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1.
ABSTRACT

Historic Japanese textiles from over 1000 years ago generally show marked deterioration and only very rare examples show their original forms and much information about textile reproduction has been lost. The replication of textile braids lacks systematic methodology and is still being practiced by only few individual braiding experts. The recreation of original braids as close as possible to original braids is a part of Japan’s intangible cultural heritage. The aim of this study is to clarify the decision-making procedure through which the braiding experts can decipher the original braiding structures. As a preliminary study of this project, interviews of a braid researcher and a replicating expert, Makiko Tada were performed regarding her working practices. It is important to clarify the braiding parameters for structural analysis such as the number of transits and the balance of ridges, and it became clear that the orientation of multiple colored threads plays an important role. The expert’s replicate works were also analyzed using a text-mining statistical technique to clarify the relationship of braiding parameters. The relationship between each braiding parameter and production method such as loop manipulation and Taka-dai became clear. As a result, the process of deciphering the original braid structure has been compiled in simplified workflows, which could contribute to the standardization and improvement in efficiency of replication of cultural property braids.  相似文献   
2.
[目的/意义]社交媒体是网络信息资源的重要组成部分,对国内外社交媒体存档的相关研究成果进行总结与梳理,形成比较完整的综述内容,为国内该领域研究提供参考与借鉴。[方法/过程]基于多学科视角,通过文献计量与内容分析得到社交媒体存档领域的文献分布情况,总结社交媒体存档的理论基础,从宏观层面将社交媒体存档理论与实践研究归纳为"目的与主体""政策与指南""问题与障碍"三个研究主题并展开综述。[结果/结论]国内外社交媒体存档研究具有发展速度较快、与实践结合紧密、区域发展不均衡等特征,在未来研究中需要重视研究对象的变化、管理标准的构建以及多主体间的协作。最后对国内社交媒体存档研究与实践的发展提出建议。  相似文献   
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In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.  相似文献   
5.
The purpose of this paper is to examine the utilization of the marketing adaptability of branded mobile applications (apps) in order to understand the relationship between consumers and their attachment to branded apps. We develop a model grounded in the purchaser-brand relationship theory of remarketing in order to develop the consumer-brand relationship through mediator brand experience (BE) and moderator digital footprint. A survey was conducted with 421 participants from different regions in India. AMOS 21.0 and SPSS plugin called “Process Analysis System” proposed by Hayes (2013) were used to analyze the hypotheses. The results corroborate the proposed research model. It approves brand association with brand connection for those brands that are easily identifiable. The result also confirms that the comprehensive consumption values are the major influencing factors in the adoption of branded apps. The study enhances the comprehension of the impact of brand connotation on consumer behavior in terms of the usage of various branded apps and the practical and non-useful esteem attached to them.  相似文献   
6.
ABSTRACT

This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallies. The expertise levels were determined using a typology based on a continuum of 5 conative stages: (1) structural, (2) functional, (3) technical, (4) contextual, and (5) expertise. The video allowed empirical measurement of the duration of the rallies, and tri-axial accelerometers measured the intensity of the player’s involvement. A principal component analysis showed that two dimensions explained 54.9% of the total variance in the data and that conative stage and empirical parameters during rallies (duration, intensity of the game) were correlated with axis 1, whereas duration and acceleration data between rallies were correlated with axis 2. A random forest algorithm showed that among the parameters considered, acceleration, duration of the rallies, and time between rallies could predict conative stages with a prediction accuracy above possibility.

This study suggests that performance analysis benefits from the confrontation of subjective and objective data in order to design training plans according to the expertise level of the participants.  相似文献   
7.
Abstract

This article reports the results of a quantitative and systematic analysis comparing usage rates between English-language and French-language e-monograph titles at Laurentian University, Canada. Full-text viewings were collected as usage metrics. English-language e-monographs exhibited medium strength linear relationships between collection size and usage. On the other hand, French-language e-monographs exhibited weak linear relationships between collection size and usage. The French-language e-monograph collection experienced a higher rate of full-text use, relative to size. The cost-per-viewing ratio for the English-language e-monograph collection was higher than those that calculated for the French-language collection.  相似文献   
8.
基于深度学习的中文专利自动分类方法研究   总被引:2,自引:0,他引:2  
[目的/意义] 面向当前国内专利审查和专利情报分析工作中对于海量专利分类的客观需求,设计了7种基于深度学习的专利自动分类方法,对比各种方法的分类效果,从而助力专利分类效率和效果的提升。[方法/过程] 针对传统机器学习方法存在的缺陷,基于Word2Vec、CNN、RNN、Attention机制等深度学习技术,考虑专利文本语序特征、上下文特征以及分类关键特征,设计Word2Vec+TextCNN、Word2Vec+GRU、Word2Vec+BiGRU、Word2Vec+BiGRU+TextCNN等7种深度学习模型,以中国专利为例,选取IPC主分类号的"部"作为分类依据,对比这7种模型与3种传统分类模型在中文专利分类任务中的效果。[结果/结论] 实证研究效果显示,采用考虑语序特征、上下文特征及强化关键特征的深度学习方法进行中文专利分类具有更优的分类效果。  相似文献   
9.
为深入理解图书情报学学术论文中所使用的研究方法的语义功能,为用户提供基于知识单元的细粒度知识服务,文章首先基于体裁理论来分析引文分析法、田野研究法、共词分析法、实验法、比较分析法和问卷调查法的知识单元构成。其次采用文本分析法,检索CSSCI图书情报学领域的18种期刊中使用以上6种研究方法的论文,进行知识单元层次的深度标引,作为知识库构建的语料。再次采用系统设计法,开发具有4种功能的学术论文研究方法学习系统。最后采用实验法,招募30位研究生使用系统,并根据用户体验对其可用性进行评价。结果表明:研究方法的体裁分析能较好地表示使用该方法开展研究的论文的语义功能,解释研究方法使用过程各部分的语义特征,为深入到知识单元层面的标引提供了基础,也为用户提供了基于知识单元的细粒度的检索点,知识库具有很好的可用性。文章揭示了论文研究方法使用过程各部分的语义特征,基于知识单元构成而设计的学术论文研究方法知识库能有效帮助用户学习研究方法,为学术论文研究方法内容的深度语义标引和本体开发奠定了基础,也为用户提供细粒度、多维度的论文研究方法内容的检索服务,对面向知识发现的知识组织研究具有参考意义。  相似文献   
10.
Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.  相似文献   
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