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教学过程中产生的群聊文本往往包含着学生对于课程的思考。通过提取分析群聊文本中学生的提问,能够了解学生的学习情况,也能结合具体内容对学生进行指导。该研究旨在通过构建问句抽取模型,对群聊文本中与课程相关的提问进行提取。实验首先针对教学过程中产生的群聊文本进行收集,并结合课程相关教材进行数据清洗工作;然后针对类别分布不均匀问题,在Text-CNN模型的基础上提出了两种优化方式:引入注意力机制和使用平衡交叉熵损失函数。实验结果表明,优化后的模型能够达到91.95%的正确率,比原有模型增加了1.04%,而问句的F1-score表现为0.72,在原有模型的基础上提高了0.06。该模型能够运用到实际教学中,将群聊文本中与教学相关的学生提问抽取出来,再与线下教学相结合,提高教师的分析效率,进一步改善教学效果。 相似文献
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皇甫国庆 《渭南师范学院学报》2002,17(2):13-15
通过对Fourier光学中常见的具有有限非零区间的分段连续函数的卷积运算的讨论，得出了确定卷积函数转折点坐标的原则，并用框图表示出确定转折点坐标的方法。 相似文献
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本文在CCS环境下编写（3,2,2）卷积编解码代码,以TMS320VC5510DSP为硬件仿真平台,对（3,2,2）卷积编解码进行了半实物仿真。仿真结果表明,（3,2,2）卷积编解码译码正确可行,能有效解码数据。 相似文献
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卷积公式有离散型随机变量和连续性随机变量两种形式.文章介绍了卷积公式的另外一种证法,并用其证明了一些分布的可加性,尤其强调了可加性中随机变量相互独立的条件必不可少。 相似文献
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Shallow semantic parsing assigns a simple structure (such as WHO did WHAT to WHOM, WHEN, WHERE, WHY, and HOW) to each predicate in a sentence. It plays a critical role in event-based information extraction and thus is important for deep information processing and management. This paper proposes a tree kernel method for a particular shallow semantic parsing task, called semantic role labeling (SRL), with an enriched parse tree structure. First, a new tree kernel is presented to effectively capture the inherent structured knowledge in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness via considering ancestral information of substructures and approximate matching via allowing insertion/deletion/substitution of tree nodes in the substructures. Second, an enriched parse tree structure is proposed to both well preserve the necessary structured information and effectively avoid noise by differentiating various portions of the parse tree structure. Evaluation on the CoNLL’2005 shared task shows that both the new tree kernel and the enriched parse tree structure contribute much in SRL and our tree kernel method significantly outperforms the state-of-the-art tree kernel methods. Moreover, our tree kernel method is proven rather complementary to the state-of-the-art feature-based methods in that it can better capture structural parse tree information. 相似文献
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本文在CCS环境下编写（3,2,2）卷积编解码代码,以TMS320VC5510DSP为硬件仿真平台,对（3,2,2）卷积编解码进行了半实物仿真。仿真结果表明,（3,2,2）卷积编解码译码正确可行,能有效解码数据。 相似文献
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Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction. 相似文献
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In this paper, we study the properties of Sumudu transform and relationship between Laplace and Sumudu transforms. Further, we also provide an example of the double Sumudu transform in order to solve the wave equation in one dimension which is having singularity at initial conditions. 相似文献
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