  Entity Matching is an essential part of all real-world systems that take in
structured and unstructured data coming from different sources. Typically no
common key is available for connecting records. Massive data cleaning and
integration processes require completion before any data analytics, or further
processing can be performed. Although record linkage is frequently regarded as
a somewhat tedious but necessary step, it reveals valuable insights, supports
data visualization, and guides further analytic approaches to the data. Here,
we focus on organization entity matching. We introduce CompanyName2Vec, a novel
algorithm to solve company entity matching (CEM) using a neural network model
to learn company name semantics from a job ad corpus, without relying on any
information on the matched company besides its name. Based on a real-world
data, we show that CompanyName2Vec outperforms other evaluated methods and
solves the CEM challenge with an average success rate of 89.3%.
