概念形成学习系统中数值属性的表示与聚类

本文在对ＣＯＢＷＥＢ、ＣＬＡＳＳＩＴ等概念聚类系统研究的基础上，提出了一种用数值属性的聚类分划来表示数值属性的方法．这种表示的核心是基于数值属性的取值分布．对于在这种表示下数值属性聚类的相关问题及性质，本文进行了较为详细的讨论．在此基础上，本文给出了一个能统一处理数值属性和符号属性的聚类评价函数．一个基于聚类分划表示方法的概念形成学习系统ＣＦＬＳ（ｃｏｎｃｅｐｔｆｏｒｍａｔｉｏｎｌｅａｒｎｉｎｇｓｙｓｔｅｍ）已在微机上实现，并被应用于地质学领域的三叶虫分类问题．本文对ＣＦＬＳ的设计和实现进行了介绍.     

采用模糊自适应评价的增强式学习控制

本文提出了基于模糊自适应评价（ＦＬＡＣ）的增强式学习（Ｒｅｉｎｆｏｒｃｅｍｅｎｔ Ｌｅａｒｎｉｎｇ）控制系统（ＦＬＡＣ／ＡＳＮ）,ＦＬＡＣ采用模糊规则表示学到的知识,因此可以有机地融入专家的经验。ＦＬＡＣ的学习方法国瞬时微分法（Ｔｅｍｐｏｒａｌ Ｄｉｆｆｅｒｅｎｃｅ）。作用选择网络（ＡＳＮ）采用多导同网络。仿真结果表明（ＦＬＡＣ／ＡＳＮ）具有很好的学习性能。     

基于ORACLE特性的优化调整

本文首先简要介绍ＯＲＡＣＬＥ的特性，然后提出了基于ＯＲＡＣＬＥ的特性进行了优化调整。由于在ＯＲＡＣＬＥ的各种工具中都要用到ＳＱＬ语句而且每个应用都要用ＦＯＲＭ设计很多屏幕表格以完成功能，因此本文主要就何为优化ＳＱＬ语句及调整ＦＯＲＭ展开讨论。     

Visual Foxpro5.0与其他软件间的资源共享

本文介绍了 Ｖｉｓｕａｌ Ｆｏｘｐｒｏ提供的 ＯＬＥ技术及其应用方法,并引用了多个实例详细介绍 ＶＦＰ与 ＤＥＬＰＨＩ,ＥＸＣＥＬ,ＷＯＲＤ,ＶＢ,ＩＥ等应用程序间的相互联系。     

基于改进型CLAFIC学习子空间算法的有限汉字集识别

采用改进型ＣＬＡＦＩＣ（Ｃｌａｓｓ－Ｆｅａｔｕｒｉｎｇ Ｉｎｆｏｒｍａｔｉｏｎ Ｃｏｍｐｒｅｓｓｉｏｎ）算法可以为学习子空间ＬＳＭ（Ｌｅａｒｎｉｎｇ Ｓｕｂｓｐａｃｅ Ｍｅｔｈｏｄ）算法提供更好的初始向量子空间,并通过ＬＳＭ算法对各类样本子空间按不同的旋转方式训练,来提高ＯＣＲ的识别率,该文的特点在于首先采用了学习子空间算法来实现字符在灰度图像上的识别,它克服了传统的基于二值化图像进行特征提取和识     

点阵式液晶显示控制器SED1335F的应用

文章主要介绍了新型显示器ＬＣＤ的控制器ＳＥＤ１３３５Ｆ；介绍了采用８７５１系列单片机控制点阵ＬＣＤ的硬件设计和软件编程。     

点阵式液晶显示控制器SED1335F的应用

文章主要介绍了新型显示器ＬＣＤ的控制器ＳＥＤ１３３５Ｆ，介绍了采用８７５１系列单片机控制点阵ＬＣＤ的硬件设计和软件编程。     

UNIX环境下ORACLE实时更新FORM的研究与实现

ＳＱＬ＊Ｆｏｒｍ是ＯＲＡＣＬＥ公司最为重要的应用开发工具之一，在实际工程项目中有着极其广泛的应用。本文介绍利用ＯＲＡＣＬＥ公司所提供的功能对其进行扩充，并利用ＵＮＩＸ操作系统所提供的进程间通信功能使ＳＱＬ＊Ｆｏｒｍｓ能够处理需要实时控制的应用     

面向对象的函数式语言及其实现技术

ＯＯＦＬ是我们设计的一种面向对象的函数式语言，该语言具有对象式语言和函数式语言的优点。本文详细介绍了ＯＯＦＬ到Ｃ＋＋的转换技术，构造了ＯＯＦＬ语言的元程序设计环境，探讨了对象函数式语言的一些实现方法，并在微机上实现了ＯＯＦＬ到Ｃ＋＋的转换系统。     

