Cross Document Event Clustering Using Knowledge Mining from Co-reference Chains.
Journal
Information Retrieval Technology, Second Asia Information Retrieval Symposium, AIRS 2005, Jeju Island, Korea, October 13-15, 2005, Proceedings
Pages
121-134
Date Issued
2005
Author(s)
Kuo, June-Jei
Abstract
Unifying 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.
Subjects
Co-reference chains; Controlled vocabulary; Event clustering; Multi-document summarization
Other Subjects
Information theory; Knowledge acquisition; Robustness (control systems); Semantics; Thesauri; Co-reference chains; Event clustering; Multi document summarization; Data mining
Type
conference paper