\section{Conclusions and Future Works}
\label{con}
In this paper, we propose a WnD classifier to map both patents and papers to CPC subclasses. The model captures both the string and semantic relatedness between labels and texts. We achieve comparative performance to the state-of-the-art models for both paper-to-CPC and patent-to-CPC tasks. We hope to contribute an intuitive, simple yet practically effective baseline for categorizing scientific publications. 

Although CPC subclass already has over 600 labels, it is still a relative coarse granularity in the taxonomy. The finest level (subgroup) consists of over 200 thousand labels and provides much more detailed classification information. At the same time, with the explosion of labels, the task is much more challenging. In the future,  we will  go deeper into the taxonomy and try to explore the hierarchical relations between labels and improve the scalability of models for finer grained label sets. 