This paper presents a new semantic relatedness measure on semantic networks (SN) that uses both hierarchical and non-hierarchical relations. Our approach relies on two assumptions. Firstly, in a given SN, only a few numbers of paths can be considered as “semantically correct” and these paths obey to a given set of rules. Secondly, following a given edge in a path has a cost (which depends on it...

We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and compiled them into a new set of relatedness norms that we call Rel-122. Judgments from individual subjects in our study exhibit high average correlation to the resulting relatedness means (r = 0.77, σ = 0.09, N = 73), although not as high as Resnik’s (1995) upper bound for expected average human cor...

There is a numerous demand for a standard representation of the ubiquitous available information on the web. Developing an efficient algorithm for traversing large ontologies is a key challenge for many semantic web applications. This paper proposes spreading activation over ontology method based on bidirectional search technique in order to detect the relatedness between two human diseases. Th...

Sentence comprehension involves timely computing different types of relations between its verbs and noun arguments, such as morphosyntactic, semantic, and thematic relations. Here, we used EEG technique to investigate the potential differences in thematic role computing and lexical-semantic relatedness processing during on-line sentence comprehension, and the interaction between these two types...

INTRODUCTION This article explores how measures of semantic similarity and relatedness are impacted by the semantic groups to which the concepts they are measuring belong. Our goal is to determine if there are distinctions between homogeneous comparisons (where both concepts belong to the same group) and heterogeneous ones (where the concepts are in different groups). Our hypothesis is that the...

Semantic textual similarity (STS) systems are designed to encode and evaluate the semantic similarity between words, phrases, sentences, and documents. One method for assessing the quality or authenticity of semantic information encoded in these systems is by comparison with human judgments. A data set for evaluating semantic models was developed consisting of 775 English word-sentence pairs, e...

This paper provides system description of the cross-level semantic similarity task for the SEMEVAL-2014 workshop. Crosslevel semantic similarity measures the degree of relatedness between texts of varying lengths such as Paragraph to Sentence and Sentence to Phrase. Latent Semantic Analysis was used to evaluate the cross-level semantic relatedness between the texts to achieve above baseline sco...

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are ...

In this article, we present a comprehensive study aimed at computing semantic relatedness of word pairs. We analyze the performance of a large number of semantic relatedness measures proposed in the literature with respect to different experimental conditions, such as (i) the datasets employed, (ii) the language (English or German), (iii) the underlying knowledge source, and (iv) the evaluation...

This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the wo...