| SunGwan Han|
(Alternative names for this author)
|Co-authors||He D., Hee-Seop Han, Jian Jiang, Soo-Hwan Kim, Wu J.|
|Authorship||Publications (2), datasets (0), tools (0)|
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PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|PITT at TREC 2011 session track||Query language model
|NIST Special Publication||English||2011||In this paper, we introduce our approaches for TREC 2011 session track. Our approaches focus on combining different query language models to model information needs in a search session. In RL1 stage, we build ad hoc retrieval system using sequential dependence model (SDM) on current query. In RL2 stage, we build query language models by combining SDM features (e.g. single term, ordered phrase, and unordered phrase) in both current query and previous queries in the session, which can significantly improve search performance. In RL3 and RL4, we combine query model in RL2 with two different pseudo-relevance feedback query models: in RL3, we use top ranked Wikipedia documents from RL2's results as pseudo-relevant documents; in RL4, snippets of the documents clicked by users in a search session are used. Our evaluation results indicate: texts of previous queries in a session are effective resources for estimating query models and improving search performance; mixing query model in RL2 with the query model estimated using click-through data (in RL4) can improve performance in evaluation setting that considers all subtopics, but no improvement is observed in evaluation setting that considers the only subtopic of current query; our methods of mixing query model in RL2 with query model in RL3 did not improve search performance over RL2 in any of the two evaluation settings.||0||0|
|The study on effective programming learning using wiki community systems||Knowledge management