  Nowadays, editors tend to separate different subtopics of a long Wiki-pedia
article into multiple sub-articles. This separation seeks to improve human
readability. However, it also has a deleterious effect on many Wikipedia-based
tasks that rely on the article-as-concept assumption, which requires each
entity (or concept) to be described solely by one article. This underlying
assumption significantly simplifies knowledge representation and extraction,
and it is vital to many existing technologies such as automated knowledge base
construction, cross-lingual knowledge alignment, semantic search and data
lineage of Wikipedia entities. In this paper we provide an approach to match
the scattered sub-articles back to their corresponding main-articles, with the
intent of facilitating automated Wikipedia curation and processing. The
proposed model adopts a hierarchical learning structure that combines multiple
variants of neural document pair encoders with a comprehensive set of explicit
features. A large crowdsourced dataset is created to support the evaluation and
feature extraction for the task. Based on the large dataset, the proposed model
achieves promising results of cross-validation and significantly outperforms
previous approaches. Large-scale serving on the entire English Wikipedia also
proves the practicability and scalability of the proposed model by effectively
extracting a vast collection of newly paired main and sub-articles.
