共查询到20条相似文献，搜索用时 218 毫秒
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科技论文连锯成段要求单一,完整,组成段落的句子之间要有紧密联系,它们围绕一个中心,完整地表达一个意思,组成段落的一群句子按基本功能可划分为中心句,支撑句,连句成段要做到上下连贯,条理清楚,语气顺畅,如果不注意就会出语病,造成上下脱节,条理不清,语气不顺,科技论文还大量运用图,表,数理公式等人工语言,所以要特别重视自然语言与人工语言的和谐组合。 相似文献
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[目的/意义]学科研究热点的发掘有助于了解其发展动向,把握研究方向。热点漂移程度旨在用数据关系描述某一学科某一时间段内的研究热点在接下来一段时期内的变化程度,揭示其热点研究的持续性和规律性。[方法/过程]通过基于学术论文关键词词频统计的文献计量方法,初步构建了研究热点漂移程度计算模型,并通过对"高等教育理论""艺术理论""史学史""自动控制理论""外科护理学"和"建筑经济学"6个学科的实证分析,计算出了这些学科2010年研究热点5年漂移程度。[结果/结论]研究表明,"建筑经济学""史学史"两个学科漂移程度较高,"自动控制理论"和"艺术理论"两个学科居中,"高等教育理论"和"外科护理学"两个学科较低。研究结果具有显著的差异性。热点漂移程度揭示了各学科研究热点的变化程度和关键词词频的分布规律,并在一定程度上体现出了学科的交叉性。 相似文献
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通过模糊聚类方法可以对软件需求进行类别划分;通过灰色关联方法,可以对每个类别内的需求进行重要程度排序.这种分析方法可以为需求管理提供一个科学的解决途径. 相似文献
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小学语文阅读教学要侧重于培养学生的感悟能力,促进学生对文本的多角度阅读,提高学生解读文本、走进文本、理解文意、情感共鸣的能力。感悟能力的提高能促进小学生在阅读过程中快速理解文本,提高学生解题的速度与质量。除了传统的多读、教师讲解引导等方法之外,感悟能力的培养还有主题解读与段落划分相结合、加强对中心句段的解读、学会“窥斑见豹”和尝试“内在表达”四种有效策略。 相似文献
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本文简要介绍CRTSII型板式无砟轨道底座板段落划分,总结了一些经验和教训,对今后类似工程有一定的借鉴作用。 相似文献
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牡丹江至绥芬河扩能改造铁路工程二标施工范围内的东胜隧道,全长为3085米,位于山岭重丘区,属长大铁路隧道,该隧道地质复杂,经常有段落风化夹层,施工中很难控制,会出现部分段落塌方,本文以东胜隧道花岗岩风化深槽段12m长塌方处理为例,介绍了大管棚法处理大塌方的方法,大管棚法在处理花岗岩风化深槽中隧道塌方是行之有效的。 相似文献
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In this paper, the task of text segmentation is approached from a topic modeling perspective. We investigate the use of two unsupervised topic models, latent Dirichlet allocation (LDA) and multinomial mixture (MM), to segment a text into semantically coherent parts. The proposed topic model based approaches consistently outperform a standard baseline method on several datasets. A major benefit of the proposed LDA based approach is that along with the segment boundaries, it outputs the topic distribution associated with each segment. This information is of potential use in applications such as segment retrieval and discourse analysis. However, the proposed approaches, especially the LDA based method, have high computational requirements. Based on an analysis of the dynamic programming (DP) algorithm typically used for segmentation, we suggest a modification to DP that dramatically speeds up the process with no loss in performance. The proposed modification to the DP algorithm is not specific to the topic models only; it is applicable to all the algorithms that use DP for the task of text segmentation. 相似文献
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【目的/意义】舆情主题识别一直是舆情领域的研究热点,如今已有丰富的研究成果。现有研究对舆情信息
进行表征时多采用了传统的词袋模型、主题模型或词向量模型,只能对词语进行唯一的向量表征且传统模型需对
文本分词,可能会因分词错误、数据稀疏、出现集外词等情况影响识别效果。【方法/过程】本文构建了一种基于多采
样双向编码表示的网络舆情主题识别模型,在训练前无需对文本进行分词,针对文本过长的情况采用头尾结合的
方式进行截断,从字、段、位置三个维度提取特征嵌入,通过自注意力机制进行舆情表征,在训练过程中使用区分性
微调和多采样dropout的方法增强泛化能力,提升识别效果。【结果/结论】实验结果表明构建模型在舆情主题分类任
务中表现良好,可以在不对文本分词的情况下实现对舆情主题的准确识别。【创新/局限】创新之处在于构建了一种
新型的网络主题识别模型,局限之处在于算法复杂,如何进一步调参优化是接下来的研究重点。 相似文献
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This paper describes, evaluates and compares the use of Latent Dirichlet allocation (LDA) as an approach to authorship attribution. Based on this generative probabilistic topic model, we can model each document as a mixture of topic distributions with each topic specifying a distribution over words. Based on author profiles (aggregation of all texts written by the same writer) we suggest computing the distance with a disputed text to determine its possible writer. This distance is based on the difference between the two topic distributions. To