Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a docum...
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ISBN:
(纸本)9781605581644
Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1% and 40.3% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario. Copyright 2008 ACM.
software architecture design is an important stage in the whole software development lifecycle. Yet non-functional requirements (NFRs) have been afflicted architects for a long time and how to efficiently design and a...
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With the popularity of Service-Oriented Computing, how to construct highly available service-oriented applications is becoming a hot topic in both the research and industry communities. As a fundamental problem in dyn...
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86 Gbit/s hybrid polarization multiplexed OOK/DPSK signal is generated and transmitted at the same wavelength. Clear eye diagrams of demultiplexed OOK and DPSK signals are obtained after transmission over a 106.5 km S...
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With more applications of multispectral remote sensing images, how to effectively and correctly make automated classification of multispectral images is still a great challenge. Utilizing both spatial contextual infor...
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With more applications of multispectral remote sensing images, how to effectively and correctly make automated classification of multispectral images is still a great challenge. Utilizing both spatial contextual information and spectral information can achieve better classification performance. In order to make better utilization of the spatial contextual information, we apply graph model to the multispectral image, and use graph-based segmentation to produce units of pixels for further classification. In this paper, we present an unsupervised approach for multispectral remote sensing image classification with graph-based segment and fuzzy c-means clustering. Our method mainly involves following steps: First, represent image as graph H = (V, E) based on the feature vector of per pixel and the relationships among neighboring pixels, and segment the graph into groups of sub-regions as basic object units using the effective graph segmentation algorithm. Then according to those global feature vectors of sub-regions, the fuzzy c-means clustering is used to obtain the classification map based on these sub-regions. Experiments shows the results by different segmentation scales, and then turn out that the approach proposed in this paper can achieve better accuracy and efficiency.
software architecture design is an important stage in the whole software development lifecycle. There are so many various concerns at architecture level which relate to all kinds of stakeholders and describe the all f...
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software features can be the perception of the influence of the human factors on software artifacts. This paper tends to investigate mainstream software evolution and perceive primary criterions of competition and coo...
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ISBN:
(纸本)9780769533162
software features can be the perception of the influence of the human factors on software artifacts. This paper tends to investigate mainstream software evolution and perceive primary criterions of competition and cooperation. It proposes and examines three hypotheses. The active resource space ARS is proposed to manage software features and enhance the success potential of emerging software. Our work makes possible embodying value-added semantic service for software design to meet the merging interconnection environment.
The internal link loss characteristic inference has become an increasingly important issue for operating and evaluating a wireless sensor network. Due to the inherent stringent bandwidth and energy constraints, it is ...
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This paper proposes a starriness start-up mechanism in trusted embedded system, a bus arbitration method to implement this mechanism is also designed. At last, we implement this arbitration mechanism in concrete proto...
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This paper proposes a starriness start-up mechanism in trusted embedded system, a bus arbitration method to implement this mechanism is also designed. At last, we implement this arbitration mechanism in concrete prototype system. As experiment shows, this mechanism is agile to control, easy to expand, secure and reliable.
Reusing software architecture, which is a kind of coarse-grained software resources at design time, is always a very difficult problem in the realm of software engineer. We consider that the lack of information and te...
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