Cross Document Event Clustering Using Knowledge Mining from Co-Reference Chains

Abstract. Unification of the terminology usages which captures more term semantics is useful for event clustering. This paper proposes a metric of normalized chain edit distance to mine controlled vocabulary from cross-document coreference chains incrementally. A novel threshold model that incorporates time decay function and spanning window utilizes the controlled vocabulary for event clustering on streaming news. The experimental results show that the proposed system has 16 % performance increase compared to the baseline system and 6 % performance increase compared to the system without introducing controlled vocabulary.