主动时态数据库中事件的时态语义研究

在事件时态一致性的前提下详细讨论了主动时态数据库中事件的时态问题,给出了复合时态事件的时态关系以及任意两个事件之间的Allen关系.     

一类周期时态关联规则的知识发现问题

探讨了一类基于某种时态约束的周期时态关联规则的有关问题．首先，引入了时态型的定义以及相关的概念．然后，给出了一种时态事件模型，用以描述基于时态型的不同属性的各种状态的事件，同时定义了一类事件问的周期时态关联规则，它能在诸如股票波动、天气预报、商品销售等领域提供短期的预测和决策．最后，给出了发现此类周期时态关联规则算法的主要步骤，并且给出了对股票数据挖掘的实验结果分析．     

基于TimeDB的时态数据库应用研究

时间是信息的重要属性。信息技术的深入发展,对时态信息处理的要求越来越迫切。时态数据库记录时态数据并反映时态信息。文章论述了时态数据库的发展历程,介绍了TimeDB的工作原理,并结合TimeDB实现职员工资管理系统,最后论述了TimeDB的不足。     

关系型数据库中加入时态数据的实现

时态数据库主要用于记录那些随着时间而变化的历史值,这些历史值对应用领域而言是非常重要的。在当前时态数据库技术尚未完全成熟的现状下,通过对传统关系型数据库元组版本化的方法,可以在关系数据库中加入时间,从而实现时态数据库的应用。     

Now相关概率不确定时态数据模型

时间是自然界无所不在的客观属性,随着数据库技术的发展,时态信息在数据库系统中的作用日益重要。本文描述了有效时间与不确定性等概念,分析了概率不确定时态数据模型,引入时态变量,提出一种新的时态信息模型,并简单讨论了在此数据模型上的时态关系演算。     

政治辩论中时态的人际意义

在政治辩论中时态和人际功能的关系具有非常的意义,时态的选择及运用的频率的高低与发话者的人际目的相关,它们反映的不仅仅是对所叙述的事件的描述,更是发话者当时当地对所述事件的看法和态度,即情态。     

时态序关系不确定性度量

事物的有效时间由于各种原因具有不确定性,如何表示和度量这种时态不确定性是时态数据库研究的重点。提出使用分段方式近似地存储概率分布函数,并在此存储结构上研究了时态序关系BeforeI操作,最终给出了高效的线性算法。     

基于3S技术的土地利用变更调查

以长沙市天心区为本研究试验区，获取了研究区2002年、2004年和2006年三期QuickBird卫星图像，并以2002年的QuickBird卫星图像为基础．提取土地利用现状信息，利用GIS技术建立土地利用基础数据库，通过对2004年和2006年QuickBird图像与基础图像比较，找到变化地类信息，然后利用GPS实地测量变化图斑，导入GIS数据库后进行更新。获得2004年、2006年土地利用现状图及其变更信息，结果表明：利用3S结合技术能准确地发现土地利用变化图斑，快速更新土地利用现状数据库、统计出变更信息，是土地利用变更调查的有效技术手段。制定土地政策的重要依据．     

英语时态的情态意义

英语时态表达的基本意义是谓语动词所示动作发生的时间和所处的状态。但在特定的语境里,时态还能表达丰富的情态意义,也就是说,时态不仅是对谓语动词表示动作的发生时间和所处状态的客观描述,而且可表明讲话者与事件和情况的关系和对它们的态度或者看法。文章拟从系统功能语法的角度出发,探析英语时态的情态意义。     

英汉语小句的句法结构及其事件论元的实现?

英汉语中的小句结构统一表现为嵌套时态短语的话题短语投射。英语小句中的时态成分在不同小句类型中具有实现事件论元的可选性特征,其句法结果为英语名词小句和形容词小句中的主语移位至话题短语指定语位置,而英语动词小句中话题短语指定语的位置由小代语 pro 占据。话题短语指定语位置上成分的不同造成了英语不同小句中主语被动化提取的差别。汉语中的时态成分同样在实现事件论元方面具有可选性特征。汉语补足语小句中的时态成分选择不实现事件论元,因此汉语名词小句和形容词小句中话题短语指定语位置上的成分为小代语 pro。汉语独立小句中的时态则选择实现事件论元,其主语实现为话题。     

Event temporal relation computation based on machine learning

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event, and it is also the ultimate goal of temporal information processing. However, temporal relation computation based on machine learning requires a lot of hand-marked work, and exploring more features from discourse. A method of two-stage machine learning based on temporal relation computation (TSMLTRC) is proposed in this paper for the shortcomings of current temporal relation computation between two events. The first stage is to get the main temporal attributes of event based on classification learning. The second stage is to compute the event temporal relation in the discourse through employing the result of the first stage as the basic features, and also employing some new linguistic characteristics. Experiments show that, compared with the artificial golden rule, the computational efficiency in the first stage is much higher, and the F1-Score of event temporal relation which is computed through combining multi-features may be increased at 85.8% in the second stage.     

一种适合分布式实时数据的动态故障恢复模式

1 Introduction One class of applications , such as power networkscheduling, intelligent traffic and real-ti me monitor-ing,demand that distributed databases are able to pro-cess theti ming constraints of data and transactions andprovide ti mely and correct results ,i.e. distributedreal-ti me database systems are required .In general ,adistributed real-ti me database system(DRTDBS) is thedistributed database system within which transactionsand data have ti ming constraints and system correct-…     

How hypertext reading sequences affect understanding of causal and temporal relations in story comprehension

The goal of this study is to examine the comprehension of global causal and temporal relations between events that are represented in single hypertext documents. In two experiments we examined how reading sequences of hypertext nodes affects the establishment of event relations and how this process can be supported by advanced organizers that provide either temporal or causal pre-information. In the first experiment we could show that discourse structure affected recall of temporal event sequences only when causal relations were absent. In a second experiment, the comprehension of either temporal or causal relations was facilitated by providing different types of pre-information (advanced organizer). Temporal pre-information facilitated recall of temporal event sequences without underlying causal relations. In contrast, causal pre-information facilitated recall of temporal event sequences with causal relations. The results demonstrate that readers use causal information to construct temporal event sequences.     

The Structure of Events and Event Representations: A Developmental Analysis

In this study, we investigated how the temporal order and variability of events influence 3- and 5-year-old children's developing event representations. Children participated in 3 different events: a logical-invariant (making fundough), a logical-variable (shape collage), and an arbitrary-invariant (sand play) event. At each age, half of the children experienced the events once prior to recalling the events both verbally and behaviorally; the other half experienced the events 4 times, recalled the events verbally after each experience, and behaviorally reenacted the events only after the last experience. Children verbally recalled more and organized their recall better for the logical events than for the arbitrary event, and these differences remained stable with increasing experience. The sequencing of behavioral recall was also more accurate for the logical events than for the arbitrary event across age and condition, but amount of recall did not differ, with one exception. 3-year-old children in the single experience condition recalled less about the variable event than the invariant events. The results indicate that both the structure of the event and children's representational capabilities influence children's developing representations of events.     

Event temporal relation computation based on machine learning

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event,and it is also the ultimate goal of temporal information processing.However,temporal relation computation based on machine learning requires a lot of hand-marked work,and exploring more features from discourse.A method of two-stage machine learning based on temporal relation computation(TSMLTRC)is proposed in this paper for the shortcomings of current temporal relation computation between two events.The firs...     

文本阅读中时间信息的加工及其对文本理解的影响

总结了当今阅读心理研究领域关于文本理解中时间信息的加工及其对文本理解影响的研究。