In four experiments, semantic satiation was investigated in young and old adults. In the first two experiments, subjects were repeatedly presented a word (e.g., DOG) and then were presented a pair of words (e.g., DOG-CAT or DOG-CHAIR) for a relatedness decision. The results of both experiments indicated that for the young adults, the relatedness effect (the difference between response latency o...

The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term ...

Semantic relatedness is a very important factor for the coreference resolution task. To obtain this semantic information, corpusbased approaches commonly leverage patterns that can express a specific semantic relation. The patterns, however, are designed manually and thus are not necessarily the most effective ones in terms of accuracy and breadth. To deal with this problem, in this paper we pr...

Explicit Semantic Analysis (ESA) is an approach to calculate the semantic relatedness between two words or natural language texts with the help of concepts grounded in human cognition. ESA usage has received much attention in the field of natural language processing, information retrieval and text analysis, however, performance of the approach depends on several parameters that are included in ...

Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent enti...

Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs ...

The measurement of the semantic relatedness has many applications in natural language processing, and many different measures have been proposed. Most of these measures use WordNet as their central resource and not domain ontologies of a particular context. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, gra...

While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet or Wikitionary, the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform the lexicon-based methods. However, word senses are not disambiguated in the training phase of both Word2Vec and GloVe, two famous word embeddi...

Folksonomy and tagging systems, which allow users to interactively annotate a pool of shared resources using descriptive tags, have enjoyed phenomenal success in recent years. The concepts are organized as a map in human mind, however, the tags in folksonomy, which reflect users’ collaborative cognition on information, are isolated with current approach. What we do in this paper is to estimate ...