现场总线网络中实时连接和实时通信的研究

本文针对现场总线网络的实时应用特征，采用了ＬＡＮ体系结构中的三层构造，重点研究了其ＬＬＣ子层的ＦＢＣＯＳ、ＦＢＣＬＳ、优先分配、ＲＴＶＣ和预约等问题，解决了传统ＬＡＮ所无法解决的实时连接问题，从而保证了其实时应用的可靠完成。     

Analyzing holistic parsers: implications for robust parsing and systematicity

Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent preorder parser (CPP). In the analysis, the CPP is viewed as a dynamical system, and holistic parsing is perceived as a sequence of state transitions through its state-space. The seemingly one-shot parsing mechanism can thus be elucidated as a step-by-step inference process, with the intermediate parsing decisions being reflected by the states visited during parsing. The study serves two purposes. First, it improves our understanding of how grammatical errors are corrected by the CPP. The occurrence of an error in a sentence will cause the CPP to deviate from the normal track that is followed when the original sentence is parsed. But as the remaining terminals are read, the two trajectories will gradually converge until finally the correct parse tree is produced. Second, it reveals that having systematic parse tree representations alone cannot guarantee good generalization performance in holistic parsing. More important, they need to be distributed in certain useful locations of the representational space. Sentences with similar trailing terminals should have their corresponding parse tree representations mapped to nearby locations in the representational space. The study provides concrete evidence that encoding the linearized parse trees as obtained via preorder traversal can satisfy such a requirement.     

Problem reduction representation for the linguistic analysis of waveforms

This paper shows how the nondirectional structural analysis of pattern data can be performed by matching a problem reduction representation (PRR) of pattern structure with sample data, using a best-first state space search algorithm called SSS*. The end result of the matching algorithm is a tree whose nodes represent recognized structures in the data. Tip nodes of the tree structure correspond to primitives which are recognized in the raw data by curve fitting routines. The operators of the algorithm allow the tree to be constructed with a combination of top-down or bottom-up steps. The matching of the structure tree to waveform segments need not be done in a left-right sequence. Moreover ambiguous matches are pursued in a best first order by using state space search with partial parse trees as states. A software system called WAPSYS (for waveform parsing system) is described, which implements this structural analysis paradigm. Experience using WAPSYS to analyze carotid pulse waves is also discussed.     

Semantic Role Labeling of Chinese Nominal Predicates with Dependency-Driven Constituent Parse Tree Structure

This paper explores a tree kernel based method for semantic role labeling （SRL） of Chinese nominal predicates via a convolution tree kernel. In particular, a new parse tree representation structure, called dependency-driven constituent parse tree （D-CPT）, is proposed to combine the advantages of both constituent and dependence parse trees. This is achieved by directly representing various kinds of dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT （Constituent Parse Tree）. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree （DPT） structure but also retains the basic hierarchical structure of CPT style. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT. This largely reduces the noisy information inherent in D-CPT. Finally, a convolution tree kernel is employed to compute the similarity between two parse trees. Besides, we also implement a feature-based method based on D-CPT. Evaluation on Chinese NomBank corpus shows that our tree kernel based method on D-CPT performs significantly better than other tree kernel-based ones and achieves comparable performance with the state-of-the-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure in representing various kinds of dependency relations in a CPT-style structure and our tree kernel based method in exploring the novel D-CPT structure. This also illustrates that the kernel-based methods are competitive and they are complementary with the feature- based methods on SRL.     

Efficiently building a parse tree from a regular expression

We show in this paper that parsing with regular expressions instead of context-free grammars, when it is possible, is desirable. We present efficient algorithms for performing different tasks that concern parsing: producing the external representation and the internal representation of parse trees; producing all possible parse trees or a single one. Each of our algorithms to produce a parse tree from an input string has an optimal time complexity, linear with the length of the string. Moreover, ambiguous regular expressions can be used. Received: 21 October 1997 / 30 May 2000     

融合句法解析树的汉-越卷积神经机器翻译

神经机器翻译是目前应用最广泛的机器翻译方法,在语料资源丰富的语种上取得了良好的效果.但是在汉语-越南语这类缺乏双语数据的语种上表现不佳.考虑汉语和越南语在语法结构上的差异性,提出一种融合源语言句法解析树的汉越神经机器翻译方法,利用深度优先遍历得到源语言的句法解析树的向量化表示,将句法向量与源语言词嵌入相加作为输入,训练翻译模型.在汉-越语言对上进行了实验,相较于基准系统,获得了0.6个BLUE值的提高.实验结果表明,融合句法解析树可以有效提高在资源稀缺情况下机器翻译模型的性能.     

