  Electronic Health Records (EHRs) in hospital information systems contain
patients' diagnosis and treatments, so EHRs are essential to clinical data
mining. Of all the tasks in the mining process, Chinese Word Segmentation (CWS)
is a fundamental and important one, and most state-of-the-art methods greatly
rely on large-scale of manually-annotated data. Since annotation is
time-consuming and expensive, efforts have been devoted to techniques, such as
active learning, to locate the most informative samples for modeling. In this
paper, we follow the trend and present an active learning method for CWS in
EHRs. Specically, a new sampling strategy combining Normalized Entropy with
Loss Prediction (NE-LP) is proposed to select the most representative data.
Meanwhile, to minimize the computational cost of learning, we propose a joint
model including a word segmenter and a loss prediction model. Furthermore, to
capture interactions between adjacent characters, bigram features are also
applied in the joint model. To illustrate the effectiveness of NE-LP, we
conducted experiments on EHRs collected from the Shuguang Hospital Affiliated
to Shanghai University of Traditional Chinese Medicine. The results demonstrate
that NE-LP consistently outperforms conventional uncertainty-based sampling
strategies for active learning in CWS.

Computation and Language