Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac...Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.展开更多
We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language pr...We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language processing, we introduced a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyzed the global structural information of behaviors using their local action statistics. 2） The natural grouping of behavior patterns was discovered through a novel clustering algorithm. 3 ） A run-time accumulative anomaly measure was introduced to detect abnormal behavior, whereas normal behavior patterns were recognized when sufficient visual evidence had become available based on an online Likelihood Ratio Test （LRT） method. This ensured robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrated the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.展开更多
In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connec...In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.展开更多
In this paper, the authors are presenting the approach to extract the multiword expression （MWEs） from monolingual corpora. It both validates and generates multiword candidates. The multiword expression provides a l...In this paper, the authors are presenting the approach to extract the multiword expression （MWEs） from monolingual corpora. It both validates and generates multiword candidates. The multiword expression provides a list of candidates which are extracted and filtered according to the number of criteria and a set of standard statistical association measures. The generation of the multiword candidates is based on the surface forms, while the validation consists of series of criteria for removing noise using language independent association measures. For generating corpus count, it provides both a corpus indexation facility. Also, this approach allows easy integration with a machine learning tool for thecreation and application of supervised multiword extraction models if annotated data is available. The authors present the use of multiword in a standard configuration, for extracting MWEs from a corpus of general purpose English.展开更多
Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teac...Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teaching progress. In this paper, we focus on the diffi culty discrimination of interpretation teaching materials on the basis of analytic hierarchy process and natural language processing. We analyze several factors which affect interpretation teaching materials, and we introduce theories of analytic hierarchy process and natural language processing which is intuitive and credible operation basis.展开更多
With the current tightening of environmental regulations of waste water treatment, there is a need for the enhancement of the treatment efficiency. This can be done through process changes, or by adding a finishing tr...With the current tightening of environmental regulations of waste water treatment, there is a need for the enhancement of the treatment efficiency. This can be done through process changes, or by adding a finishing treatment after the process, like flotation or filtration. Wetlands are also one possibility for the finishing treatment of waste water. In Finland Wetlands have been used as a polishing treatment for municipal wastewater for approx. 20 years. Most of these are natural wetlands. Using wetlands after efficient wastewater treatment is an economical way of producing high quality treated water and cutting down the pollution load of water bodies. Wetlands have also been used in the control of diffuse pollution in agriculture and forestry.展开更多
基金Supported by the National Natural Science Foundation of China (No.60435020).
文摘Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.
基金This work is supported by National Natural Science Foundation of China （NSFC） under Grant No. 60573139 andNational Science ＆ Technology Pillar Program of China under Grant NO. 2008BAH221303.
文摘We presented a novel framework for automatic behavior clustering and unsupervised anomaly detection in a large video set. The framework consisted of the following key components： 1 ） Drawing from natural language processing, we introduced a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyzed the global structural information of behaviors using their local action statistics. 2） The natural grouping of behavior patterns was discovered through a novel clustering algorithm. 3 ） A run-time accumulative anomaly measure was introduced to detect abnormal behavior, whereas normal behavior patterns were recognized when sufficient visual evidence had become available based on an online Likelihood Ratio Test （LRT） method. This ensured robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrated the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.
文摘In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.
文摘In this paper, the authors are presenting the approach to extract the multiword expression （MWEs） from monolingual corpora. It both validates and generates multiword candidates. The multiword expression provides a list of candidates which are extracted and filtered according to the number of criteria and a set of standard statistical association measures. The generation of the multiword candidates is based on the surface forms, while the validation consists of series of criteria for removing noise using language independent association measures. For generating corpus count, it provides both a corpus indexation facility. Also, this approach allows easy integration with a machine learning tool for thecreation and application of supervised multiword extraction models if annotated data is available. The authors present the use of multiword in a standard configuration, for extracting MWEs from a corpus of general purpose English.
文摘Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teaching progress. In this paper, we focus on the diffi culty discrimination of interpretation teaching materials on the basis of analytic hierarchy process and natural language processing. We analyze several factors which affect interpretation teaching materials, and we introduce theories of analytic hierarchy process and natural language processing which is intuitive and credible operation basis.
文摘With the current tightening of environmental regulations of waste water treatment, there is a need for the enhancement of the treatment efficiency. This can be done through process changes, or by adding a finishing treatment after the process, like flotation or filtration. Wetlands are also one possibility for the finishing treatment of waste water. In Finland Wetlands have been used as a polishing treatment for municipal wastewater for approx. 20 years. Most of these are natural wetlands. Using wetlands after efficient wastewater treatment is an economical way of producing high quality treated water and cutting down the pollution load of water bodies. Wetlands have also been used in the control of diffuse pollution in agriculture and forestry.
