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The recent spread of Linked Open Data (LOD) fueled the research in the area of Recommender Systems, since the (semantic) data points available in the LOD cloud can be exploited to improve the performance of recommendation algorithms by enriching item representations with new and relevant features. In this article we investigate the impact of the features gathered from the LOD cloud on a hybrid recommendation...
Face annotation presents a challenging crisis in the field of image analysis and computer vision. Face annotation techniques are used for adding name to a facial image. It is also acknowledged a great deal of many applications in various fields. This paper mainly focused on the survey of face annotation techniques related to Content, Retrieval, Search, Cluster and Caption based methods. These methods...
The classifier-noun construction in Mandarin is different from the numeral-classifier-noun construction in the aspects of syntactic distribution and semantic interpretation. This paper analyzes the syntactic classifier-noun construction in the perspective of Generative Grammar. Under the discussion of the morphosyntactic property of Chinese classifiers, the paper argues that in the classifier-noun...
There is a need to analyze sentiments of people with the advancement of social media like social networks, blogs, and presentations. Sentiment analysis is a very challenging task as there is a huge amount of data present online. It is the analysis of sentiments on a particular entity in terms of positive, negative or neutral polarity. The challenge is the identification of the meaning of the word...
Supervised learning methods rely on large sets of labeled training examples. However, large training sets are rare and making them is expensive. In this research, Latent Semantic Indexing Subspace Signature Model (LSISSM) is applied to labeling for active learning of unstructured text. Based on Singular Value Decomposition (SVD), LSISSM represents terms and documents as semantic signatures by the...
One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, support vector machines, linear discriminant analysis, neural networks or k-nearest neighbors, which classify emotions using several features obtained...
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