  Subject categories of scholarly papers generally refer to the knowledge
domain(s) to which the papers belong, examples being computer science or
physics. Subject category information can be used for building faceted search
for digital library search engines. This can significantly assist users in
narrowing down their search space of relevant documents. Unfortunately, many
academic papers do not have such information as part of their metadata.
Existing methods for solving this task usually focus on unsupervised learning
that often relies on citation networks. However, a complete list of papers
citing the current paper may not be readily available. In particular, new
papers that have few or no citations cannot be classified using such methods.
Here, we propose a deep attentive neural network (DANN) that classifies
scholarly papers using only their abstracts. The network is trained using 9
million abstracts from Web of Science (WoS). We also use the WoS schema that
covers 104 subject categories. The proposed network consists of two
bi-directional recurrent neural networks followed by an attention layer. We
compare our model against baselines by varying the architecture and text
representation. Our best model achieves micro-F1 measure of 0.76 with F1 of
individual subject categories ranging from 0.50-0.95. The results showed the
importance of retraining word embedding models to maximize the vocabulary
overlap and the effectiveness of the attention mechanism. The combination of
word vectors with TFIDF outperforms character and sentence level embedding
models. We discuss imbalanced samples and overlapping categories and suggest
possible strategies for mitigation. We also determine the subject category
distribution in CiteSeerX by classifying a random sample of one million
academic papers.
