In this introduction, we provide the scholarly background that motivates special issue and briefly discuss its content. We touch on contemporary debates in symbolic interactionism, ethnomethodology conversation analysis, anthropology inform research undertaken by contributors. conclude deriving six interrelated themes—intersection, entwinement, multimodal, contextually embedded, structured, ser...

This study attempts to pinpoint the factors that restrict reliable word sense annotation, focusing on the influence of the number of senses annotators use and the semantic granularity of those senses. Both of these factors may be possible causes of low interannotator agreement (ITA) when tagging with fine-grained word senses, and, consequently, low WSD system performance (Ng et al., 1999; Snyde...

Supervised and semi-supervised sense disambiguation methods will mis-tag the instances of a target word if the senses of these instances are not defined in sense inventories or there are no tagged instances for these senses in training data. Here we used a model order identification method to avoid the misclassification of the instances with undefined senses by discovering new senses from mixed...

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in...

In this paper we define a similarity measure between semantic valence frames. It is based on a similarity measure between senses of verb arguments. Senses are represented by general semantic categories or wordnet synset. The measure is supposed to be used for aggregation of semantic valence frames that differ in NP/PP slot senses but represent the same meaning of the verb.

We describe an unusual data set of thousands of annotated images with interesting sense phenomena. Natural language image sense annotation involves increased semantic complexities compared to disambiguating word senses when annotating text. These issues are discussed and illustrated, including the distinction between word senses and iconographic senses.