Most existing researches on relation extraction focus on binary flat relations like Bomln relation between a Person and a *** a large portion of objective facts de-scribed in natural language are complex,especially in...
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Most existing researches on relation extraction focus on binary flat relations like Bomln relation between a Person and a *** a large portion of objective facts de-scribed in natural language are complex,especially in professional documents in fields such as finance and biomedicine that require precise *** example,“the GDP of the United States in 2018 grew 2.9%compared with 2017”describes a growth rate relation between two other relations about the economic index,which is beyond the expressive power of binary flat ***,we propose the nested relation extraction problem and formulate it as a directed acyclic graph(DAG)structure extraction ***,we propose a solution using the Iterative Neural Network which extracts relations layer by *** proposed solution achieves 78.98 and 97.89 FI scores on two nested relation extraction tasks,namely semantic cause-and-efFect relation extraction and formula ***,we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively,which makes the annotation difficult and ***,we extend our model to incorporate a mention-insensitive mode that only requires annotations of relations on entity concepts(instead of exact mentions)while preserving most of its *** mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.
In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is *** on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted t...
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In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is *** on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost *** the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration *** convergence is thoroughly *** results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.
Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to ...
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Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.
In this paper, we propose a method to improve localization algorithm of maximum likelihood estimation;the localization scheme relies on the distance threshold. In order to suppress effectively the effects of received ...
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ISBN:
(纸本)9781509038237;9781509038220
In this paper, we propose a method to improve localization algorithm of maximum likelihood estimation;the localization scheme relies on the distance threshold. In order to suppress effectively the effects of received signal strength error to node localization precision. This paper presents an indoor localization algorithm based on received signal strength to select anchor nodes. Compared with the traditional localization algorithm, this scenario not only improve the localization accuracy, but also reduce the calculation complexity of nodes. The simulation results show that the average error of the proposed method is less than 0.15 m. Moreover, when there are a large number of anchor nodes, the computational complexity is effectively reduced. Verification result verifies the effectiveness and reliability of the algorithm.
The traditional double-threshold endpoint detection method has the phenomenon of missing detection. Therefore, the speech recognition(SR) system based on vector quantization(VQ) in this paper proposes an improved algo...
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ISBN:
(纸本)9781509038237;9781509038220
The traditional double-threshold endpoint detection method has the phenomenon of missing detection. Therefore, the speech recognition(SR) system based on vector quantization(VQ) in this paper proposes an improved algorithm for this phenomenon, which effectively avoids the problem of missing detection. Then, Mel Frequency Cepstral Coefficients(MFCC) is used to extract the characteristic parameters of the speech signal, and the multistage vector quantization is used to quantify the characteristic parameters. Experimental results show that, the proposed algorithm improves the recognition rate of the text-independent speaker recognition system by 8.7%, and it also confirms that the longer the training speech is, the higher the recognition rate will be.
In recent years, semantic search has become one hot motivation of the semantic web. In this paper, we propose a semantic-based resource management and search architecture and its implementation in research community, ...
In the cyber-physical society, networks are constructed for information transportation. Among them, power law networks with the scale free property are extensively found in self-organized systems. The dynamicity of th...
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In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information ...
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In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.
As the Web continues to grow, the pornographic texts in varied forms run rampant on Internet, despite repeated prohibitionsm. It severely does harms to the development of people's mental health and the stability o...
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By introducing a discrete Frenet frame, this paper first proposes 3D discrete clothoid splines to extend the planar discrete clothoid splines of Schneider and Kobbelt. On the basis of 3D discrete clothoid spline curve...
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