  Named Entity Recognition and Disambiguation (NERD) systems have recently been
widely researched to deal with the significant growth of the Web. NERD systems
are crucial for several Natural Language Processing (NLP) tasks such as
summarization, understanding, and machine translation. However, there is no
standard interface specification, i.e. these systems may vary significantly
either for exporting their outputs or for processing the inputs. Thus, when a
given company desires to implement more than one NERD system, the process is
quite exhaustive and prone to failure. In addition, industrial solutions demand
critical requirements, e.g., large-scale processing, completeness, versatility,
and licenses. Commonly, these requirements impose a limitation, making good
NERD models to be ignored by companies. This paper presents TANKER, a
distributed architecture which aims to overcome scalability, reliability and
failure tolerance limitations related to industrial needs by combining NERD
systems. To this end, TANKER relies on a micro-services oriented architecture,
which enables agile development and delivery of complex enterprise
applications. In addition, TANKER provides a standardized API which makes
possible to combine several NERD systems at once.
