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In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work has shown that certain level of noise in relevance judgments has little effect on evaluation, especially for comparison purposes. Recently learning to rank has become one of the(More)
The key element of many query processing tasks can be formalized as calculation of similarities between queries. These include query suggestion, query reformulation, and query expansion. Although many methods have been proposed for query similarity calculation, they could perform poorly on rare queries. As far as we know, there was no previous work(More)
Bulletin Board Systems (BBS), similar to blogs, newsgroups, online forums, etc., are online broadcasting spaces where people can exchange ideas and make announcements. As BBS are becoming valuable repositories of knowledge and information, effective BBS search engines are required to make the information universally accessible and useful. However, the(More)
Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search. The lack of features affects the efficiency of collection selection in distributed(More)
Twitter-like services are now a popular kind of online social networking services, in which user can express themselves, share contents, and follow others they are interested in. User modeling, building a model for user's interests, is a key problem in many social networking applications, such as recommendation, advertisement, etc. This paper focuses on(More)
Web collection is essential for many Web based researches such as Web Information Retrieval (IR), Web data mining, Corpus linguistics and so on. However, it is usually expensive and time-consuming to collect a large scale of Web pages in lab-based environment and public-available collection becomes a necessity for these researches. In this study, we present(More)
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources(More)