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语用模型表征是一种语境论的知识表征观。它不仅消解了关于模型的反结构主义的质疑,而且有助于将心灵、语言和世界整合于模型的建构之中。科学家的认知能力在确认目标系统的潜在粗略结构方面发挥着重要作用,而且这种结构处于语境之中。以认知科学为基础的语用模型表征,经过逻辑的重构,使认知和逻辑在语境基底上走向协同,并表现出有力的认知推理的功能。 相似文献
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本文通过问卷调查,采用结构方程模型,对影响双语教学效果的因素进行系统分析。要提高双语教学效果,高校应特别注意双语师资队伍建设、双语教学过程管理及双语教学对象和课程选择等三个方面的工作。 相似文献
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高校双语教学对任课教师外语的“听、说、读、写、译”五项能力提出了较高要求,如何提高双语教学准备工作的效率,出色完成双语教学任务？本文结合个人双语教学的经历,提出了一套从听、读入手的学习方法,以期达到提高双语教学能力、提高双语教学准备工作效率的目的。 相似文献
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隐性知识表征直接影响隐性知识的传递与共享,隐性知识异于显性知识,其难以通过命题符号准确和完整的表征,但这无法否认其在人脑中的存在性。文章从认识论视角出发,通过个体隐性知识表征模型论证了隐性知识是人的知觉中关于事物之间联系与特定情境的整体心像。在信息论及隐性知识表征模型的基础上得出了隐性知识的传递模型,其由隐性知识源、隐性知识输出映射、隐性知识展示、镜像、信道、隐性知识感知、隐性知识输入映射和隐性知识宿八个部件构成,隐性知识传递通过这八个部件完成从隐性知识演示到隐性知识吸取的完整传递过程。隐性知识传递的目标是最小化隐性知识条件熵,最大化传递主体间的互隐性知识量。 相似文献
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网络课程是现代远程教育的重要资源,是终身教育的重要方式,网络课程是通过互联网进行传播的,网络课程内容的呈现需要网络多媒体技术的支持,和传统的面对面的课程相比有其网络环境下的特性,根据网络课程内容的特点选择合适的表征方式非常重要,本文探讨了网络课程文本、图片、课件、视频媒体等的表征特点,以及制作方式,为网络课程的内容表征设计提供了一定的参考。 相似文献
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信息技术在中学化学实验教学中是重要的教学手段,三重表征在中学化学实验教学中是重要的教学形式。因此,将信息技术的手段与三重表征的形式相结合并应用于中学化学实验教学,不仅可以使实验教学更加快捷、系统和有效,也能促进学生的学习效果。通过将中学化学实验在教材中的内容和作用不同,把实验分为揭示基本概念和化学基础理论、有关物质性质与制备、化学实验基本操作、联系生活和生产实际以及综合设计即探究性实验五大方面。对于不同类型的化学实验,使用恰当的信息手段,并从三重表征即宏观表征、微观表征和符号表征的角度对其进行实验教学,以此提高中学化学实验教学效率。 相似文献
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说课,作为新的教研形式,对于教育观念的改革、教育理论的理解和掌握,对于教学的研究、反思、评价无疑是一种可取的有效途径。由于说课的目的不同,说课可以是课前说,也可以是课后说。文章分析课后说课的特点,与课前说课区别开来,从而走出课后说课的误区。 相似文献
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表征危机与建构主义思潮的兴起拉开了研究的方法论之争的新序幕。表征与建构都为人类理解把握世界提供了重要的视角,但它们各自也都面临诸多困境和风险。知识既非对客观实在的准确再现,亦非完全社会建构的结果,而是研究实践的产物。只有将表征与建构两种理念纳入到一种知识生产实践过程中来理解,才能真正规避知识生产的内卷化难题。表征与建构遵循着"问题化—科学化—社会化—客观化"的实践逻辑推动着知识的阶跃式增长,这为实践取向的社会研究提供了一种可行的解释,也为表征与建构之争的终结开启了希望。 相似文献
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图像表征是科学表征的三种基本形式之一,在科学创新中发挥着重要作用。与语言描述、数值表示等表征形式相比,图像表征的基本特征在于：能够提供关于研究对象的大量信息,能够直接给出研究对象的多层次的抽象信息,从而为研究者提供了广阔的思维空间和开展创新思维的基础。图像表征在科学研究中具有特殊的认识论地位,是开展创新研究的重要方法论工具。其创新功能主要表现在三个方面：（1）使研究对象的多层次的抽象信息成为直接可得到的,有助于创新性思想的形成;（2）图像与其表征对象的异质同构特征为创新思维的开展指示方向;（3）图像表征可以使研究者的意向信息突显出来,从而使研究者更易于创新性地研究对象的特定属性和规律。 相似文献
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本文详细阐述知识集合的研究范围,从物元模型、知识集合中的组合表示和相关性三方面分析知识集合的表示方式,最后介绍了物元模型在科研评价中的应用。 相似文献
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Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications. 相似文献
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谭力扬 《科学技术与辩证法》2014,(4):1-6
一般来说,在科学哲学之中,对科学理论的考察都围绕着理论表达的方式、机制及其恰当性而进行,而且无论是科学理论的陈述观还是模型观都会将表达与同构关系密切地联系起来。这些考察的哲学背景都与关于科学理论的知识论、然后是科学中的形而上学问题有关——尤其是对于知识论而言,是否可能、如何论证科学知识的真理性、客观性、合理性与普适性以及如何证成如此的科学理论知识都是非常重要的问题,而且这些问题和表达及其恰当性有密切的关系。于是,与科学知识有关的怀疑论、工具主义挑战其实都来自于对基础的科学理论表达机制、被表达项的认定等产生的怀疑与挑战。可是,基于同构性的表达机制不可能完全消除对科学理论知识的怀疑论挑战。这时,科学哲学的讨论就需要超越表达并将对科学理论知识的证成引向先验的进路。 相似文献
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《Information processing & management》2023,60(5):103421
Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents. 相似文献
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《Information processing & management》2022,59(6):103103
Representation learning has recently been used to remove sensitive information from data and improve the fairness of machine learning algorithms in social applications. However, previous works that used neural networks are opaque and poorly interpretable, as it is difficult to intuitively determine the independence between representations and sensitive information. The internal correlation among data features has not been fully discussed, and it may be the key to improving the interpretability of neural networks. A novel fair representation algorithm referred to as FRC is proposed from this conjecture. It indicates how representations independent of multiple sensitive attributes can be learned by applying specific correlation constraints on representation dimensions. Specifically, dimensions of the representation and sensitive attributes are treated as statistical variables. The representation variables are divided into two parts related to and unrelated to the sensitive variables by adjusting their absolute correlation coefficient with sensitive variables. The potential impact of sensitive information on representations is concentrated in the related part. The unrelated part of the representation can be used in downstream tasks to yield fair results. FRC takes the correlation between dimensions as the key to solving the problem of fair representation. Empirical results show that our representations enhance the ability of neural networks to show fairness and achieve better fairness-accuracy tradeoffs than state-of-the-art works. 相似文献