Computing semantic relatedness of natural language texts is a key component of tasks such as information retrieval and summarization, and often depends on knowledge from a broad range of real-world concepts and relationships. We address this knowledge integration issue with a method of computing semantic relatedness using personalized PageRank (random walks) on a graph derived from Wikipedia. T...

Measures of similarity have traditionally focused on computing the semantic relatedness between pairs of words and texts. In this paper, we construct an evaluation framework to quantify cross-modal semantic relationships that exist between arbitrary pairs of words and images. We study the effectiveness of a corpus-based approach to automatically derive the semantic relatedness between words and...

Personalized recommendations to cold start user is one of the significant challenges in information filtering systems. Most existing systems inherited idea collaborative (CF) and avoids item metadata. For instance, consider a book domain whose metadata are author, publisher, language...etc. The elimination due diverse nature attributes cross-domain environment. Because this ideology, it hard fo...

In this paper, we introduce a WordNetbased measure of semantic relatedness by combining the structure and content of WordNet with co–occurrence information derived from raw text. We use the co–occurrence information along with the WordNet definitions to build gloss vectors corresponding to each concept in WordNet. Numeric scores of relatedness are assigned to a pair of concepts by measuring the...

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, the authors aim at measuring ...

Theories of semantic memory differ in the extent to which relationships among concepts are captured via associative or via semantic relatedness. We examined the contributions of these two factors, using a visual world paradigm in which participants selected the named object from a four-picture display. We controlled for semantic relatedness while manipulating associative strength by using the v...

The need to determine semantic relatedness or its inverse, semantic distance, between two lexically expressed concepts is a problem that pervades much of natural language processing such as document summarization, information extraction and retrieval, word sense disambiguation and the automatic correction of word errors in text. Standard ways of measuring similarity between two words on a thesa...

A number of previous studies using picture-word interference (PWI) tasks conducted with speakers of Western languages have demonstrated non-additive effects of semantic and form overlap between pictures and words, which may indicate underlying non-discrete processing stages in lexical retrieval. The present study used Mandarin speakers and presented Chinese characters as distractors. In two exp...

In this paper, we describe our query expansion approach submitted for the Semantic Enrichment task in Cultural Heritage in CLEF (CHiC) 2012. Our approach makes use of an external knowledge base such as Wikipedia and DBpedia. It consists of two major steps, concept candidates generation from knowledge bases and the selection of K-best related concepts. For selecting the K-best concepts, we ranke...

A corpus-based Measure of Semantic Relatedness can be calculated for every pair of words occurring in the corpus, but it can produce erroneous results for many word pairs due to accidental associations derived on the basis of several context features.We propose a novel idea of a partial measure that assigns relatedness values only to word pairs well enough supported by corpus data. Three simple...