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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ipm.2006.07.015 Byline: Wen-Cheng Lin, Yih-Chen Chang, Hsin-Hsi Chen Keywords: Cross-language image retrieval; ImageCLEF; Language translation; Medium transformation; Trans-media dictionary Abstract: This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. First, we mine the relationships between text and images and employ the mined relationships to construct visual queries from textual ones. Then, the retrieval results of textual and visual queries are combined. To evaluate the proposed approach, we conduct English monolingual and Chinese-English cross-language retrieval experiments. The selection of suitable textual query terms to construct visual queries is the major issue. Experimental results show that the proposed approach improves retrieval performance, and use of nouns is appropriate to generate visual queries. Author Affiliation: Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan Article History: Received 17 May 2006; Accepted 25 July 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ipm.2006.07.016 Byline: June-Jei Kuo, Hsin-Hsi Chen Keywords: Controlled vocabulary; Co-reference chains; Event clustering; Multi-document summarization 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. Author Affiliation: Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan Article History: Received 16 May 2006; Accepted 25 July 2006