论多媒体技术在高校中国现代文学课程中的辅助作用

近年来,多媒体技术已经越来越广泛地运用到了大学课堂的教学当中,在中国现代文学的教学中也发挥了较大的辅助佐用,如激发学习兴趣、突破教学难点,在文学史、文学理论、作家思想、诗歌、戏剧的教学及作品分析等方面发挥了重要作用,在讲座中多媒体课件与无线话筒、电子教鞭有机结合还能够达到非常好的演示效果。但多媒体技术也有一些局限性,必须在设备的提前调试、信息量的把握、教学过程的设计、影视资料的选择等方面有所注意,这样才能更好地辅助中国现代文学的教学。     

多媒体技术在数学教学中的作用

学生学习的动机很大程度上来源于兴趣,而多媒体教学集文本、图形图像、视频、动画等于一身,既直观、形象、新颖、生动,又能够直接作用于学生的多种感官,有利于激发学生的学习兴趣,从而彻底改变“满堂灌”的传统教学模式。喜欢新奇、有趣的事物是小学生的天性。多媒体教学有利于调节课堂气氛、创设学习情境,能够激发学生的求知欲,让学生满怀喜悦地去学习。兴趣是学生探索、     

多媒体技术在物理教学中的作用

多媒体技术进入物理教学领域,对提高学生的兴趣、开阔学生的视野、培养学生的能力都起到了积极的作用,同时也提高了课堂教学效率.下面是我在教学工作中,利用多媒体技术的几点体会.     

多媒体技术在物理教学中的作用

多媒体技术进入物理教学领域，对提高学生的兴趣、开阔学生的视野、培养学生的能力都起到了积极的作用，同时也提高了课堂教学效率。下面是我在教学工作中，利用多媒体技术的几点体会。     

多媒体技术在C语言教学中的应用研究

随着现代科学技术的发展,计算机已进入我国的教育领域,并得到迅速发展。计算机在教育上的应用,使得教学手段、教学方法、教材观念与形式、课堂教学结构以至教学思想与教学理论都发生了变革。运用多媒体教学,能够充分利用教学资源,对教与学的整个过程进行系统设计,利用最少时间得到最大的教学效果。本文将从三方面对多媒体在C语言教学中的应用进行论述。     

浅谈多媒体技术在数学教学中的作用

多媒体网络教学以其鲜明的教学特点．丰富的教学内容．形象生动的教学情境．促进了教育技术的信息化．并逐步打破“一块黑板．一支粉笔．一张嘴巴众人听”这种以教师灌输为主的传统教学法．建立起新型的教学模式。实践证明．多媒体课程资源作为一种特殊的知识载体以其固有的特点．在课堂教学中发挥着重要的作用。     

简论多媒体技术在政治教学中的作用

电化教学手段在课堂教学中的应用和普及,优化了教学手段,唤起了青少年的学习兴趣,提高了教学效益。特别是在政治课教学中,由于初中学生认识水平有限,生活经验不足,对有些陌生的知识     

多媒体技术在高中英语教学中的积极作用

如何利用电脑这一现代化的智能工具，把多媒体技术引入英语课堂，是英语教学发展的必然趋势。教学活动是向学生传授知识，而知识就是各种信息。通过多种信息形式向学生提供信息，是最好的信息传达方式。多媒体技术正是具有这种能力的一种技术。英语教学方法，不仅是向学生传授知识，培养学列习惯，而且要发展语言交际能力。在能力培养方面，多媒体技术会提供逼真的、生动的语言学习和交际环境。多媒体辅助教学给课堂注入了新的活力，有利于全方位地在英语教学中体现交际语言教学的原则。多媒体英语教学软件辅助教学有以下几个优点。     