evaluate different attribution schemes, we carried out an experiment based on 5408 newspaper articles (Glasgow Herald) written by 20 distinct authors. To complement this experiment, we used 4326 articles extracted from the Italian newspaper La Stampa and written by 20 journalists. This research demonstrates that the LDA-based classification scheme tends to outperform the Delta rule, and the χ2 distance, two classical approaches in authorship attribution based on a restricted number of terms. Compared to the Kullback–Leibler divergence, the LDA-based scheme can provide better effectiveness when considering a larger number of terms. 相似文献
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提出了基于小波变换和隐马尔可夫模型的人像鉴别算法. 该算法首先对图像进行3级小波分解，然后把3个不同分辨率的低频子图像由小到大排列成树状结构，形成低频小波树. 接着利用独立元分析对每个小波树枝进行去相关、降维，形成特征小波树枝，并把它作为观测向量对隐马尔可夫模型进行训练，把优化的模型参数用于人脸识别. 分析了观测向量维数与识别率的关系，以及状态个数和高斯概率混合成分的个数对识别率的影响，定性描述了隐马尔可夫模型的本质. 在ORL人脸数据库上，同其他四种相关方法进行了比较，实验结果表明，该方法识别率较高，工程上易于应用. 相似文献
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[目的/意义]构建数学模型研究网络话题传播规律建模和预测问题，为深入研究网络话题传播规律提供理论参考。[方法/过程]定性分析网络话题传播的大数据特征、可量化性、可度量周期性、可预测性等特征，通过分析网络话题传播机理，构建网络话题传播规律的常态模型和衍生模型，提出划分网络话题传播阶段的一般方法，并据此研究网络话题传播趋势预测方法。[结论/结果]通过"雾霾"微博话题数据开展实证分析，能够较好地划分传播阶段并预测传播趋势，由此，根据本文构建的数学模型研究网络话题传播规律是可行的。 相似文献
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《Information processing & management》2019,56(3):939-954
Recently, the high popularity of social networks accelerates the development of item recommendation. Integrating the influence diffusion of social networks in recommendation systems is a challenging task since topic distribution over users and items is latent and user topic interest may change over time. In this paper, we propose a dynamic generative model for item recommendation which captures the potential influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of topic interest and topic popularity over communities in social networks. A collapsed Gibbs sampling algorithm is proposed to train the model, and an improved diversification algorithm is proposed to obtain item diversified recommendation list. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show the superiority of our method compared with state-of-the-art diversified recommendation methods. 相似文献
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一种基于主题和分众分类的信息检索优化方法 总被引：1，自引：0，他引：1
本文针对目前搜索引擎存在的检索结果缺乏组织导致检准率不高的问题,提出一种基于主题和分众分类的信息检索优化方法.首先对用户检索主题进行获取和表达,然后以社会标签为聚类项,采用向量空间模型实现基于分众分类的文档主题聚类,并将检索结果按相似度和标签"受欢迎度"复合排序,达到提高检索准确率和优化检索的效果. 相似文献
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《Information processing & management》2003,39(4):521-541
Addressed here is the issue of ‘topic analysis’ which is used to determine a text’s topic structure, a representation indicating what topics are included in a text and how those topics change within the text. Topic analysis consists of two main tasks: topic identification and text segmentation. While topic analysis would be extremely useful in a variety of text processing applications, no previous study has so far sufficiently addressed it. A statistical learning approach to the issue is proposed in this paper. More specifically, topics here are represented by means of word clusters, and a finite mixture model, referred to as a stochastic topic model (STM), is employed to represent a word distribution within a text. In topic analysis, a given text is segmented by detecting significant differences between STMs, and topics are identified by means of estimation of STMs. Experimental results indicate that the proposed method significantly outperforms methods that combine existing techniques. 相似文献