Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an Information Retrieval (IR) ranking function. The naive approach would be to submit a ...

There has been growing momentum in building information retrieval (IR) systems that consider both relevance and diversity of retrieved information, which together improve the usefulness of search results as perceived by users. Some users may genuinely require a set of multiple results to satisfy their information need as there is no single result that completely fulfils the need. Others may be ...

This paper shows that using linguistically motivated features for English that-complementizer choice in an averaged perceptron model for classification can improve upon the prediction accuracy of a state-of-the-art realization ranking model. We report results on a binary classification task for predicting the presence/absence of a that-complementizer using features adapted from Jaeger’s (2010) ...

In the database retrieval and nearest neighbor classification tasks, the two basic problems are to represent the query and database objects, and to learn the ranking scores of the database objects to the query. Many studies have been conducted for the representation learning and the ranking score learning problems, however, they are always learned independently from each other. In this paper, w...

We consider a setting where a system learns to rank a fixed set of m items. The goal is produce good item rankings for users with diverse interests who interact online with the system for T rounds. We consider a novel top-1 feedback model: at the end of each round, the relevance score for only the top ranked object is revealed. However, the performance of the system is judged on the entire rank...

In this paper, we address the issue of answering PICO (Patient/Problem, Intervention, Comparison, Outcome) clinical queries. The contributions of this work include (1) a new document ranking model based on a prioritized aggregation operator that computes the global relevance score based on the relevance estimation of the semantic facet sub-queries and (2) leverages the importance of the facets ...

In this paper, we describe our participation to the NTCIR-11 IMine task, for both subtopic mining and document ranking sub-tasks. We experimented a new approach for aspect embedding which learns query aspects by selecting (good) expansion terms from a set of resources. In our participation, we used five representative resources: ConceptNet, Wikipedia, query logs, feedback documents and query su...

Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairnes...

NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary aim of this task was to develop a system which is capable of minimizing interaction between the human users and mobile phones while extracting relevant data with respect to given queries. Organizers provided the data represented as information units (iUnits). Each of the iUnits describes a pertinent q...