@inproceedings{172c58dd52c24020b468699a8a5cd7c2,
title = "Feature analysis in microblog retrieval based on learning to rank",
abstract = "Learning to rank, which can fuse various of features, performs well in microblog retrieval. However, it is still unclear how the features function in microblog ranking. To address this issue, this paper examines the contribution of each single feature together with the contribution of the feature combinations via the ranking SVM for microblog retrieval modeling. The experimental results on the TREC microblog collection show that textual features, i.e. content relevance between a query and a microblog, contribute most to the retrieval performance. And the combination of certain non-textual features and textual features can further enhance the retrieval performance, though non-textual features alone produce rather weak results.",
keywords = "Feature combination, Learning to rank, Microblog retrieval",
author = "Zhongyuan Han and Xuwei Li and Muyun Yang and Haoliang Qi and Sheng Li",
year = "2013",
doi = "10.1007/978-3-642-41644-6\_40",
language = "英语",
isbn = "9783642416439",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "410--416",
booktitle = "Natural Language Processing and Chinese Computing - Second CCF Conference, NLPCC 2013, Proceedings",
address = "德国",
note = "2nd CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2013 ; Conference date: 15-11-2013 Through 19-11-2013",
}