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 naïve 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/10.1145/3338906.3338943Software developers reuse software libraries and frameworks through Application Programming Interfaces (APIs). They often rely on reference documentation to identify which API elements are relevant for the task at hand, how the API can be instantiated, configured, and combined [1]. Compared to other knowledge sources, such as tutorials and Q&A portals, reference documentation like JavaDoc and PyDoc are considered the official API technical documentation. They provide detailed and fundamental information about API elements, components, operations, and structures [2, 3]. Figure 1: A reference documentation page in the JDK API annotated with the knowledge types it contains. DirectiveAs API documentation can be thousands of pages long [4,5], accessing relevant knowledge can be tedious and timeconsuming [1]. Moreover, the information necessary to accomplish a task can be scattered across the documentation pages of multiple elements, such as classes, methods, and properties. Thus, developers try to use other sources to fulfill their information needs. For example, although the Java Development Kit (JDK) API documentation contains more than 7,000 pages, as of early 2019, there are more than 3 million StackOverflow posts tagged as java.Over the last decade, sof...