We propose a method for inferring semantic information from textual data in content-based multimedia retrieval. Training examples of images and videos belonging to a specific semantic class are associated with their low-level visual and aural descriptors augmented with textual features such as frequencies of significant words. A fuzzy mapping of a semantic class in the training set to a class o...

In this paper, we propose a model for evaluating the quality of general user-created documents. The model is based on supervised classification approach, in which output scores are considered as quality of given document. In order to utilize both textual and non-textual attributes of documents, we incorporated a number of objectively measurable, real-valued features selected upon predefined cri...

Metadiscourse features are those facets of a text, which make the organization of the text explicit, provide information about the writer's attitude toward the text content, and engage the reader in the interaction. This study interpreted metadiscourse markers in translated and non-translated persuasive texts. To this end, the researcher chose the translated versions of one of the leading newsp...

We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data f...

poetic analysis involves the explication of a poem by focusing on the process of semiosis in it. through semiosis linguistic meaning is transformed into stylistic meaning. an examination of semiosis brings us to look at the hypersemanticized poetic structures which are none other than the style features of a poem. since style functions in a literary text by conveying meanings other than literal...

Automatic indexing of video data, especially news videos, is in strong demand considering their contents' importance and value. Various attempts have been made to index news videos automatically in order to cope with this demand, including recent challenges that utilize accompanying textual information. However, most of these methods tend to be textual information driven, which do not thoroughl...

Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. ...

This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize ...

We propose a method of content-based multimedia retrieval of objects with visual, aural and textual properties. In our method, training examples of objects belonging to a specific semantic class are associated with their low-level visual descriptors (such as MPEG-7) and textual features such as frequencies of significant keywords. A fuzzy mapping of a semantic class in the training set to a cla...

Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house’s textual information. The combined features are fed to ...