Microsoft Research Asia participated in the Subtopic Mining subtask and Document Ranking subtask of the NTCIR-10 INTENT Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, clickthrough data and top results of the queries, and rank them based on their importance for the given query. In the Document Ranking subtask, we diversify top search results by estimating the int...

We describe our participation in the subtopic mining and document ranking subtasks of the NTCIR-9 Intent task, for both Chinese and Japanese languages. In the subtopic mining subtask, we experiment with a novel data-driven approach for ranking reformulations of an ambiguous query. In the document ranking subtask, we deploy our state-ofthe-art xQuAD framework for search result diversification.

Conventional approaches to visual search re-ranking empirically take the “classification performance” as the optimization objective, in which each visual document is determined relevant or not, followed by a process of increasing the order of relevant documents. First show that the classification performance fails to produce a globally optimal ranked list, and then formulate re-ranking as an op...

Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...

LEWIS CHURCH: Combinatoric Models of Information Retrieval Ranking Methods and Performance Measures for Weakly-Ordered Document Collections (Under the direction of Robert M. Losee) This dissertation answers three research questions: (1) What are the characteristics of a combinatoric measure, based on the Average Search Length (ASL), that performs the same as a probabilistic version of the ASL?;...

In an expert search task, the user’s need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the user’s query. In this work, we propose a novel approach for estimating and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a vot...

The previous work has justified the assumption that document ranking can be improved by further considering the coarse-grained relations in various linguistic levels (e.g., lexical, syntactical and semantic). To the best of our knowledge, little work is reported to incorporate the fine-grained ontological relations (e.g., ) in document ranking. Two contributions are wo...

Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate persons with relevant expertise to a query is generated after consideration of a document ranking. Many models exist for aggregate ranking tasks, however obtaining an effective and robust setting for different aggregat...

Document retrieval techniques have proven to be competitive methods in the evaluation of focused retrieval. Although focused approaches such as XML element retrieval and passage retrieval allow for locating the relevant text within a document, using the larger context of the whole document often leads to superior document level ranking. In this paper we investigate the impact of using the docum...