By Chen Wenliang, Chang Xingzhi, Wang Huizhen, Zhu Jingbo, Yao Tianshun (auth.), Sung Hyon Myaeng, Ming Zhou, Kam-Fai Wong, Hong-Jiang Zhang (eds.)
TheAsiaInformationRetrievalSymposium(AIRS)wasestablishedbytheAsian info retrieval group after the winning sequence of knowledge - trieval with Asian Languages (IRAL) workshops held in six di?erent destinations in Asia, ranging from 1996. whereas the IRAL workshops had their specialize in inf- mation retrieval difficulties related to Asian languages, AIRS covers a much broader scope of functions, platforms, applied sciences and conception elements of data retrieval in textual content, audio, picture, video and multimedia info. This extension of the scope re?ects and fosters expanding learn actions in details retrieval during this sector and the transforming into want for collaborations throughout subdisciplines. we're very happy to document that we observed a pointy bring up within the variety of submissions and their caliber, in comparison to the IRAL workshops. We obtained 106papersfromninecountriesinAsiaandNorthAmerica,fromwhich28papers (26%) have been offered in oral classes and 38 papers in poster classes (36%). It was once an exceptional problem for this system Committee to pick the simplest one of the very good papers. The low reputation premiums witness the good fortune of this year’s convention. After a protracted dialogue among the AIRS 2004 guidance Committee and Springer, the writer agreed to post our lawsuits within the Lecture Notes in laptop technology (LNCS) sequence, that's SCI-indexed. We think that this strongly attests to the wonderful caliber of the papers.
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Additional info for Information Retrieval Technology: Asia Information Retrieval Symposium, AIRS 2004, Beijing, China, October 18-20, 2004. Revised Selected Papers
In our future work we plan to concentrate on improving the translation accuracy. Potential methods include better machine translation techniques, named entity tracking, and investigating various sampling strategies. References 1. Allan, J. ): Topic Detection and Tracking: Event Based Information Retrieval. Kluwer Academic Press (2002) 2. : Topic Detection and Tracking Pilot Study Final Report. In Proc. of the Broadcast News Transcription and Understanding Workshop (1998) 3. : The BBN Crosslingual Topic Detection and Tracking System.
In this paper, we propose a novel algorithm for document clustering based on a combinatorial framework of the PDDP algorithm and a variant of the EM algorithm. As discussed above, each algorithm has its own strengths and weaknesses. We are interested in the idea of the PDDP algorithm that uses the PCA for analyzing the data. More speciﬁcally, it splits the data samples into two subclusters based on the hyperplane normal to the principal direction derived from the covariance matrix of the data. When the principal direction is not representative, the corresponding hyperplane tends to produce individual clusters with wrongly partitioned contents.
We also use the Detection-Error Tradeoff (DET) curve to show how the threshold would affect the trade-off between the miss and false alarm rates. 3 Mixed Language Event Tracking Results In the mixed language event tracking task, LIMSI was the best in the benchmark evaluation in TDT2001. 1415, as released by NIST. In order to compare our new weighted and normalized adaptation (NWAdapt) with LIMSI’s weighted adaptation (LWAdapt) and our old adaptation approach (FWAdapt), we implemented LIMSI’s approach in our system.