  Semantic text similarity plays an important role in software engineering
tasks in which engineers are requested to clarify the semantics of descriptive
labels (e.g., business terms, table column names) that are often consists of
too short or too generic words and appears in their IT systems. We formulate
this type of problem as a task of matching descriptive labels to glossary
descriptions. We then propose a framework to leverage an existing semantic text
similarity measurement (STS) and augment it using semantic label enrichment and
set-based collective contextualization where the former is a method to retrieve
sentences relevant to a given label and the latter is a method to compute
similarity between two contexts each of which is derived from a set of texts
(e.g., column names in the same table). We performed an experiment on two
datasets derived from publicly available data sources. The result indicated
that the proposed methods helped the underlying STS correctly match more
descriptive labels with the descriptions.
