Discovering event evolution patterns from document sequences

Recent advances in information and networking technologies havecontributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns (EPs) in this paper) in sequences of documents. The discovery of such EPs can be applied in domains such as knowledge management and used to facilitate existing document management and retrieval techniques [e.g., event tracking (ET)]. Specifically, we propose and design an EP discovery technique for mining EPs from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating ET. Measured by miss and false alarm rates, the EP-supported ET (EPET) technique exhibits better tracking effectiveness than a traditional ET technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of EPs in supporting ET and suggests that the proposed EP technique could effectively discover event episodes and EPs in sequences of documents. Index Terms--Document clustering, event evolution, event tracking (ET), evolution patterns (EPs), knowledge management, temporal patterns, text mining.