Owing to the ever increasing information deluge, it is becoming increasingly difficult to locate relevant information through traditional term-based search methods. Event–based text mining provides a more promising approach, as it also takes into account the semantic relationships between terms. Typical event representations only focus on identifying the type of the event, its par-ticipants and their types. However, additional information, which is essential for correct interpretation of the event, is often present in the text. This includes infor-mation about the polarity, certainty level, intensity/rate/frequency, type and source of the knowledge conveyed by the event. We refer to this additional information as meta-knowledge. This thesis focusses on our work involving the enrichment of events with meta-knowledge information. In this thesis we: • describe the annotation scheme designed specifically to capture meta-knowledge information at the event level• report on the corpora that have been enriched through deployment of the meta-knowledge annotation scheme• describe the work on automated identification of meta-knowledge including: - a broad-ranging study on analysis and identification of polarity of bio-events using three different bio-event corpora - a detailed study on analysis and identification of knowledge source in bio-events found in abstracts as well as in full papers - a first study on analysis and identification of bio-event manner• describe the initial work on a new approach to discourse analysis based on me-ta-knowledge annotations at the event level