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Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
Document summarization plays an important role in the area of natural language processing and text mining. This paper proposes several novel information-theoretic models for multi-document summarization. They consider document summarization as a transmission system and assume that the best summary should have the minimum distortion. By defining a proper distortion measure and a new representation...
This paper presents a novel application of incorporating Alternating Structure Optimization (ASO) to conduct the task of text chunking of Semantic Role Labeling (SRL) in Chinese texts. ASO is a competent linear algorithm based on the theory of multi-task learning. In this paper, by constructing several SRL tasks to constitute a multi-task, we are able to encode the inference obtained by ASO algorithm...
This paper presents a method based on feature selection to obtain sets of human activity recognizers of different complexity. Classifiers for human activity recognition are built exploring a space of candidate feature subsets, trying to maximize the accuracy of a classifier trained with them. At the same time, the size of the selected feature subset is minimized. The accuracy of a classifier tends...
The paper proposes a concept segmentation method to extract meta-knowledge from the multi-source knowledge base. We improve the traditional structure-based extracting method by using the concept hierarchical partition. The concept and concept relationship can be described with ontology model, which can discover the semantic relationship between concepts. Then a self-learning of meta-knowledge model...
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