The hierarchical phrase-based (HPB) translation exploits the power of grammar to perform long distance reorderings, without specifying nonterminal orientations against adjacent blocks or considering the lexical inform...
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Contextual question answering （CQA）, in which user information needs are satisfied through an interactive question answering （QA） dialog, has recently attracted more research attention. One challenge is to fuse co...
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Contextual question answering （CQA）, in which user information needs are satisfied through an interactive question answering （QA） dialog, has recently attracted more research attention. One challenge is to fuse contextual information into the understanding process of relevant questions. In this paper, a discourse structure is proposed to maintain semantic information, and approaches for recognition of relevancy type and fusion of contextual information according to relevancy type are proposed. The system is evaluated on real contextual QA data. The results show that better performance is achieved than a baseline system and almost the same performance as when these contextual phenomena are resolved manually. A detailed evaluation analysis is presented.
In order to probe the secret of our brain, it is necessary to design large scale dynamical neural circuits( more than 106 neurons) to simulate complex process of our brain. But such kind task is not easy to achieve on...
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Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functio...
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General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open...
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General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.
Cloud computing focuses on supporting high scalable and high available parallel and distributed computing, based on the infrastructure built on top of large scale clusters which contain a large number of cheap PC serv...
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Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on dependency structure based models typically resort to insertion operations ...
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Multi-path propagation models of electromagnetic waves in tunnels are very important in resisting fading and enhancing communication quality. The differences between the wave-guide mode matching theory and ray tracing...
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We introduce synchronous tree adjoining grammars (TAG) into tree-to-string translation, which converts a source tree to a target string. Without reconstructing TAG derivations explicitly, our rule extraction algorithm...
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