  One essential task in information extraction from the medical corpus is drug
name recognition. Compared with text sources come from other domains, the
medical text is special and has unique characteristics. In addition, the
medical text mining poses more challenges, e.g., more unstructured text, the
fast growing of new terms addition, a wide range of name variation for the same
drug. The mining is even more challenging due to the lack of labeled dataset
sources and external knowledge, as well as multiple token representations for a
single drug name that is more common in the real application setting. Although
many approaches have been proposed to overwhelm the task, some problems
remained with poor F-score performance (less than 0.75). This paper presents a
new treatment in data representation techniques to overcome some of those
challenges. We propose three data representation techniques based on the
characteristics of word distribution and word similarities as a result of word
embedding training. The first technique is evaluated with the standard NN
model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two
deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked
Denoising Encoders). The third technique represents the sentence as a sequence
that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term
Memory). In extracting the drug name entities, the third technique gives the
best F-score performance compared to the state of the art, with its average
F-score being 0.8645.
