An Operator Precedence Parser for Standard Prolog Text

Prolog is a language with a dynamic grammar which is the result of embedded operator declarations. The parsing of such a language cannot be done easily by means of standard tools. Most often, an existing parsing technique for a static grammar is adapted to deal with the dynamic constructs. This paper uses the syntax definition as defined by the ISO standard for the Prolog language. It starts with a brief discussion of the standard, highlighting some aspects that are important for the parser, such as the restrictions on the use of operators as imposed by the standard in order to make the parsing deterministic. Some possible problem areas are also indicated. As output is closely related to input in Prolog, both are treated in this paper. Some parsing techniques are compared and an operator precedence parser is chosen to be modified to deal with the dynamic operator declarations. The necessary modifications are discussed and an implementation in C is presented. Performance data are collected and compared with a public domain Prolog parser written in Prolog. It is the first efficient public domain parser for Standard Prolog that actually works and deals with all the details of the syntax.     

Nonstandard operator precedence in Excel

Microsoft's Excel spreadsheet program implements an unusual operator precedence in formulas. This can cause errors in statistical calculations, if it is not properly followed. For example, some errors by Excel in nonlinear regression reported by McCullough and Wilson (2002. On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Comput. Statist. Data Anal. 40, 713-721) were probably due to programming by the users that did not follow the unusual operator precedence rules. In this note the operator precedence in Excel is explained, the previous nonlinear regression results are corrected, and a simple example of maximum likelihood estimation is given.     

Weak Precedence Story Parsing Grammar

Story understanding is one of the important branches of natural language understanding research in AI techniques.The story understanding approach based on Story Parsing Grammar (SPG) involves that SPG is used to represent different abstracting processes of stories with different levels in story understanding and that the story understanding process is converted to the recognition process of stories using the syntactic parser of SPG.This kind of story understanding is called story parsing.In this paper,firstly a subclass of SPG,called Weak Precedence SPG(WPSPG),is defined.Afterwards the syntactic parsing algorithm of WPSPG is studied.An example of story parsing is also given.     

The parser generating system PGS

PGS is a parser generating system accepting LALR(1) and related grammars in extended BNF notation and producing parsers based on table-driven stack automata. To enable syntax-directed translation, semantic actions can be attached to rules of the input grammar. An attribution mechanism allows the transfer of information between rules. The generated parsers have an automatic error recovery which can be tailored to satisfy specific needs of the language to be accepted. PGS generates parsers written in Pascal, Modula-2, C or Ada. Compared with existing systems, e.g. YACC¹, a parser generated by PGS is twice as fast and the parse tables require 25 per cent less storage. This paper gives a survey of algorithms involved in the generator and the generated parsers, and compares them with algorithms used in other systems. In detail, it compares several parse-table representations and their implications for space and time efficiency of the generated parsers.     

A cognitive interactionist sentence parser with simple recurrent networks

Sentence parsing has a long history in the research fields of machine learning and natural language processing. The state-of-the-art technologies used to tackle this task include those based on statistical language learning. In the meantime, human sentence parsing has attracted massive research efforts for decades in the field of cognitive psychology. A range of behaviouristic experiments verify that the interactionist approach is a sensible and effective way to simulate the human parsing mechanism.This paper proposes a novel and effective sentence parser, the Cognitive Interactionist Parser (CIParser), which incorporates the cognitive interactionist approach with semantic information and simple recurrent networks to extend and enrich the technologies for sentence parsing. Considering the parsing efficiency, CIParser processes the semantic information of nouns and verbs in current stage.The performance of the Cognitive Interactionist Parser is evaluated using elaborately designed experiments using the noted SUSANNE Corpus. The experimental results demonstrate that the Cognitive Interactionist Parser surpasses two state-of-the-art statistical parsers in two classical measures, Precision and Recall, of Information Retrieval (IR).     

