  Using API reference documentation like JavaDoc is an integral part of
software development. Previous research introduced a grounded taxonomy that
organizes API documentation knowledge in 12 types, including knowledge about
the Functionality, Structure, and Quality of an API. We study how well modern
text classification approaches can automatically identify documentation
containing specific knowledge types. We compared conventional machine learning
(k-NN and SVM) and deep learning approaches trained on manually annotated Java
and .NET API documentation (n = 5,574). When classifying the knowledge types
individually (i.e., multiple binary classifiers) the best AUPRC was up to 87%.
The deep learning and SVM classifiers seem complementary. For four knowledge
types (Concept, Control, Pattern, and Non-Information), SVM clearly outperforms
deep learning which, on the other hand, is more accurate for identifying the
remaining types. When considering multiple knowledge types at once (i.e.,
multi-label classification) deep learning outperforms na\"ive baselines and
traditional machine learning achieving a MacroAUC up to 79%. We also compared
classifiers using embeddings pre-trained on generic text corpora and
StackOverflow but did not observe significant improvements. Finally, to assess
the generalizability of the classifiers, we re-tested them on a different,
unseen Python documentation dataset. Classifiers for Functionality, Concept,
Purpose, Pattern, and Directive seem to generalize from Java and .NET to Python
documentation. The accuracy related to the remaining types seems API-specific.
We discuss our results and how they inform the development of tools for
supporting developers sharing and accessing API knowledge. Published article:
https://doi.org/10.1145/3338906.3338943
