The Experts below are selected from a list of 10194 Experts worldwide ranked by ideXlab platform
H Zhang - One of the best experts on this subject based on the ideXlab platform.
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Atomic Event extraction based on information unit fusion
Journal of Wuhan University, 2015Co-Authors: H ZhangAbstract:Atomic Event extraction is an important means to represent the unstructured text structurally. This paper proposes an information basic unit fusion approach to extract Atomic Event from the news. On the basis of Chinese word segmentation,part of speech tagging and named entity recognition,information basic units can be marked and fused according to linguistic rules. And then,the Atomic Events can be extracted from information basic unit fused texts by the Atomic Event extraction algorithm. This approach does not restrict the length of texts and the types of Atomic Events. The experiment results demonstrate the effectiveness and feasibility of the Atomic Event extraction approach based on information unit fusion.
Donghong Ji - One of the best experts on this subject based on the ideXlab platform.
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Open Domain Atomic Event Extraction via Double Propagation for Chinese Text
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016Co-Authors: Donghong JiAbstract:Recent studies show structured Atomic Event information is beneficial to represent the discourse semantic. However, extracting useful structured representation of Events from open domain is a challenging problem. On one hand, previous Event extraction methods on special domain, cannot be directly used for open domain because of domain limitation and predefined Event pattern. On the other hand, Atomic Event extraction is simply regarded as a preprocessing step in previous related work, and few studies focus on Atomic Event extraction in open domain. In this paper, we propose an unsupervised method for Chinese Event extraction in open domain. Being directed against the ellipsis and flexible sentence structure in Chinese text, the proposed method exploits double propagation (DP) to combine Event extraction and Event pattern generation, which does not require seed Events or seed Event patterns and is able to eliminate noise from syntactic parsing. Experimental results on standard benchmark show that our proposed method outperforms state-of-the-art algorithm.
L I Guoxiang - One of the best experts on this subject based on the ideXlab platform.
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abnormal Event detection in crowded scenes via bag of Atomic Events based topic model
Turkish Journal of Electrical Engineering and Computer Sciences, 2016Co-Authors: H U Xing, H U Shiqiang, Lingkun Luo, L I GuoxiangAbstract:In this paper, we propose a novel framework for abnormal Event detection in crowded scenes. A new concept of Atomic Event is introduced into this framework, which is the basic component of video Events. Different from previous bag-of-words (BoW) modeling-based methods that represent feature descriptors using only one code word, a feature descriptor is represented using a few more Atomic Events in bag-of-Atomic-ev (BoAE) modeling. Consequently, the approximation error is reduced by using the obtained BoAE representation. In the context of abnormal Event detection, BoAE representation is more suitable to describe abnormal Events than BoW representation, because the abnormal Event may not correspond to any code word in BoW modeling. Fast latent Dirichlet allocation is adopted to learn a model of normal Events, as well as classify the testing Event with low likelihood under the learned model. Our proposed framework is robust, computationally efficient, and highly accurate. We validate these advantages by conducting extensive experiments on several challenging datasets. Qualitative and quantitative results show the promising performance compared with other state-of-the-art methods.
Tao Huang - One of the best experts on this subject based on the ideXlab platform.
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detecting Atomicity violations for Event driven node js applications
International Conference on Software Engineering, 2019Co-Authors: Xiaoning Chang, Wensheng Dou, Yu Gao, Jie Wang, Jun Wei, Tao HuangAbstract:Node.js has been widely-used as an Event-driven server-side architecture. To improve performance, a task in a Node.js application is usually divided into a group of Events, which are non-deterministically scheduled by Node.js. Developers may assume that the group of Events (named Atomic Event group) should be Atomically processed, without interruption. However, the Atomicity of an Atomic Event group is not guaranteed by Node.js, and thus other Events may interrupt the execution of the Atomic Event group, break down the Atomicity and cause unexpected results. Existing approaches mainly focus on Event race among two Events, and cannot detect high-level Atomicity violations among a group of Events. In this paper, we propose NodeAV, which can predictively detect Atomicity violations in Node.js applications based on an execution trace. Based on happens-before relations among Events in an execution trace, we automatically identify a pair of Events that should be Atomically processed, and use predefined Atomicity violation patterns to detect Atomicity violations. We have evaluated NodeAV on real-world Node.js applications. The experimental results show that NodeAV can effectively detect Atomicity violations in these Node.js applications.
H U Xing - One of the best experts on this subject based on the ideXlab platform.
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abnormal Event detection in crowded scenes via bag of Atomic Events based topic model
Turkish Journal of Electrical Engineering and Computer Sciences, 2016Co-Authors: H U Xing, H U Shiqiang, Lingkun Luo, L I GuoxiangAbstract:In this paper, we propose a novel framework for abnormal Event detection in crowded scenes. A new concept of Atomic Event is introduced into this framework, which is the basic component of video Events. Different from previous bag-of-words (BoW) modeling-based methods that represent feature descriptors using only one code word, a feature descriptor is represented using a few more Atomic Events in bag-of-Atomic-ev (BoAE) modeling. Consequently, the approximation error is reduced by using the obtained BoAE representation. In the context of abnormal Event detection, BoAE representation is more suitable to describe abnormal Events than BoW representation, because the abnormal Event may not correspond to any code word in BoW modeling. Fast latent Dirichlet allocation is adopted to learn a model of normal Events, as well as classify the testing Event with low likelihood under the learned model. Our proposed framework is robust, computationally efficient, and highly accurate. We validate these advantages by conducting extensive experiments on several challenging datasets. Qualitative and quantitative results show the promising performance compared with other state-of-the-art methods.