The search for robustness in natural language understanding

Practical natural language understanding systems used to be concerned with very small miniature domains only: They knew exactly what potential text might be about, and what kind of sentence structures to expect. This optimistic assumption is no longer feasible if NLU is to scale up to deal with text that naturally occurs in the "real world". The key issue is robustness: The system needs to be prepared for cases where the input data does not correspond to the expectations encoded in the grammar. In this paper, we survey the approaches towards the robustness problem that have been developed throughout the last decade. We inspect techniques to overcome both syntactically and semantically ill-formed input in sentence parsing and then look briefly into more recent ideas concerning the extraction of information from texts, and the related question of the role that linguistic research plays in this game. Finally, the robust sentence parsing schemes are classified on a more abstract level of analysis.Dept. of Computer Science, University of TorontoFor helpful comments on earlier drafts of this paper, I thank Judy Dick, Graeme Hirst, Diane Horton, Kem Luther, and Jan Wiebe. Financial support by the University of Toronto is acknowledged. Communication and requests for reprints should be directed to the author at Department of Computer Science, University of Toronto, Toronto, Canada M5S 1A4.     

Inference and the computer understanding of natural language

The notion of computer understanding of natural language is examined relative to inference mechanisms designed to function in a language-free deep conceptual base (Conceptual Dependency). The conceptual analysis of a natural language sentence into this conceptual base, and the nature of the memory which stores and operates upon these conceptual structures are described from both theoretical and practical standpoints. The various types of inferences which can be made during and after the conceptual analysis of a sentence are defined, and a functioning program which performs these inference tasks is described. Actual computer output is included.     

Module-level testing for natural language understanding

Conclusion We have examined a module-level evaluation methodology for natural language understanding, illustrating insights gained during implementation. It is possible to produce useful quantitative and qualitative results using such a methodology, provided certain pitfalls are avoided.     

Connectionist systems for natural language understanding

We will discuss various connectionist schemes for natural language understanding (NLU). In principle, massively parallel processing schemes, such as connectionist networks, are well-suited for modelling highly integrated forms of processing. The connectionist approach towards natural language processing is motivated by the belief that a NLU system should process knowledge from many different sources, e.g. semantic, syntactic, and pragmatic, in just this sort of integrated manner. The successful use of spreading activation for various disambiguation tasks in natural language processing models lead to the first connectionist NLU systems. In addition to describing in detail a connectionist disambiguation system, we will also discuss proposed connectionist approaches towards parsing and case role assignment. This paper is intended to introduce the reader to some of the basic ideas behind the connectionist approach to NLU. We will also suggest some directions for future research.     

基于自然语言理解的SPARQL本体查询

为了用户能够方便地获取本体知识,提出了基于自然语言理解的SPARQL本体查询。利用Stanford Parser分析用户的自然语言查询,根据语法构建查询三元组,与关键词的方法相比,有效地减少了组合的个数。结合用户词典,能较准确地把查询三元组的词汇映射到本体实体。分值计算时除了考虑词语的形式相似和语义相似外,还考虑了概念的模糊性,尽量返回具体的概念。利用本体推理获取隐藏在本体中的信息,对查询进行过滤和限制,提高了准确率。用户通过图形交互界面和系统进行交互,选择需要的结果,最后返回树形查询结果,并能看到相关的信息。实验结果表明,该方法达到了预期的效果。     

基于领域的自然语言篇章理解的实现方法

介绍了一种应用于知识系统的基于领域的自然语言理解篇章理解的实现方法.该方法的核心是聚类和关联,本质上是关注概念和概念间的关联.以本体作为知识表示的方法,通过对基本概念的聚类,把底层离散的概念进行初步的集中,再通过分析底层概念间的相互关联,把整个篇章中的基本概念都联系起来,得到一个篇章层次上的本体模型.     

