Junzhao Bu

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Time is an important aspect of information and is very useful for information utilization. The goal of this study was to analyze the challenges of temporal expression (TE) extraction and normalization in Chinese clinical notes by assessing the performance of a rule-based system developed by us on a manually annotated corpus (including 1,778 clinical notes(More)
In this paper, a machine learning-based system was proposed for the challenge task of chemical entity mention recognition in patents (CEMP) in BioCreative V. The CEMP task was recognized as a sequence labeling problem and conditional random fields (CRF) were employed for it. Evaluation on the CEMP challenge corpus showed that our system (team 293) achieved(More)
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