济南市县（市）区科技创新能力评价研究

从济南市打造全国重要的区域性科技创新中心和创新型城市建设的实践出发,借鉴国内外科技创新评价的 理论和方法,参照济南市县（市）区、高新区科技创新进展情况通报的指标体系,构建济南市县（市）区科技创新能力评 价指标体系。采用灰关联分析法确定指标权重,并选用2012、2013、2014 年度数据进行实证分析,对各县（市）区的科 技创新能力进行综合评价和一级指标评价。     

基于圆壳区域伸缩的空间知识表示

区域连接演算RCC(region connection calculs)是定性空间推理的重要理论之一,但由于缺乏必要的定量工具,RCC只能描述空间拓扑关系,而不能描述距离、方向等其他空间关系。在RCC理论的基础上,引入区域圆壳概念对区域伸缩加以度量,得出一种以区域为单位的形式化度量方法,来描述区域间的距离关系、方向关系,从而增强RCC的空间关系表示能力,拓展了RCC理论的适用范围。     

农村小学规模、地理位置与公用经费支出的关系--基于陕西省X县调查数据的实证分析

基于公用经费支出与学校规模之间非线性变化的理论框架,利用陕西省 X县423所农村小学的调研数据,对学校规模、地理位置与校际公用经费支出之间的关系进行了实证分析。结果表明：校际公用经费支出与学校规模之间呈现三次曲线关系,并具有良好的稳定性;与公用经费支出显著正相关的学校地理位置和教师数、寄宿生数的交互作用,对曲线形态无实质性影响,但使学校最优规模有所降低。实证结果在表明农村小学公用经费支出具有显著规模经济效应的同时,为改进公用经费的拨付模式提供了具有可操作性的参考依据。     

中美战略互信的困境与机遇

目前中美战略互信处于一种低度信任状态。增进中美战略互信,我们应该持有的预期不可能是高度信任,而只能是中度信任,因为中美战略互信存在结构困境、博弈困境和政治文化困境。但同时也应看到,由于中美关系完全失衡的状态正在发生根本性改变,中美经济关系正在朝着前所未有的深度和广度发展,中国的国际形象正处在一个最佳时期,中美之间的相互了解和交流已进入前所未有的高峰期,全球治理的挑战要求中美加强战略互信,中美战略互信进入重要机遇期。     

浅谈XML与HTML和关系数据库的比较

XML技术作为一门新兴技术,在许多领域得到广泛的支持并有着广阔的应用前景,其与HTML以及关系数据库之间的关系是值得探讨的。本文得出结论:XML并不是要替代HTML和关系数据库。HTML、关系数据库及XML将凭借各自的特点继续适用于其合适的领域。     

概率关系代数研究

基于概率关系的可能世界概念的基本性质,为了能够有效对概率关系查询进行评估,本文对概率关系的元组进行了分类,分为相互不相容的元组和相互独立的元组,提出了概率关系合并操作和四种概率关系代数运算。     

赣州市中小企业劳动关系问题研究——基于雇主的视角

本文在专家访谈和问卷调查的基础上,以赣州市中小企业雇主的劳动关系自我认知为研究视角,对赣州市中小企业劳动关系的现状进行了实证分析,得出影响赣州市中小企业劳动关系的主要因素,期望为促进赣州市中小企业劳资双方的和谐共生发展提供借鉴。     

Using semantic similarity to reduce wrong labels in distant supervision for relation extraction

Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.     

Self-training on refined clause patterns for relation extraction

Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.     

长征精神的内在逻辑关系及其时代价值

长征精神的内容在逻辑关系上是相互联系的,是一个不可分割的整体。对党、对革命无比忠诚和坚定不移的理想信念是长征精神的灵魂和核心,其它四个方面都是它的根本要求和具体体现。科学认识长征精神的逻辑关系对构建社会主义和谐社会有重大的现实意义。