一种XML解析器技术的研究与实现

介绍了XML解析的详细过程,设计并实现了一个特定Schema的XML解析器的自动生成工具。该生成工具以一个XML Schema文件作为输入,输出一个JavaCC词法和语法规格说明文件,然后在JavaCC工具的帮助下,生成一个基于特定XML Schema的XML解析器。实验证明,这个生成解析器能够对XML文档进行解析的同时,验证其有效性。     

嵌入式WAP解析器的设计与实现

描述在10KB内存(实际使用内存池6KB)的嵌入式系统中如何使用C语言实现WAP浏览器的解析过程。讨论极小内存环境下的WAP解析器实现,并且涉及到如何使用一些模拟技术实现类似OOD(Object Oriented Design面向对象设计),以及如何提高软件的可移植性的问题。对该WAP浏览器的性能以及前景进行了测试与分析。     

支持多线程的空间数据GSQL解析器设计与实现*

为了使基于ingres扩展的空间数据库支持多线程空间数据上传和下载,设计实现了一个支持多线程的GSQL解析器。在分析PostGIS解析器的基础上,通过全局变量封装,实现解析器对多线程的支持。为了提高GSQL解析器的效率,提出并实现了可变长内存池管理机制。通过验证,GSQL解析器在多线程上传和下载空间数据时性能优良,而且使用可变长的内存池管理机制大幅度提高了GSQL解析器的时空效率。     

基于广义优序法的语言型多属性群决策方法

针对具有语言评价信息的多属性群决策问题,提出基于广义优序法的语言型多属性群决策方法。该方法通过对传统优序法进行有效拓展,采用近年来最新发展的二元语义概念,将语言评价信息转化为二元语义形式的广义优序数,并在此基础上利用方案广义优序数的偏差最大化思想求解得到属性权重,最终确定最优方案。该方法对语言信息的处理较为精确,有效地避免了信息的丢失和扭曲。最后,通过对风险投资案例的分析结果表明了所提出方法的简洁性和有效性。     

基于PostgreSQL解析器模块的底层研究与分析

介绍了PostgreSQL的整体架构,着重针对其解析器模块进行底层研究,分析查询请求在解析器模块中的查询处理过程和其内部各种函数的调用关系,最后就其解析器模块的开源代码做出的剖析以全面理解该模块的运作,并通过一个子查询程序调试实现了一个基本的查询请求。     

Decentralized multi-robot allocation of tasks with temporal and precedence constraints

Abstract

We present an auction-based method for a team of robots to allocate and execute tasks that have temporal and precedence constraints. Temporal constraints are expressed as time windows, within which a task must be executed. The robots use our priority-based iterated sequential single-item auction algorithm to allocate tasks among themselves and keep track of their individual schedules. A key innovation is in decoupling precedence constraints from temporal constraints and dealing with them separately. We demonstrate the performance of the allocation method and show how it can be extended to handle failures and delays during task execution. We leverage the power of simulation as a tool to analyze the robustness of schedules. Data collected during simulations are used to compute well-known indexes that measure the risk of delay and failure in the robots’ schedules. We demonstrate the effectiveness of our method in simulation and with real robot experiments.     

JavaScript语言解释器的设计与实现

论文介绍了作者自行设计并实现的JavaScript语言解释器,提出并实现了一种扩展的、基于对象的算符优先算法。     

Quantile-induced vector-based heavy OWA operator and the application in dynamic decision making

To the incentive problems in dynamic decision making, we propose a new type of aggregation operator denoted the quantile-induced vector-based heavy ordered weighted averaging (QI-VHOWA) operator. The main characteristic of the operator is that the arguments are aggregated using the form of vector. Additionally, the decision maker's incentive expectation is integrated into the aggregation process by various segmented incentive coefficients. We calculate the quantile measures of the argument vectors based on the technique for order of preference by similarity to ideal solution method. We determine the QI-VHOWA weighting vector by considering the location position of an alternative's performance as well as the development trend. The primary properties of the operator are discussed, including commutativity, boundness, and monotonicity under certain conditions. Finally, a numerical example regarding the evaluation of employees' performances in multiple periods is provided. The results are compared with that of the vector-based OWA and vector-based weighted arithmetic averaging operators. It is found that the incentive effectiveness of the QI-VHOWA is the most significant. The use of the QI-VHOWA operator is helpful to guide the long-term development of an alternative.     

基于改进线图分析算法的浅层汉语句法分析器的设计与实现

针对传统的汉语句法分析算法进行改进,采用了自底向上与自顶向下相结合的线图分析方法,分析、设计和实现了一个汉语句法分析原型系统.该系统实现了基于最大词长匹配算法的分词模块、基于统计方法的词性标注模块和基于改进的线图分析算法的句法分析模块.最后对系统进行小规模中文文本试验测试,测试结果表明利用改进的分析算法,使得句法分析效率和分析结果的准确率均有一定程度的提高.