Fast development of information and communication technologies made available vast amounts of heterogeneous information. With these amounts growing faster and faster, information integration and search technologies are becoming a key for the success of information society. To handle such amounts efficiently, data needs to be leveraged and analysed at deep levels. Metadata is a traditional way of getting leverage over the data. Deeper levels of analysis include language analysis, starting from purely string-based (keyword) approaches, continuing with syntactic-based approaches and now semantics is about to be included in the processing loop. Metadata gives a leverage over the data. Often a natural language, being the easiest way of expression, is used in metadata. We call such metadata ``natural language metadata''. The examples include various titles, captions and labels, such as web directory labels, picture titles, classification labels, business directory category names. These short pieces of text usually describe (sets of) objects. We call them ``descriptive phrases''. This thesis deals with a problem of understanding natural language metadata for its further use in semantics aware applications. This thesis contributes by portraying descriptive phrases, using the results of analysis of several collected and annotated datasets of natural language metadata. It provides an architecture for the natural language metadata understanding, complete with the algorithms and the implementation. This thesis contains the evaluation of the proposed architecture.