A comparison of storage optimizations in automatically-generated attribute evaluators

Summary Attribute grammars are a value-oriented, non-procedural extension to context-free grammars that facilitate the specification of translations whose domain is described by the underlying context-free grammar. Just as parsers for context-free languages can be automatically constructed from a context-free grammar, so can translators, called attribute evaluators, be automatically generated from an attribute grammar. A major obstacle to generating efficient attribute evaluators is that they typically use large amounts of memory to represent the attributed parse tree. In this report we investigate the problem of efficient representation of the attributed parse tree by analyzing and comparing the strategies of two systems that have been used to automatically generate a translator from an attribute grammar: the GAG system developed at the Universitat de Karlsruhe and the LINGUIST-86 system written at Intel Corporation. Our analysis will characterize the two strategies and highlight their respective strengths and weaknesses. Drawing on the insights given by this analysis, we propose a strategy for storage optimization in automatically generated attribute evaluators that not only incorporates the best features of both GAG and LINGUIST-86, but also contains novel features that address aspects of the problem that are handled poorly by both systems.This research was partially supported by the National Science Foundation under grant DCR-83-10930, and partially supported by the Defense Advanced Research Projects Agency under contract number N00039-84-C-0165     

Recursive functions of context free languages (Ⅱ)——Validity of CFPRF and CFRF definitions

In this paper we proved that the function class CFRF and its proper subclass CFPRF are respectively the partial recursive functions and primitive recursive functions of context free languages (CFLs). Also we discussed the relation between them and recursive functions defined on other domains . It is indicated that the functions of natural numbers and/or symbol strings (words) are functions of CFLs. Several frequently used primitive recursive functions on words were given, including logical connectives, conditional expressions. Also the powerful operators (bounded maximization and minimization operators) for constructing primitive recursive functions were defined. Two important nontrivial algorithms, the characteristic function of arbitrary CFL and the parse function of CFL sentences were constructed. Based on them, the method for extending or restricting function domain was described.     

An application for plagiarized source code detection based on a parse tree kernel

Program plagiarism detection is a task of detecting plagiarized code pairs among a set of source codes. In this paper, we propose a code plagiarism detection system that uses a parse tree kernel. Our parse tree kernel calculates a similarity value between two source codes in terms of their parse tree similarity. Since parse trees contain the essential syntactic structure of source codes, the system effectively handles structural information. The contributions of this paper are two-fold. First, we propose a parse tree kernel that is optimized for program source code. The evaluation shows that our system based on this kernel outperforms well-known baseline systems. Second, we collected a large number of real-world Java source codes from a university programming class. This test set was manually analyzed and tagged by two independent human annotators to mark plagiarized codes. It can be used to evaluate the performance of various detection systems in real-world environments. The experiments with the test set show that the performance of our plagiarism detection system reaches to 93% level of human annotators.     

Web table discrimination with composition of rich structural and content information

A table is a well-organized and summarized knowledge expression for a domain. Therefore, it is of great importance to extract information from tables. However, many tables in Web pages are used not to transfer information but to decorate pages. One of the most critical tasks in Web table mining is thus to discriminate meaningful tables from decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for discrimination. This paper proposes a novel discrimination method using a composite kernel which combines parse tree kernels and a linear kernel. Because a Web table is represented as a parse tree by an HTML parser, it is natural to represent the structural information of a table as a parse tree. In this paper, two types of parse trees are used to represent structural information within and around a table. These two trees define the structure kernel that handles the structural information of tables. The contents of a Web table are manipulated by a linear kernel with content features. Support vector machines with the composite kernel distinguish meaningful tables from decorative ones with high accuracy. A series of experiments show that the proposed method achieves state-of-the-art performance.     

Learning context-free grammars using tabular representations

We present a novel algorithm using new hypothesis representations for learning context-free grammars from a finite set of positive and negative examples. We propose an efficient hypothesis representation method which consists of a table-like data structure similar to the parse table used in efficient parsing algorithms for context-free grammars such as Cocke-Younger-Kasami algorithm. By employing this representation method, the problem of learning context-free grammars from examples can be reduced to the problem of partitioning the set of nonterminals. We use genetic algorithms for solving this partitioning problem. Further, we incorporate partially structured examples to improve the efficiency of our learning algorithm, where a structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of the unknown grammar. We demonstrate some experimental results using these algorithms and theoretically analyse the completeness of the search space using the tabular method for context-free grammars.