Situating natural language understanding within experience-based design

Building useful systems with an ability to understand "real" natural language input has long been an elusive goal for Artificial Intelligence. Well-known problems such as ambiguity, indirectness, and incompleteness of natural language inputs have thwarted efforts to build natural language interfaces to intelligent systems. In this article, we report on our work on a model of understanding natural language design specifications of physical devices such as simple electrical circuits. Our system, called KA, solves the classical problems of ambiguity, incompleteness and indirectness by exploiting the knowledge and problem-solving processes in the situation of designing simple physical devices. In addition, KA acquires its knowledge structures (apart from a basic ontology of devices) from the results of its problem-solving processes. Thus, KA can be bootstrapped to understand design specifications and user feedback about new devices using the knowledge structures it acquired from similar devices designed previously.In this paper, we report on three investigations in the KA project. Our first investigation demonstrates that KA can resolve ambiguities in design specifications as well as infer unarticulated requirements using the ontology, the knowledge structures, and the problem-solving processes provided by its design situation. The second investigation shows that KA's problem-solving capabilities help ascertain the relevance of indirect design specifications, and identify unspecified relations between detailed requirements. The third investigation demonstrates the extensibility of KA's theory of natural language understanding by showing that KA can interpret user feedback as well as design requirements. Our results demonstrate that situating language understanding in problem solving, such as device design in KA, provides effective solutions to unresolved problems in natural language processing.     

On algorithmic natural language analysis and understanding

In this article an attempt is made to substantiate that understanding and analyzing natural language texts is not algorithmic, that is to show that there is a natural boundary separating the human from the algorithm supposed to model understanding and syntax analysis. Our main concern will be to discriminate the human from the algorithm with respect to the notion of truth; the analogous discrimination with respect to understanding and analyzing natural language will then be shown reduceable to the first mentioned one.To this end we cannot apply directly the classic results, such for example as Goedel's incompleteness theorem. In fact all these results attempt to show inevitable shortcomings in the formalism's way of handling some important tasks. Now, however, we are confronted with the problem of differences between the human and the formalism. This problem differs from the one set in Hilbert's programme in that now a formalism is wanted which would act, (e.g. in handling the notion of truth) as imperfectly as the human does. We attempt to show such a formalism to be impossible.Our analysis cannot be a mathematical one (and not even a formal deduction in general) as for example it is impossible to obtain formally the conclusion “intelligence is not formalizable” after having started the investigation with formalizing intelligence. This certainly calls down methodological difficulties, which better are avoided, and apparently weakens the discourse. But, those not having what they like have to like what they have. Besides we are not within a deductive science and there is no reason to limit our methods to deductive ones. And, after all, we are in good company: the present investigation seems methodologically related to metaphysical discourses of physics (such, e.g. as Gendanken-experiments, first named but not first used by Einstein) or speculations of astro-physics. Finally, the problem of limitations for Artificial Intelligence, as well as for Computer Science in general, just exists independently from whether you are able to handle it mathematically or not.     

Exploiting convergence to improve natural language understanding

Convergence is the phenomenon in human dialogue whereby participants adopt characteristics of each other's speech. Communicants are unaware of this occurring. If it were possible to invoke such a phenomenon in a natural language interface it would provide a means of keeping user inputs within the range of lexical and syntactic coverage of the system, while keeping the dialogue ‘natural’ in the sense of requiring no more conscious effort in observing conventions of format than human-human dialogue.

A ‘Wizard of Oz’ study was conducted to test the feasibility of this technique. Subjects were required to type queries into what they thought was a natural language database querying system. On completion of input the system presented a paraphrase for confirmation by subjects before presenting the answer. The paraphrases were constructed using particular terms and syntactic structures. Subjects began to use these terms and structures spontaneously in subsequent queries.

Observation of convergence in human-computer dialogue suggests that the technique can be incorporated in user interfaces to improve communication. The implementation issues for natural language dialogue are discussed, and other applications of the technique in HCI are outlined.     

基于自然语言的分拣机器人解析器技术研究

工业机器人通常采用特定的机器人语言进行示教编程与控制,对于操作人员需要具有较高专业与技能要求,并且示教周期长导致工作效率降低。为了提高工业机器人使用效率与易用性,提出一种基于受限自然语言解析器的设计方法。该系统通过对受限自然语言进行词法解析、语法解析、语义解析,得到所需求的工作意图,然后与实时生成的三维空间语义地图进行匹配,结合机械臂轨迹规划,生成能够完成工作任务的机器人作业程序,并完成了机器人作业程序的解析与实际机械臂的控制。通过实验证明设计的基于受限自然语言处理的分拣机器人解析器能够正确解析自然语言命令,实现对机械臂的控制。