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For action recognition, traditional multitask learning can share low-level features among actions effectively, but it neglects high-level semantic relationships between latent visual attributes and actions. Some action classes might be related, where latent visual attributes across categories are shared among them. In this paper, we improve multitask learning model using attribute-actions relationship...
Using neural networks to train high quality distributed representations of words and multi-relational data has attracted a great attention in recent years. Mapping the words and their relations to low-dimensional continues vector spaces has proved to be useful in natural language processing and information extraction tasks. In this paper, we present a neural network based model that can train word...
Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents...
Latent Semantic Indexing is a widely used text mining technology nowadays due its effectiveness in dealing with the problems of synonymy and polysemy within a proper matrix scale. However LSI is enormously computationally intensive especially for processing large scale data. And effective solution is to increase the computational power available to LSI using multiple computing nodes. In this paper...
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