In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clust...

We tested whether the N400 event-related potential (ERP) indexes the integration of semantic knowledge in the context or whether it indexes the inhibition of activated, but inappropriate, knowledge. A distractor-prime-target word sequence was presented in each trial. Subjects had to make semantic relatedness judgments on prime-target pairs. In the first experiment, subjects had an additional ta...

In this paper, we address the task of crosslingual semantic relatedness. We introduce a method that relies on the information extracted from Wikipedia, by exploiting the interlanguage links available between Wikipedia versions in multiple languages. Through experiments performed on several language pairs, we show that the method performs well, with a performance comparable to monolingual measur...

Determining the semantic relatedness between two words refers to computing a statistical measure of similarity between those words. Word similarity measures are useful in a wide range of applications such as natural language processing, query recommendation, relation extraction, spelling correction, document comparison and other information retrieval tasks. Although several methods that address...

Matrix and tensor factorization have been applied to a number of semantic relatedness tasks, including paraphrase identification. The key idea is that similarity in the latent space implies semantic relatedness. We describe three ways in which labeled data can improve the accuracy of these approaches on paraphrase classification. First, we design a new discriminative term-weighting metric calle...

This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide a vast amount of structured world knowledge about the terms of interest. Our system, the Wikipedia Link Vector Model or WLVM, is unique in that it does so using only the hyperlink structure of Wikipedia rather than its full textual content. To evaluate ...

Over the past fifteen years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retr...