This paper presents an alternative method to measuring word-word semantic relatedness in distributional semantics framework. The main idea is to represent target words as rankings of all co-occurring words in a text corpus, ordered by their tf – idf weight and use a metric between rankings (such as Jaro distance or Rank distance) to compute semantic relatedness. This method has several advantag...

In this paper, we examine the problem of indexing over non-metric distance functions. In particular, we focus on a general class of distance functions, namely Bregman Divergence [6], to support nearest neighbor and range queries. Distance functions such as KL-divergence and Itakura-Saito distance, are special cases of Bregman divergence, with wide applications in statistics, speech recognition ...

An alternative perspective on dimensionality reduction is offered by Multidimensional scaling (MDS). MDS is another classical approach that maps the original high dimensional space to a lower dimensional space, but does so in an attempt to preserve pairwise distances. That is MDS addresses the problem of constructing a configuration of t points in Euclidean space by using information about the ...

Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distance function as a “black box”. The structure is able to speed up nearest neighbor searching in a v...

In 1960, Klee showed that a subset of a Euclidean space must be a singleton provided that each point in the space has a unique farthest point in the set. This classical result has received much attention; in fact, the Hilbert space version is a famous open problem. In this paper, we consider Klee sets from a new perspective. Rather than measuring distance induced by a norm, we focus on the case...

To improve the convergence properties of 'embedding' distance geometry, a new approach was developed by combining the distance-geometry methodology with a genetic algorithm. This new approach is called DG-OMEGA (DG omega, optimised metric matrix embedding by genetic algorithms). The genetic algorithm was used to combine well-defined parts of individual structures generated by the distance-geome...