Xiaoshi Yin

From WikiPapers
Jump to: navigation, search

Xiaoshi Yin is an author.


Only 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
A survival modeling approach to biomedical search result diversification using wikipedia Biomedical IR
Survival modeling
SIGIR English 2010 0 0
Building taxonomy of web search intents for name entity queries Query clustering
Web search intent
Proceedings of the 19th International Conference on World Wide Web, WWW '10 English 2010 A significant portion of web search queries are name entity queries. The major search engines have been exploring various ways to provide better user experiences for name entity queries, such as showing "search tasks" (Bing search) and showing direct answers (Yahoo!, Kosmix). In order to provide the search tasks or direct answers that can satisfy most popular user intents, we need to capture these intents, together with relationships between them. In this paper we propose an approach for building a hierarchical taxonomy of the generic search intents for a class of name entities (e.g., musicians or cities). The proposed approach can find phrases representing generic intents from user queries, and organize these phrases into a tree, so that phrases indicating equivalent or similar meanings are on the same node, and the parent-child relationships of tree nodes represent the relationships between search intents and their sub-intents. Three different methods are proposed for tree building, which are based on directed maximum spanning tree, hierarchical agglomerative clustering, and pachinko allocation model. Our approaches are purely based on search logs, and do not utilize any existing taxonomies such as Wikipedia. With the evaluation by human judges (via Mechanical Turk), it is shown that our approaches can build trees of phrases that capture the relationships between important search intents. 0 0
Promoting Ranking Diversity for Biomedical Information Retrieval Using Wikipedia English 2010 In this paper, we propose a cost-based re-ranking method to promote ranking diversity for biomedical information retrieval. The proposed method concerns with finding passages that cover many different aspects of a query topic. First, aspects covered by retrieved passages are detected and explicitly presented by Wikipedia concepts. Then, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, retrieved passages are re-ranked using the proposed cost-based re-ranking method which ranks a passage according to the number of new aspects covered by the passage and the query-relevance of aspects covered by the passage. A series of experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed method in promoting ranking diversity for biomedical information retrieval. 0 0