In this paper, we explore the use of new page segmentation algorithm using both visual and structural mark-up (,) to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web. We propose a new ranking function that combines content and link rank based on propagation of scores over links on block-to-page grap...

In this paper, we describe our participation in the English Subtopic Mining and Document Ranking subtasks of the NTCIR-11 IMINE Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, query dimensions, and Freebase entities of a given query, rank them based on their importance for the given query, and finally construct a two-level hierarchy of subtopics. In the Document ...

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper,...

This paper describes the development of a polyphonic music retrieval system with the n-gram approach. Musical n-grams are constructed from polyphonic musical performances in MIDI using the pitch and rhythm dimensions of music. These are encoded using text characters enabling the musical words generated to be indexed with existing text search engines. The Lemur Toolkit was adapted for the develo...

This paper presents a novel strategy improvement algorithm for parity and payoff games, which is guaranteed to select, in each improvement step, an optimal combination of local strategy modifications. Current strategy improvement methods stepwise improve the strategy of one player with respect to some ranking function, using an algorithm with two distinct phases: They first choose a modificatio...

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...

Web search quality can vary widely across languages, even for the same information need. We propose to exploit this variation in quality by learning a ranking function on bilingual queries: queries that appear in query logs for two languages but represent equivalent search interests. For a given bilingual query, along with corresponding monolingual query log and monolingual ranking, we generate...

Many sources on the Internet and elsewhere rank the objects in query results according to how well these objects match the original query. For example, a real-estate agent might rank the available houses according to how well they match the user's preferred location and price. In this environment, \meta-brokers" usually query multiple autonomous, heterogeneous sources that might use varying res...

In Multi-criteria Decision Making (MCDM) problems dealing with qualitative criteria and uncertain information the use of linguistic values is suitable for the experts in order to express their judgments. It is common that the group of experts involved in such problems have different degrees of knowledge about the criteria, so we propose a multi-granular linguistic framework such that each exper...

Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the results pages. By not downloading additional information, such as the full document, we decrease b...