(i.e., the one who has also annotated the test data, and by whose judgment we
must abide), with the accuracy deriving from training data annotated by a
different coder. The results indicate that, although the disagreement between
the two coders (as measured on the training set) is substantial, the difference
is (surprisingly enough) not always statistically significant.
