HiGitClass: KeywordDriven Hierarchical Classification of GitHub Repositories GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topicbased search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topicbased analysis. This work targets the automatic repository classification problem as keyworddriven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the usersu0027 needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multimodal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weaklysupervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification. Code and datasets related to this paper are available at https:github.comyuzhimanhuaHiGitClass.

HierCon: Hierarchical Organization of Technical Documents Based on Concepts In this work we study the hierarchical organization of technical documents, where given a set of documents and a hierarchy of categories, the goal is to assign documents to their corresponding categories. Unlike prior work on supervised hierarchical document categorization that relies on large amount of labeled training data, which is expensive to obtain in closed technical domain and tends to stale as new knowledge emerges, we study this problem in a weak supervision setting, by leveraging semantic information from concepts. The core idea is to project both documents and categories into a common concept embedding space, where their finegrained similarity can be easily and effectively computed. Experiments over realworld datasets from the subject of computer science, physics u0026 mathematics, and medicine demonstrated the superior performance of our approach over a wide range of state of the art baseline approaches.