An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

When danger lurks in the background: Attentional capture by animal fear-relevant distractors is specific and selectively enhanced by animal fear.

Across 2 experiments, a new experimental procedure was used to investigate attentional capture by animal fear-relevant stimuli. In Experiment 1 (N = 34), unselected participants were slower to detect a neutral target animal in the presence of a spider than a cockroach distractor and in the presence of a snake than a large lizard distractor. This result confirms that phylogenetically fear-relevant animals capture attention specifically and to a larger extent than do non-fear-relevant animals. In Experiment 2 (N = 86), detection of a neutral target animal was slowed more in the presence of a feared fear-relevant distractor (e.g., a snake for snake-fearful participants) than in presence of a not-feared fear-relevant distractor (e.g., a spider for snake-fearful participants). These results indicate preferential attentional capture that is specific to phylogenetically fear-relevant stimuli and is selectively enhanced in individuals who fear these animals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

折半查找算法在电子会议系统调试中的应用

关联性故障代表单元的查找定位是电子会议系统调试的关键环节。为了提高查找定位效率,提出了一种基于折半查找的快速定位算法。通过对电子会议系统中干线上的代表单元可靠度的分析,把代表单元的故障情况转化成随着代表单元数量动态变化的量化数据,构成一组有序的可靠度数据,将工程调试问题转化成数据分析问题,引入折半查找算法,设计高效准确定位故障代表单元的算法,并进行了算法效率分析。该方法操作简单,无需借助特殊工具即可应用于复杂环境下电子会议系统的现场调试。     

基于相关向量机的MEMS陀螺仪随机漂移补偿

提出了一种基于相关向量机的MEMS陀螺仪随机漂移预测方法。针对MEMS陀螺仪随机漂移误差的非线性、不确定性等特点,建立了相关向量机预测模型,并采用EM算法获得模型的参数。针对随机漂移的混沌特性,利用相空间重构技术,将重构后的漂移序列作为输入变量进行模型的训练和预测。训练和测试结果均表明,该方法具有很好的预测效果,优于常用的时间序列分析法和支持向量机法。利用预测结果对随机漂移进行补偿,有效地提高了陀螺的使用精度。     

基于多尺度相关向量机的城市日用水量预测

为提高日用水量预测精度,提出一种基于多尺度相关向量机的预测模型。通过静态小波分解将用水量非平稳时间序列分解为不同尺度的平稳时间序列,然后在分解后的各子序列分别建立相关向量机回归模型进行预测,最后通过小波逆变换将各子序列预测结果整合得出原始用水量时间序列的预测值。在实例分析中分别利用多尺度关联向量机模型和单尺度相关向量机预测模型对实际用水量进行预测分析。结果表明,前者具有更高的预测精度,可应用于城市日用水量的预测。     

基于相关向量机的高光谱图像解混方法

针对传统的高光谱数据解混方法中存在的解混精度不高、丰度图模糊的缺陷,提出一种基于相关向量机的高光谱图像解混方法(unmixing algorithm based on relevance vector machine,UARVM)。其核心思想是采用改进的一对余型的相关向量机将多分类问题转化为多个二分类的问题,且求取出每个样本所对应的归属类别的概率值,即丰度值来完成图像的解混。理论研究和仿真结果表明:相对于传统解混方法,UARVM解混精度高,丰度分布图效果好。     

以关联理论为视角看话语标记语“So”的语用功能——以The Kite Runner为例

话语标记语是会话中常见语言现象之一,它对话语信息具有制约性。话语标记语作为实现话语连贯的重要衔接手段,在交际过程中,具有制约、提示、话语组织等功能。它能够帮助读者/听话人用最小的努力达到最佳的关联效果。随着语言学的不断发展,话语标记语成为了语言学研究中发展迅速的一个领域。文章从关联理论的角度对话语标记语"So"的语用功能进行研究。     

基于知网的矛盾问题语义二义性研究

提出了利用知网的相关知识,结合分词、词语相关度计算的方案解决可拓策略生成系统对矛盾问题的界定与建模时的语义二义性的问题,尝试提高计算机语言表达用户信息的准确度和可拓策略生成系统解决矛盾问题的成功率.通过实验表明,知网与可拓策略生成系统的综合应用,可提高可拓策略生成系统解决矛盾问题的准确性和效率.     

长江三角洲地区传统饮食文化资源的赋存及关联研究

传统饮食文化资源是区域文化资源的重要构成部分,是地区历史文化和人文风情的具体体现。将传统饮食文化资源解构为饮食类非物质文化遗产和中华老字号两个部分,分析长三角地区传统文化资源的赋存及空间关联情况。研究表明,长三角地区传统饮食文化资源的赋存具有明显的空间分异特点,文化资源在类型上存在诸多共性之处,体现了吴越文化的特点。文化资源的相互关联呈现出地区和类型两方面的差异,需要构建不同的传统饮食文化资源保护传承和协同开发的路径和模式。     

不同实验装置测定粉尘湿润剂的湿润效果相关性

采用滴液法、正向渗透法和反渗湿润法分别研究了3种阴离子型湿润剂对10种硫化矿粉尘的湿润效果,比较了不同实验装置研究湿润剂湿润粉尘的相关性.结果表明:温度约20℃时,使用上述3种实验方法对相同的试剂和粉尘做湿润实验没有相关性,许多情况下还出现完全相反的结果;而对于相同的实验装置,3种湿润剂都具有较好的相关性.由此可以看出,实验室研究湿润剂所采用的方法在原理上要与应用现场的特征相适应,否则就将适得其反.