Abstract
Temporal ordering is important in deducing time sequence of medical events in biomedical text and has significant application in summarization, narrative generation, and information extraction tasks. We attempt temporal ordering of events in medical case reports. Our approach is deterministic in extracting explicit temporal expressions and probabilistic in extracting implicit boundaries. We introduce event context as a set of features to learn a CRF model. We achieve F1 score of 0.78 in detecting boundaries. We apply rule-based normalization and ordering of identified temporal expressions.
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Sarkar, R., Nayal, B., Joshi, A. (2019). An Approach for Temporal Ordering of Medical Case Reports. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-1274-8_10
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DOI: https://doi.org/10.1007/978-981-13-1274-8_10
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