  Automatic text classification (TC) research can be used for real-world
problems such as the classification of in-patient discharge summaries and
medical text reports, which is beneficial to make medical documents more
understandable to doctors. However, in electronic medical records (EMR), the
texts containing sentences are shorter than that in general domain, which leads
to the lack of semantic features and the ambiguity of semantic. To tackle this
challenge, we propose to add word-cluster embedding to deep neural network for
improving short text classification. Concretely, we first use hierarchical
agglomerative clustering to cluster the word vectors in the semantic space.
Then we calculate the cluster center vector which represents the implicit topic
information of words in the cluster. Finally, we expand word vector with
cluster center vector, and implement classifiers using CNN and LSTM
respectively. To evaluate the performance of our proposed method, we conduct
experiments on public data sets TREC and the medical short sentences data sets
which is constructed and released by us. The experimental results demonstrate
that our proposed method outperforms state-of-the-art baselines in short
sentence classification on both medical domain and general domain.
