中文词义关系的定义与判定原则

在英语及其它的欧洲语言里,词汇语意关系已有相当充分的研究。例如,欧语词网( EuroWordNet ,Vossen 1998) 就是一个以语意关系来勾勒词汇词义的数据库。也就是说,词汇意义的掌握是透与其它词汇语意的关连来获致的。为了确保数据库建立的品质与一致性,欧语词网计画就每一个处理的语言其词汇间的词义关系是否成立提出相应的语言测试。实际经验显示,利用这些语言测试,人们可以更容易且更一致地辨识是否一对词义之间确实具有某种词义关系。而且,每一个使用数据库的人也可以据以检验其中关系连结的正确性。换句话说,对一个可检验且独立于语言的词汇语意学理论而言,这些测试提供了一个基石。本文中,我们探究为中文词义关系建立中文语言测试的可能性。尝试为一些重要的语意关系提供测试的句式和规则来评估其可行性。这项研究除了建构中文词汇语意学的理论基础,也对Miller的词汇网络架构(WordNet ,Fellbaum 1998) 提供了一个有力的支持,这个架构在词汇表征和语言本体架构研究上开拓了关系为本的进路。     

Determining Prepositional Attachment, Prepositional Meaning, Verb Meaning, and Thematic Roles

An algorithm for semantic interpretation that integrates the determination of the meaning of verbs, the attachment and meaning of prepositions, and the determination of thematic roles is presented. The parser does not resolve structural ambiguity, which is solely the task of the semantic interpreter. Lexical semantic information about nouns and verbs is applied to the resolution of verb polysemy and modifier attachment. Semantic interpretation is centered on the representation of the meaning of the verb, called verbal concept. Verbal concepts are organized into a classification hierarchy. As long as the meaning of the verb remains unknown, parsing proceeds on a syntactic basis. Once the meaning of the verb is recognized, the semantic component makes sense of the syntactic relations built so far by the parser and of those still to be parsed. The algorithm has been implemented and tested on real–world texts.     

Mining multiple-level spatial association rules for objects with a broad boundary

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceeds people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. Most of the spatial data mining methods do not take into account the uncertainty of spatial information. In our work we use objects with broad boundaries, the concept that absorbs all the uncertainty by which spatial data is commonly affected and allows computations in the presence of uncertainty without rough simplifications of the reality. The topological relations between objects with a broad boundary can be organized into a three-level concept hierarchy. We developed and implemented a method for an efficient determination of such topological relations. Based on the hierarchy of topological relations we present a method for mining spatial association rules for objects with uncertainty. The progressive refinement approach is used for the optimization of the mining process.     

Data Mining via Discretization, Generalization and Rough Set Feature Selection

We present a data mining method which integrates discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated. Numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. The horizontal reduction is done by merging identical tuples after substituting an attribute value by its higher level value in a pre- defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples we consider further in the database(s). In the second phase, a novel context- sensitive feature merit measure is used to rank features, a subset of relevant attributes is chosen, based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without changing the interdependence relationships between the classes and the attributes. Finally, the tuples in the reduced relation are transformed into different knowledge rules based on different knowledge discovery algorithms. Based on these principles, a prototype knowledge discovery system DBROUGH-II has been constructed by integrating discretization, generalization, rough set feature selection and a variety of data mining algorithms. Tests on a telecommunication customer data warehouse demonstrates that different kinds of knowledge rules, such as characteristic rules, discriminant rules, maximal generalized classification rules, and data evolution regularities, can be discovered efficiently and effectively.     

空间对象及其拓扑关系

论文首先讨论了传统的地理信息系统中空间对象的空间数据模型,提出了面向对象的层次矢量数据模型。然后定性地分析了空间对象点与点、点与线和点与区域之间的拓扑关系,并给出了一种形式化表达空间对象成分拓扑关系的模型。最后,将这种模型与空间对象之间的拓扑关系结合起来,使其可以方便地处理地理信息系统中的对象间的拓扑关系。     

Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach

This study presents a new method for the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based classification approach. This new method can integrate an object with its super-objects’ metrics. The entire classification involves two object hierarchies: (1) a five-level object hierarchy to extract object metrics at five scales, and (2) a three-level object hierarchy for the classification process. A five-level object hierarchy was developed through multi-scale segmentation to calculate and extract both spectral and textural metrics. Layers representing the hierarchy at each of the five scales were then intersected by using the overlay tool, an intersected layer was created with metrics from all five scales, and the same geometric elements were retained as those of the objects of the lowest level. A decision tree analysis was then used to build rules for the classification of the intersected layer, which subsequently served as the thematic layer in a three-level object hierarchy to identify shadow regions and produce the final map. The use of multi-scale object metrics yielded improved classification results compared with single-scale metrics, which indicates that multi-scale object metrics provide valuable spatial information. This method can fully utilize metrics at multiple scales and shows promise for use in object-based classification approaches.     

