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
With the warming up and continuous development of machine learning,especially deep learning,the research on visual question answering field has made significant progress,with important theoretical research significanc...With the warming up and continuous development of machine learning,especially deep learning,the research on visual question answering field has made significant progress,with important theoretical research significance and practical application value.Therefore,it is necessary to summarize the current research and provide some reference for researchers in this field.This article conducted a detailed and in-depth analysis and summarized of relevant research and typical methods of visual question answering field.First,relevant background knowledge about VQA(Visual Question Answering)was introduced.Secondly,the issues and challenges of visual question answering were discussed,and at the same time,some promising discussion on the particular methodologies was given.Thirdly,the key sub-problems affecting visual question answering were summarized and analyzed.Then,the current commonly used data sets and evaluation indicators were summarized.Next,in view of the popular algorithms and models in VQA research,comparison of the algorithms and models was summarized and listed.Finally,the future development trend and conclusion of visual question answering were prospected.展开更多
The purpose of this study was to investigate the memory effects of the postgraduates’memorizing Everyday English from 30 to 100 using the Natural Numeral Imagery Memory(Method by memorizing the concrete objects assoc...The purpose of this study was to investigate the memory effects of the postgraduates’memorizing Everyday English from 30 to 100 using the Natural Numeral Imagery Memory(Method by memorizing the concrete objects associated with the shapes of Arabic numeral to produce marvelous imagination,MMOASAPMI).The results indicated as follows:Firstly,the postgraduates,who applied the MMOASAPMI to memorize and recall the Everyday English from 30 to 100,could recite them well in sequence backward,forward,and randomly.The reaction time of reciting any sentence randomly is no more than 2 seconds.Secondly,it can transform the materials of the short-term memory into long-term memory quickly,and effectively prevent them from the interference of proactive and retroactive inhibition,so it is useful for keeping memorized information with less loss and remaining for a long period.Thirdly,with the materials in strong sequence,large quantities and the difficulty to memorize,it is an extremely effective method for memorizing them.Fourthly,the keys to improving the memory efficiency are the well-storing skills of memory,storing methods,and memory clues.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Project(61702063)supported by the National Natural Science Foundation of China。
文摘With the warming up and continuous development of machine learning,especially deep learning,the research on visual question answering field has made significant progress,with important theoretical research significance and practical application value.Therefore,it is necessary to summarize the current research and provide some reference for researchers in this field.This article conducted a detailed and in-depth analysis and summarized of relevant research and typical methods of visual question answering field.First,relevant background knowledge about VQA(Visual Question Answering)was introduced.Secondly,the issues and challenges of visual question answering were discussed,and at the same time,some promising discussion on the particular methodologies was given.Thirdly,the key sub-problems affecting visual question answering were summarized and analyzed.Then,the current commonly used data sets and evaluation indicators were summarized.Next,in view of the popular algorithms and models in VQA research,comparison of the algorithms and models was summarized and listed.Finally,the future development trend and conclusion of visual question answering were prospected.
文摘The purpose of this study was to investigate the memory effects of the postgraduates’memorizing Everyday English from 30 to 100 using the Natural Numeral Imagery Memory(Method by memorizing the concrete objects associated with the shapes of Arabic numeral to produce marvelous imagination,MMOASAPMI).The results indicated as follows:Firstly,the postgraduates,who applied the MMOASAPMI to memorize and recall the Everyday English from 30 to 100,could recite them well in sequence backward,forward,and randomly.The reaction time of reciting any sentence randomly is no more than 2 seconds.Secondly,it can transform the materials of the short-term memory into long-term memory quickly,and effectively prevent them from the interference of proactive and retroactive inhibition,so it is useful for keeping memorized information with less loss and remaining for a long period.Thirdly,with the materials in strong sequence,large quantities and the difficulty to memorize,it is an extremely effective method for memorizing them.Fourthly,the keys to improving the memory efficiency are the well-storing skills of memory,storing methods,and memory clues.
