Exploring the Interactions of Storylines from Informative News Events

Today＇s news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

用于文本分类的特征项权重算法改进

TF-IDF算法是文本分类中一种常用的权重计算方法,但是TF-IDF仅仅考虑了特征项在文本中出现的次数以及该特征项在训练集中的出现频率,没有考虑特征项在各个类间的分布情况及特征项的语义信息。因此针对TF-IDF的不足提出了一种改进的TF-IDF算法,此算法既考虑了特征项在类内的分布情况又考虑了特征项的位置及长度等语义因素,能更好地反映特征项的重要性。用朴素贝叶斯分类器验证其有效性,实验结果表明该算法优于TF-IDF算法,能较好地提高文本分类的准确率。     

长链羧酸铕四元配合物的合成及其发光性质

合成了铕与长链羧酸、二苯甲酰甲烷、邻菲啰啉形成的四元配合物,用元素分析、红外光谱、核磁共振氢谱和紫外光谱对所合成的铕四元配合物进行了表征。由于长链羧酸的引入,铕配合物在氯仿等挥发性有机溶剂中具有良好的溶解性,这为今后与高分子基体复合创造了条件。铕配合物在342nm光波激发下,发出以铕的特征发射谱线612nm左右为主的强荧光。     

硬约束限制的情感文本生成方法研究

预训练语言模型在情感文本的生成任务中取得了良好效果,但现有情感文本生成方法多使用软约束的方式控制文本整体的情感属性,缺乏单词和短语级别的硬性控制。为解决以上问题,提出硬约束限制下的情感文本生成方法。首先使用方面情感分析技术提取句子的方面词、情感词并判断情感极性;之后,选择目标情感的方面词和情感词作为预训练语言模型的硬约束输入来重建完整句子,其中,设计了一种新的单词权重计算方法,旨在使模型优先生成重要单词。实验结果表明,该方法生成的句子不仅具有方面级情感,在文本质量和多样性的评价指标上也有显著提高。     

多类型分类器融合的文本分类方法研究

传统的文本分类方法大多数使用单一的分类器,而不同的分类器对分类任务的侧重点不同,就使得单一的分类方法有一定的局限性,同时每个特征提取方法对特征词的考虑角度不同。针对以上问题,提出了多类型分类器融合的文本分类方法。该模型使用了word2vec、主成分分析、潜在语义索引以及TFIDF特征提取方法作为多类型分类器融合的特征提取方法。并在多类型分类器加权投票方法中忽略了类别信息的问题,提出了类别加权的分类器权重计算方法。通过实验结果表明,多类型分类器融合方法在二元语料库、多元语料库以及特定语料库上都取得了很好的性能,类别加权的分类器权重计算方法比多类型分类器融合方法在分类性能方面提高了1.19%。     

不同添加剂对SrAl2O4:(Eu,Dy)磷光体发光性能的影响

在较低的温度下用燃烧合成法快速合成了 Eu2 ,Dy3 掺杂的SrAl2O4长余辉磷光体发光材料.研究了P2O5,CaF2,H3BO3,NaF 几种添加剂对SrAl2O4:(Eu,Dy)粉体发光性能的影响.结果发现:H3BO3和P2O5的添加有利于改善磷光体的发光性能,而在配料中加入CaF2和NaF,磷光体发光效率降低.随着这几种添加剂的加入,SrAl2O4:(Eu,Dy)磷光体材料发射光谱的主发射峰不同程度的出现蓝移.根据实验结果分析了添加剂的作用机理.     

基体树脂对长玻璃纤维增强PP力学性能的影响

采用在线混合设备制备了长玻璃纤维(LGF)增强聚丙烯(PP)复合材料(LGF-PP),研究了基体性质及界面相容剂对LGF-PP力学性能的影响。基体树脂熔体流动速率的增加,使最终LGF-PP中的玻璃纤维长度从5.63 mm增至8.56 mm,提高了力学性能。与均聚PP比较,以共聚PP为基体树脂的LGF-PP冲击强度高出约10%,但其他性能略差。界面相容剂有利于增强玻璃纤维与PP的界面结合,拉伸强度和拉伸模量明显增加,但是冲击强度降低了20%～30%。     

超长、超大基坑开挖对邻近地铁结构安全影响的数值模拟分析

为了研究超长、超大基坑开挖对邻近地铁结构安全影响,以西安地铁2号线周边某基坑为工程实例,通过运用数值分析软件,结合场地的水文地质和工程地质条件,建立数值模型,进行施工过程的动态模拟,同时进行现场监测,经对比数值模拟结果及监测数据,表明运用PLAXIS软件能有效的模拟基坑开挖过程中的土体变形。同时工程采用的分段、分块开挖,中心岛顺做,周边预留土台反压、使用旋喷锚索的支护方案,对复杂条件下基坑开挖对邻近地铁结构的变形影响能起到有效的控制作用,可对类似工程的设计优化提供有益的参考及借鉴。     

End-to-end scene text recognition using tree-structured models

Detecting and recognizing text in natural images are quite challenging and have received much attention from the computer vision community in recent years. In this paper, we propose a robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models. For each category of characters, we build a part-based tree-structured model (TSM) so as to make use of the character-specific structure information as well as the local appearance information. The TSM could detect each part of the character and recognize the unique structure as well, seamlessly combining character detection and recognition together. As the TSMs could accurately detect characters from complex background, for text localization, we apply TSMs for all the characters on the coarse text detection regions to eliminate the false positives and search the possible missing characters as well. While for word recognition, we propose a normalized pictorial structure (PS) framework to deal with the bias caused by words of different lengths. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods both for text localization and word recognition.