Kuo, June-JeiJune-JeiKuoHSIN-HSI CHEN2020-05-042020-05-042005https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750473709&doi=10.1016%2fj.ipm.2006.07.016&partnerID=40&md5=31fd5607063d7e18f0f3fc5258e31b20Unifying terminology usages which captures more term semantics is useful for event clustering. This paper proposes a metric of normalized chain edit distance to mine, incrementally, controlled vocabulary from cross-document co-reference chains. Controlled vocabulary is employed to unify terms among different co-reference chains. A novel threshold model that incorporates both time decay function and spanning window uses the controlled vocabulary for event clustering on streaming news. Under correct co-reference chains, the proposed system has a 15.97% performance increase compared to the baseline system, and a 5.93% performance increase compared to the system without introducing controlled vocabulary. Furthermore, a Chinese co-reference resolution system with a chain filtering mechanism is used to experiment on the robustness of the proposed event clustering system. The clustering system using noisy co-reference chains still achieves a 10.55% performance increase compared to the baseline system. The above shows that our approach is promising. © 2006 Elsevier Ltd. All rights reserved.Co-reference chains; Controlled vocabulary; Event clustering; Multi-document summarizationInformation theory; Knowledge acquisition; Robustness (control systems); Semantics; Thesauri; Co-reference chains; Event clustering; Multi document summarization; Data miningconference paper10.1007/11562382_102-s2.0-33750473709https://doi.org/10.1007/11562382_10