  Developers prefer to utilize third-party libraries when they implement some
functionalities and Application Programming Interfaces (APIs) are frequently
used by them. Facing an unfamiliar API, developers tend to consult tutorials as
learning resources. Unfortunately, the segments explaining a specific API
scatter across tutorials. Hence, it remains a challenging issue to find the
relevant segments. In this study, we propose a more accurate model to find the
exact tutorial fragments explaining APIs. This new model consists of a text
classifier with domain specific features. More specifically, we discover two
important indicators to complement traditional text based features, namely
co-occurrence APIs and knowledge based API extensions. In addition, we
incorporate Word2Vec, a semantic similarity metric to enhance the new model.
Extensive experiments over two publicly available tutorial datasets show that
our new model could find up to 90% fragments explaining APIs and improve the
state-of-the-art model by up to 30% in terms of F-measure.