基于J2EE的空间关联规则研究与原型系统实现

空间关联规则与传统关联规则的主要区别在于空间关联规则挖掘需要考虑空间实体的距离关系、方位关系和拓扑关系,而空间概念层次的形成往往是基于多个空间数据层或面向特定主题的。该文在研究空间关联规则算法的基础上,提出一种基于概念树的多层次空间关联规则挖掘算法,设计和实现一种基于J2EE的空间关联规则原型挖掘系统,并以某市土地利用为例说明了系统的实施过程。该系统挖掘出来的8大类土地利用类型的空间关联规则具有一定的现实意义。     

FDAS: architecture and implementation

Abstract: FDAS (Fabric Defects Analysis System) is a knowledge-based system (KBS) for diagnosing defects in woven textile structures. The following major issues were considered in the design of FDAS: (1) range of applications; (2) user profiles; (3) response time requirements; (4) modularity and (5) ease of system modification and enhancements. Knowledge about defects is represented in FDAS using a hierarchy of classes, with the slots representing defect attributes, and forward chaining rules. The inferencing process is controlled by slots of another distinct class hierarchy. Inference is made more efficient by hierarchical classification of the defects with pruning. The agenda (i.e. ordered set of hypotheses) is dynamically reset using actions attached to rules. The diagnosis information—information about the causes of the defects and remedial actions to be taken—is kept separate from the rules in the knowledge base. The user interface part of the system is also independent of the knowledge base, which facilitates easier tailoring of the system to meet the needs of different users. The user interaction with FDAS is menu-based and has been designed to minimize cognitive load on the user. FDAS has been extensively evaluated by in-house individuals who are experts in the task of fabric defects analysis. It has also been demonstrated to experts from the industry and is ready for field tests.     

Efficient Implementation of Semantic Relations in Lexical Databases

In this paper we develop a formalization of semantic relations that facilitates efficient implementations of relations in lexical databases or knowledge representation systems using bases. The formalization of relations is based on a modeling of hierarchical relations in Formal Concept Analysis. Further, relations are analyzed according to Relational Concept Analysis, which allows a representation of semantic relations consisting of relational components and quantificational tags. This representation utilizes mathematical properties of semantic relations. The quantificational tags imply inheritance rules among semantic relations that can be used to check the consistency of relations and to reduce the redundancy in implementations by storing only the basis elements of semantic relations. The research presented in this paper is an example of an application of Relational Concept Analysis to lexical databases and knowledge representation systems (cf. Priss 1996) which is part of a larger framework of research on natural language analysis and formalization.     

A multi-objective heuristic algorithm for gene expression microarray data classification

Microarray data has significant potential in clinical medicine, which always owns a large quantity of genes relative to the samples’ number. Finding a subset of discriminatory genes (features) through intelligent algorithms has been trend. Based on this, building a disease prognosis expert system will bring a great effect on clinical medicine. In addition, the fewer the selected genes are, the less cost the disease prognosis expert system is. So the small gene set with high classification accuracy is what we need. In this paper, a multi-objective model is built according to the analytic hierarchy process (AHP), which treats the classification accuracy absolutely important than the number of selected genes. And a multi-objective heuristic algorithm called MOEDA is proposed to solve the model, which is an improvement of Univariate Marginal Distribution Algorithm. Two main rules are designed, one is ’Higher and Fewer Rule’ which is used for evaluating and sorting individuals and the other is ‘Forcibly Decrease Rule’ which is used for generate potential individuals with high classification accuracy and fewer genes. Our proposed method is tested on both binary-class and multi-class microarray datasets. The results show that the gene set selected by MOEDA not only results in higher accuracies, but also keep a small scale, which cannot only save computational time but also improve the interpretability and application of the result with the simple classification model. The proposed MOEDA opens up a new way for the heuristic algorithms applying on microarray gene expression data.     

Development of Granular Fuzzy Relation Equations Based on a Subset of Data

Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.      

基于知网的中文短信文本词汇链抽取方法

提出一种基于知网的中文短信文本词汇链抽取方法。根据知网的语义关系,利用相同语义类给出上下文词汇项信息,构造多条词汇链,表达短信文本的多条叙事线索,从中抽取富含短信文本信息的词汇链,表达短信文本的语义信息,采用词汇链的关键词集合进行文本分类。实验结果证明,该方法的抽取准确率较高,文本分类速度较快。     

An isolated-word recognizer based on grammar-controlled classification processes

A recognizer of isolated words spoken in the Italian language is presented. Each level of recognition (segmentation, phonemic classification and lexical recognition) is controlled by the rules of appropriate grammars whose symbols are fuzzy linguistic variables. The recognition strategy depends on the lexical redundancy of the protocol and is based on a classification of speech units into broad phonetic classes, eventually followed by a classification into more detailed classes if some ambiguities still remain.     

