Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities – This paper presents a new method of finding annotated sentences based on semantic labels for word pairs. Our approach consists of two parts: (1) a method for detecting when two sentences are alike by means of lexicon-based annotations of the same sentence pairs, and (2) a method to discover the semantic tag of the annotations for the two sentences that contains the same semantic tag. The tag of annotated sentences in some sentences can be inferred by means of lexicon-based annotations of the two sentences. Our first approach consists of two stages: (1) a method for identifying the semantic tag that contains the same semantic tag. (2) an algorithm that identifies the semantic tag for each sentence where the tags of the two sentences can be identified by means of lexicon and tag system. Using these two stages our method detects and aggregates annotated sentences for several sentences of different types including short sentences. Furthermore, we present a new method that automatically identifies and aggregates sentences for different kinds of sentences by means of lexicon-based annotations for each type of sentences.

Learning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.

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Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities

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A note on the lack of convergence for the generalized median classifierLearning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.