  We analyze the RI-TIMEXes in temporally annotated corpora and propose two
hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative
domain: the anchor point hypothesis and the anchor relation hypothesis. We
annotate the RI-TIMEXes in three corpora to study the characteristics of
RI-TMEXes in different domains. This informed the design of our RI-TIMEX
normalization system for the clinical domain, which consists of an anchor point
classifier, an anchor relation classifier and a rule-based RI-TIMEX text span
parser. We experiment with different feature sets and perform error analysis
for each system component. The annotation confirmed the hypotheses that we can
simplify the RI-TIMEXes normalization task using two multi-label classifiers.
Our system achieves anchor point classification, anchor relation classification
and rule-based parsing accuracy of 74.68%, 87.71% and 57.2% (82.09% under
relaxed matching criteria) respectively on the held-out test set of the 2012
i2b2 temporal relation challenge. Experiments with feature sets reveals some
interesting findings such as the verbal tense feature does not inform the
anchor relation classification in clinical narratives as much as the tokens
near the RI-TIMEX. Error analysis shows that underrepresented anchor point and
anchor relation classes are difficult to detect. We formulate the RI-TIMEX
normalization problem as a pair of multi-label classification problems.
Considering only the RI-TIMEX extraction and normalization, the system achieves
statistically significant improvement over the RI-TIMEX results of the best
systems in the 2012 i2b2 challenge.
