  In the recent rapid growth of web services, IoT, and cloud computing, many
web services and APIs appeared on the web. With the failure of global UDDI
registries, different service repositories started to appear, trying to list
and categorize various types of web services for client applications' discover
and use. In order to increase the effectiveness and speed up the task of
finding compatible Web Services in the brokerage when performing service
composition or suggesting Web Services to the requests, high-level
functionality of the service needs to be determined. Due to the lack of
structured support for specifying such functionality, classification of
services into a set of abstract categories is necessary. We employ a wide range
of Machine Learning and Signal Processing algorithms and techniques in order to
find the highest precision achievable in the scope of this article for the fast
classification of three type of service descriptions: WSDL, REST, and WADL. In
addition, we complement our approach by showing the importance and effect of
contextual information on the classification of the service descriptions and
show that it improves the accuracy in 5 different categories of services.
