In this paper we present a new approach for measuring the relatedness between text segments, based on implicit semantic links between their words, as offered by a word thesaurus, namely WordNet. The approach does not require any type of training, since it exploits only WordNet to devise the implicit semantic links between text words. The paper presents a prototype on-line demo of the measure, t...

The role of morphological, semantic, and form-based factors in the early stages of visual word recognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic relatedness were presented at three SOAs (36, 48, and...

Evaluating semantic relatedness of Web resources is still an open challenge. This paper focuses on knowledge-based methods, which represent alternative to corpus-based approaches, and rely in general the availability knowledge graphs. In particular, we have selected 10 methods from existing literature, that been organized according it adjacent resources, triple patterns, weights-based methods. ...

We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relate...

Measures of Semantic Relatedness are well established in Natural Language Processing. Their purpose is to determine the degree of relatedness between two words without specifying the nature of their relationship. Most of these measures work only between pairs of words in a single language. We propose a novel method of measuring semantic relatedness between pairs of words in two different langua...

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from ...