GIS中由多种方向关系推理拓扑关系的方法

研究了根据多种方向关系（包括内部、边界、环部和外部等方向关系）推理拓扑关系的方法．在推理中,首先提出了根据单种方向关系推理拓扑关系的规则;然后,将多种方向关系的组合分为4种基本类型,每种类型的推理方法和规则可表示为单种方向关系推理的组合;最后,讨论了根据多种方向关系可得到的拓扑关系的几何关系、约束条件和推理规则,根据多种方向关系推理拓扑关系的方法和规则可用于空间数据库查询和基于内容的数据检索．     

基于概念网的Web课件组织及导航系统的研究

文章介绍了用于组织课程知识的概念网模型。利用概念的层次和概念关联，将课件片断组织成合理分类、多层次、揭示复杂的相互依存关系的知识网络；通过概念检索与概念联想实现课件组织、生成与学习推进。     

A Reasoning System of Ternary Projective Relations

This paper introduces a reasoning system based on a previously developed model for ternary projective relations between spatial objects. The model applies to spatial objects of the kind point and region is based on basic projective invariants and takes into account the size and shape of the three objects that are involved in a relation. The reasoning system proposes a set of permutation and composition rules, which allow the inference of unknown relations from given ones.     

Complex predicates in Indian languages and wordnets

Wordnets, which are repositories of lexical semantic knowledge containing semantically linked synsets and lexically linked words, are indispensable for work on computational linguistics and natural language processing. While building wordnets for Hindi and Marathi, two major Indo-European languages, we observed that the verb hierarchy in the Princeton Wordnet was rather shallow. We set to constructing a verb knowledge base for Hindi, which arranges the Hindi verbs in a hierarchy of is-a (hypernymy) relation. We realized that there are unique Indian language phenomena that bear upon the lexicalization vs. syntactically derived choice. One such example is the occurrence of conjunct and compound verbs (called Complex Predicates) which are found in all Indian languages. This paper presents our experience in the construction of lexical knowledge bases for Indian languages with special attention to Hindi. The question of storing versus deriving complex predicates has been dealt with linguistically and computationally. We have constructed empirical tests to decide if a combination of two words, the second of which is a verb, is a complex predicate or not. Such tests provide a principled way of deciding the status of complex predicates in Indian language wordnets.     

融合时态特征的日英层次短语翻译模型

针对基于层次短语翻译模型的统计机器翻译使用上下文信息有限,时态翻译质量不高的问题,提出一种融合时态特征的日英统计机器翻译方法。该方法通过引入翻译规则的时态分类约束信息,解码器可以根据每条规则的潜在时态分类,为相应时态的句子匹配到最合适的规则进行翻译。首先从双语训练语料中抽取时态特征构建最大熵分类模型,然后再抽取包含各类时态信息的层次短语规则的时态特征,最后将规则的时态分类结果作为一类新特征,融入基于层次短语的翻译系统中。实验结果表明,与基线系统相比,该方法在多个测试集上提高了翻译质量,在一定程度上解决了日英层次短语模型的时态翻译问题。     

土地利用分类规则集的构建与应用研究

利用高空间分辨率影像,采用面向对象的分类方法,通过多种方法确定了土地利用类型的适宜尺度,形成了多尺度的影像对象层次网络体系。对影像对象进行多特征与空间关系描述,有效集成了辅助特征和专家知识,构建了影像对象分类规则集。研究区分类结果表明:地物分布特征及其空间关系规则的应用,可以有效地提高分类精度,得到更好的语义区分和更精确的分类结果。以期仅作少许改动就可将方案应用到条件类似的高分辨率影像分类中。     

A fuzzy rule based approach to cloud cover estimation

A fuzzy rule based cloud classification scheme is proposed to estimate the cloud cover from satellite imagery. METEOSAT-5 images are classified into three classes: cloudy, partially cloudy, and clear sky. Five features, which measure the temporal and spatial properties of visible (VIS) and infrared (IR) images of METEOSAT-5, are used for this. The proposed classifier finds out a few human understandable rules (fuzzy rules) using exploratory data analysis. A novel attribute of the system is that it analyzes the behavior of misclassifications during training (i.e., typical mistakes) to extract a few more rules which are augmented to the initial rule base to improve its performance. The scheme is tested on images other than the training image(s) and the performance is found to be quite satisfactory. A post-processing scheme is also developed, which utilizes experts' knowledge to generate additional rules to account for coastal region, sunglint areas, and snow-covered Himalayan region. This improves the performance of the system further. Finally, the classification results are compared with multispectral threshold tests, surface synoptic observations, and total cloud cover (tcdc) of reanalysis data produced by National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR). The high accuracy achieved by the proposed method may be attributed to (1) better design philosophy of classifiers; (2) good choice for the feature vectors; (3) accurate labeling of training data; and (4) exploitation of experts' knowledge.