Correctly predicting abbreviations given the full forms is important in many natural language processing systems. In this paper we propose a two-stage method to find the corresponding abbreviation given its full form. We first use the contextual information given a large corpus to get abbreviation candidates for each full form and get a coarse-grained ranking through graph random walk. This coa...

Reasoning about agent preferences on a set of alternatives, and the aggregation of such preferences into some social ranking is a fundamental issue in reasoning about multi-agent systems. When the set of agents and the set of alternatives coincide, we get the ranking systems setting. A famous type of ranking systems are page ranking systems in the context of search engines. Such ranking systems...

We argue that ranking algorithms for XML should reflect the actual combined content and structure constraints of queries, while at the same time producing equal rankings for queries that are semantically equal. Ranking algorithms that produce different rankings for queries that are semantically equal are easily detected by tests on large databases: We call such algorithms not sound. We report t...

We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analyzing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequenc...

We argue that ranking algorithms for XML should reflect the actual combined content and structure constraints of queries, while at the same time producing equal rankings for queries that are semantically equal. Ranking algorithms that produce different rankings for queries that are semantically equal are easily detected by tests on large databases: We call such algorithms not sound. We report t...

A vertex k-ranking of a graph G is a function c : V (G) → {1, . . . , k} such that if c(u) = c(v), u, v ∈ V (G) then each path connecting vertices u and v contains a vertex w with c(w) > c(u). If each vertex v has a list of integers L(v) and for a vertex ranking c it holds c(v) ∈ L(v) for each v ∈ V (G) then c is called L-list k-ranking, where L = {L(v) : v ∈ V (G)}. In this paper we investigat...

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete one, which we call the coarsening process. To this end, we propose the concept ...

Centering Theory is applied to a narrated film retelling in Kaqchikel Mayan in order to better understand discourse constraints on the form of referring expression. It is shown that Backwards Looking Centers are very often encoded by zero pronouns, and that center Shifts more often employ full pronouns and full noun phrases than do center Continues and Retains. Preverbal pronouns and full noun ...

In this article, we use innovative full-text citation analysis along with supervised topic modeling and networkanalysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefine...

In this paper we will show that Grammatical Inference is applicable to Natural Language Processing. Given the wide and complex range of structures appearing in an unrestricted Natural Language like English, full Grammatical Inference, yielding a comprehensive syntactic and semantic definition of English, is too much to hope for at present. Instead, we focus on techniques for dealing with ambigu...