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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 neighborhood after the profitable sequence of data - trieval with Asian Languages (IRAL) workshops held in six di?erent destinations in Asia, ranging from 1996. whereas the IRAL workshops had their specialise in inf- mation retrieval difficulties concerning Asian languages, AIRS covers a much wider scope of purposes, platforms, applied sciences and idea points of knowledge retrieval in textual content, audio, photograph, video and multimedia information. This extension of the scope re?ects and fosters expanding study actions in details retrieval during this area and the growing to be desire 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 bought 106papersfromninecountriesinAsiaandNorthAmerica,fromwhich28papers (26%) have been offered in oral classes and 38 papers in poster classes (36%). It used to be a superb problem for this system Committee to pick the easiest one of the first-class papers. The low reputation premiums witness the luck of this year’s convention. After a protracted dialogue among the AIRS 2004 guidance Committee and Springer, the writer agreed to put up our complaints within the Lecture Notes in computing device technology (LNCS) sequence, that's SCI-indexed. We suppose that this strongly attests to the superb caliber of the papers.
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Additional resources for Information Retrieval Technology: Asia Information Retrieval Symposium, AIRS 2004, Beijing, China, October 18-20, 2004. Revised Selected Papers
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To address question (2), we define the new centroid to be: G c ′ = 1 | R + 1 + α | y ∑ i ∈ R (i) G G ( yi + α ⋅ y new ) + γ 1 | S n | y G yi ∑ i ∈ S n (3) (i) G G where c ′ is the new centroid after adaptation, y new is the vector of the incoming story G used to adapt the centroid; α is the weight given to the vector y new . e. fixed weight adaptation). However, intuitively, different stories should have different weights; stories with a higher confidence score S(d,C) should have higher weight.
For Chinese phrase segmentation we reconstructed a phrase dictionary from segmented Chinese data provided by the LDC. 2 Evaluation Measures To evaluate the performance of our system, we chose the conventional measures for event tracking used in TDT benchmark evaluations[7]. Each story is assigned a label of YES/NO for each of the topics. If the system assigns a YES to a story labeled NO by humans, it commits a false alarm error. If the system assigns a NO to a story labeled YES, it commits a miss error.
For bigram-based segmentation, we simply used all two consecutive Chinese characters as tokens. 1 compares the tracking effectiveness of these two alternatives. 3 Experiments and Results This section presents our experimental setup and results. 3 discusses previously published results. The following describe our experiments, which can be grouped as follows: 1. Mixed language event tracking: Here, the topics as well as the stories are either in English, or translated into English from Mandarin by SYSTRAN.