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...

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...

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...

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four crite...

We study generalization properties of ranking algorithms in the setting of the k-partite ranking problem. In the k-partite ranking problem, one is given examples of instances labeled with one of k ordered ‘ratings’, and the goal is to learn from these examples a real-valued ranking function that ranks instances in accordance with their ratings. This form of ranking problem arises naturally in a...

We design the first polynomial time approximation schemes (PTASs) for the Minimum Betweenness problem in tournaments and some related higher arity ranking problems. This settles the approximation status of the Betweenness problem in tournaments along with other ranking problems which were open for some time now. The results depend on a new technique of dealing with fragile ranking constraints a...