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A MAXIMUM ENTROPY chunking MODEL WITH N-FOLD TEMPLATE CORRECTION

Journal of Electronics(China) 2007年第5期24卷 690-695页

作者： Sun Guanglu Guan Yi Wang Xiaolong Ministry of Education-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction *** two types of machine learning models are *** on the analysis of the two models,then the chunking model which combines the profits of conditional probability model and rule based model is *** selection of features and rule templates in the chunking model is *** results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score:92.93%.Compared with the ME model and ME Markov model,the new chunking model achieves better performance.

关键词： chunking Maximum Entropy (ME) model Template correction Cross-validation

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Study of the Influence of the chunking on Improving Speaking Ability Among Senior High Students

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Sino-US English Teaching 2023年第11期20卷 457-464页

作者： YE Meixue University of Shanghai for Science and Technology ShanghaiChina

English speaking skill is one of the most important skills that senior high students need to obtain in learning ***,there are still many problems existing in students’speaking *** a teaching and learning strategy,chunking is now gradually used in English classroom and has received a positive ***,in this paper,the influence of chunking on improving English speaking skill among senior high school students will be investigated and analyzed through the methods of questionnaire and the follow-up interview to answer four questions:(1)What effect does chunking have on the oral fluency of high school students?(2)What effect does chunking have on the oral accuracy of high school students?(3)What effect does chunking have on the vocabulary?And(4)Does the English speaking performance relate to genders?After analyzing the results of questionnaire by the SPSS and summing up the interview record,we found that most of them agree the fact that the strategy of chunking does benefit their oral fluency,oral accuracy,and ***,female students have higher scores than male students.

关键词： the chunking memory strategy high school students oral English ability chunking

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Probability Theory Predicts That chunking into Groups of Three or Four Items Increases the Short-Term Memory Capacity

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Applied Mathematics 2014年第10期5卷 1474-1484页

作者： Motohisa Osaka Department of Basic Science Nippon Veterinary and Life Science University Tokyo Japan

Short-term memory allows individuals to recall stimuli, such as numbers or words, for several seconds to several minutes without rehearsal. Although the capacity of short-term memory is considered to be 7 ±?2 items, this can be increased through a process called chunking. For example, in Japan, 11-digit cellular phone numbers and 10-digit toll free numbers are chunked into three groups of three or four digits: 090-XXXX-XXXX and 0120-XXX-XXX, respectively. We use probability theory to predict that the most effective chunking involves groups of three or four items, such as in phone numbers. However, a 16-digit credit card number exceeds the capacity of short-term memory, even when chunked into groups of four digits, such as XXXX-XXXX-XXXX-XXXX. Based on these data, 16-digit credit card numbers should be sufficient for security purposes.

关键词： Short-Term Memory chunking Probabilistic Model Credit Card Number

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Linguistically-enriched Information Retrieval

Linguistically-enriched Information Retrieval

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第四届全国信息检索与内容安全学术会议

作者： Haoliang QI~(1,2+) Muyun YANG~1 Jianfeng GAO~3 Sheng LI~1 Guohua LEI~2 ~1 School of Computer Science and Technology,Harbin Institute of Technology,150001,Harbin,China, ~2 Department of Computer Science and Technology,Heilongiiang Institute of Technology,150050,Harbin,China ~3 Microsoft Corporation,Redmond,WA 98052,U.S.A.

There are two problems in linguistically-enriched Information Retrieval(IR),one is how to get the linguistically decencies,the other is how to integrate linguistically features in an IR *** paper presents the reasons why using shallow parsing instead of full parsing to get linguistically *** then scores of linguistically feature are assigned by Hidden Markov Model(HMM),which is widely used in language modeling approaches to *** average precision training is used to training the free parameters of the linear discriminative model,which optimizes the average precision(AP)directly using an iterative *** effectiveness of our approach has been evaluated on the task of ad hoe retrieval using 5 English and Chinese TREC test *** show that the presented method significantly outperforms the state-of-the-art language modeling approaches and the classical probabilistic retrieval model.

关键词： Information Retrieval Linguistically Features chunking Generative Feature Extraction Discriminative Learning

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“基于用法”的语言观及语法教学中的三对关系

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语言文字学 2016年第9期 1-10页

作者：武和平王晶

进入21世纪后，语言学理论及语言教学的图景都发生了深刻的变化。主流语言学理论从传统的规定主义，经过了结构主义、转换—生成语法等“基于规则”的语言理论，目前已进入以认知语言学为主要代表的“基于用法”的语言理论时代。这种从... 详细信息

进入21世纪后，语言学理论及语言教学的图景都发生了深刻的变化。主流语言学理论从传统的规定主义，经过了结构主义、转换—生成语法等“基于规则”的语言理论，目前已进入以认知语言学为主要代表的“基于用法”的语言理论时代。这种从“基于规则”到“基于用法”的范式改变正在颠覆着我们传统的语法观和语言教学观。本文从“基于用法”的语言理论视角，结合近年来国内外语言教学理论与实践，尝试重新梳理语法教学中形式和意义、知识和技能、语法和词汇这三对重要关系。文章认为，“基于用法”语言理论中意义优先的观点、语法技能的观点以及语法—词汇连续体的观点，为语法教学研究和教学实践提供了新的视角和理论资源。

关键词：基于用法语法教学意义优先语块教学 usage-based grammar teaching primacy of meaning chunking grammaring

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