The impact of semantic document expansion on cluster-based fusion for microblog search
|The impact of semantic document expansion on cluster-based fusion for microblog search|
|Author(s)||Liang S., Ren Z., De Rijke M.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Data fusion, Information retrieval, Clustering information, Document expansions, Fusion methods, Optimal setting, Pre-processing step, Search Algorithms, Search problem, Wikipedia articles, Semantics)|
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The impact of semantic document expansion on cluster-based fusion for microblog search is a 2014 conference paper written in English by Liang S., Ren Z., De Rijke M. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Searching microblog posts, with their limited length and creative language usage, is challenging. We frame the microblog search problem as a data fusion problem. We examine the effectiveness of a recent cluster-based fusion method on the task of retrieving microblog posts. We find that in the optimal setting the contribution of the clustering information is very limited, which we hypothesize to be due to the limited length of microblog posts. To increase the contribution of the clustering information in cluster-based fusion, we integrate semantic document expansion as a preprocessing step. We enrich the content of microblog posts appearing in the lists to be fused by Wikipedia articles, based on which clusters are created. We verify the effectiveness of our combined document expansion plus fusion method by making comparisons with microblog search algorithms and other fusion methods.
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