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We propose a novel framework for overcoming the challenges in extracting causal relations from domain-specific texts. Our technique is minimally-supervised, alleviating the need for manually-annotated, expensive training data. As our main contribution, we show that open-domain corpora can be exploited as knowledge bases to overcome data sparsity issues posed by domain-specific relation extraction,...
Current research on reconflgurable manufacturing systems (RMSs) only highlights the isolated empirical study with a limited attempt to explore the modeling and design support issues surrounding this economically important class of system development problems. This paper proposes colored object-oriented Petri nets with changeable structures to shed light on how RMSs can dynamically reconfigure system...
Clustering semantically related terms is crucial for many applications such as document categorization, and word sense disambiguation. However, automatically identifying semantically similar terms is challenging. We present a novel approach for automatically determining the degree of relatedness between terms to facilitate their subsequent clustering. Using the analogy of ensemble classifiers in machine...
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