  Given a few seed entities of a certain type (e.g., Software or Programming
Language), entity set expansion aims to discover an extensive set of entities
that share the same type as the seeds. Entity set expansion in software-related
domains such as StackOverflow can benefit many downstream tasks (e.g., software
knowledge graph construction) and facilitate better IT operations and service
management. Meanwhile, existing approaches are less concerned with two
problems: (1) How to deal with multiple types of seed entities simultaneously?
(2) How to leverage the power of pre-trained language models (PLMs)? Being
aware of these two problems, in this paper, we study the entity set
co-expansion task in StackOverflow, which extracts Library, OS, Application,
and Language entities from StackOverflow question-answer threads. During the
co-expansion process, we use PLMs to derive embeddings of candidate entities
for calculating similarities between entities. Experimental results show that
our proposed SECoExpan framework outperforms previous approaches significantly.
