We propose a top-down algorithm for extracting k-best lists from a parser. Our algorithm, TKA∗ is a variant of the kbest A∗ (KA∗) algorithm of Pauls and Klein (2009). In contrast to KA∗, which performs an inside and outside pass before performing k-best extraction bottom up, TKA∗ performs only the inside pass before extracting k-best lists top down. TKA∗ maintains the same optimality and effici...

Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top K items from noisy pairwise comparisons. In our setting, we are non-actively given r pairwise comparisons between each pair of n items, where each comparison has noise constrained by a very general noise model called the strong stochastic transitiv...

Atpresent, most of the applications return to the user a limited set of ranked results based on the individual user’s preferences, which are commonly validated through top-k queries. From the perspective of a manufacturer, it is imperative that the products appear in the highest ranked positions for many different user preferences, otherwise the product is not visible to the potential customers...

Conventional frequent pattern mining algorithms require users to specify some minimum support threshold. If that specified-value is large, users may lose interesting information. In contrast, a small minimum support threshold results in a huge set of frequent patterns that users may not be able to screen for useful knowledge. To solve this problem and make algorithms more user-friendly, an idea...

Recent boom in internet growth and the advancement in internet security have led to rapid growth in Ecommerce and related services. In this context, capturing the preferences of customers plays an important role in decisions about the design and launch of new products in the market. The science that primarily deals with the support of such decisions is the Operational Research. Since many of th...

In AI and Web communities, many applications utilize PageRank. To obtain high PageRank score nodes, the original approach iteratively computes the PageRank score of each node until convergence by using the whole graph. If the graph is large, this approach is infeasible due to its high computational cost. The goal of this study is to find top-k PageRank score nodes efficiently for a given graph ...

Several algorithms for top-k query processing over web data sources have been proposed, where sources return relevance scores for some query predicate, aggregated through a composition function. They assume specific conditions for the type of source access (sorted and/or random) and for the access cost, and propose various heuristics for choosing the next source to probe, while generally trying...

The ability to compute top-k matches to eXtensible Markup Language (XML) queries is gaining importance owing to the increasing of large XML repositories. Current work on top-k match to XML queries mainly focuses on employing XPath, XQuery or NEXI as the query language, whereas little work has concerned on top-k match to XML keyword search. In this study, the authors propose a novel two-layer-ba...

This paper addresses a problem of searching the best K objects in more attributes, which is indicated as a top-K problem. Last few years, research of top-K problem solving is in progress. The aim of this paper was to provide an introduction into top-K problem and present various interesting top-K processing methods. Firstly, this paper focuses on the family of threshold algorithm, which uses mo...

This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. We start with the Naive approach. We then introduce a populated-cell list (PC-list), a sorted list of non-empty cells of a grid (grid is used to index our data). Using PC-list, our top-k outlier de...