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Semantic Relevance and Aspect Dependency in a given Subject Domain

Contents-driven algorithmic processing of fuzzy word

meanings to form dynamic stereotype representations?

Burghard B.Rieger

Arbeitsgruppe f¨u r mathematisch-empirische Systemforschung(MESY) German Department,Technical University of Aachen,

Aachen,West Germany

Abstract

Cognitive principles underlying the(re-)construction of word meaning and/or world knowledge structures are poorly understood yet.In a rather sharp depar-

ture from more orthodox lines of introspective acquisition of structural data

on meaning and knowledge representation in cognitive science,an empirical

approach is explored that analyses natural language data statistically,repre-

sents its numerical?ndings fuzzy-set theoretically,and interprets its intermedi-

ate constructs(stereotype meaning points)topologically as elements of semantic

space.As connotative meaning representations,these elements allow an aspect-

controlled,contents-driven algorithm to operate which reorganizes them dynam-

ically in dispositional dependency structures(DDS-trees)which constitute a pro-

cedurally de?ned meaning representation format.

1Introduction

Modelling system structures of word meanings and/or world knowledge is to face the problem of their mutual and complex relatedness.As the cognitive principles un-derlying these structures are poorly understood yet,the work of psychologists,AI-researchers,and linguists active in that?eld appears to be determined by the re-spective discipline’s general line of approach rather than by consequences drawn from these approaches’intersecting results in their common?eld of interest.In linguistic semantics,cognitive psychology,and knowledge representation most of the necessary data concerning lexical,semantic and/or external world information is still provided introspectively.Researchers are exploring(or make test-persons explore)their own linguistic/cognitive capacities and memory structures to depict their?ndings(or let hypotheses about them be tested)in various representational formats(lists,arrays, trees,nets,active networks,etc.).It is widely accepted that these modelstructures ?Published in:COLING84—Proceedings of the10th International Conference on Computational Linguistics,Stanford(Stanford UP)1984,pp.298–301.

do have a more or less ad hoc character and tend to be con?ned to their limited the-oretical or operational performances within a speci?ed approach,subject domain or implemented system.Basically interpretative approaches like these,however,lack the most salient characteristics of more constructive modelstructures that can be developed along the lines of an entity-relationship approach(Chen1980).Their properties of ?exibility and dynamics are needed for automatic meaning representation from input texts to build up and/or modify the realm and scope of their own knowledge,however baseline and vague that may appear compared to human understanding.

In a rather sharp departure from those more orthodox lines of introspective data ac-quisition in meaning and knowledge representation research,the present approach(1) has been based on the algorithmic analysis of discourse that real speakers/writers pro-duce in actual situations of performed or intended communication on a certain subject domain,and(2)the approach makes essential use of the word-usage/entity-relationship paradigm in combination with procedural means to map fuzzy word meanings and their connotative interrelations in a format of stereotypes.Their dynamic dependencies(3) constitute semantic dispositions that render only those conceptual interrelations ac-cessible to automatic processing which can—under di?ering aspects di?erently—be considered relevant.Such dispositional dependency structures(DDS)would seem to be an operational prerequisite to and a promising candidate for the simulation of contents-driven(analogically-associative),instead of formal(logically-deductive)infer-ences in semantic processing.

2The approach

The empirical analysis of discourse and the formal representation of vague word mean-ings in natural language texts as a system of interrelated concepts(Rieger1980)is based on a Wittgenstein ian assumption according to which a great number of texts analysed for any of the employed terms’usage regularities will reveal essential parts of the concepts and hence the meanings conveyed.

It has been shown elsewhere(Rieger1980),that in a su?ciently large sample of pragmatically homogeneous texts,called corpus,only a restricted vocabulary,i.e.a limited number of lexical items will be used by the interlocutors however comprehen-sive their personal vocabularies in general might be.Consequently,the lexical items employed to convey information on a certain subject domain under consideration in the discourse concerned will be distributed according to their conventionalized communica-tive properties,constituting semantic regularities,which may be detected empirically from the texts.

For the quantitative analysis not of propositional strings but of their elements, namely words in natural language texts,rather simple statistics serve the basically descriptive purpose.Developed from and centred around a correlational measure to specify intensities of co-occurring lexical items used in natural language discourse, these analysing algorithms allow for the systematic modelling of a fragment of the lexical structure constituted by the vocabulary employed in the texts as part of the concomitantly conveyed world knowledge.

A correlation coe?cient appropriately modi?ed for the purpose has been used as a mapping function(Rieger1981a).It allows to compute the relational interde-

UNTERNEHM/enterpr0.000

SYSTEM/system 2.035LEIT/guide 2.113

ELEKTR/electron 2.195COMPUTER 2.208

DIPLOM/diploma 2.288VERBAND/assoc 2.299

INDUSTR/industry 2.538STELLE/position 2.620

SUCHE/search 2.772SCHREIB/write 2.791

SCHUL/school 2.922AUFTRAG/order 3.058

FOLGE/consequ 3.135BERUF/professn 3.477

ERFAHR/experienc 3.485UNTERR/instruct 3.586

ORGANISAT/organis 3.846VERWALT/administ 3.952

GEBIET/area 4.055WUNSCH/wish/desir 4.081

......

Table1:Topological environment E UNTERNEHM

pendency of any two lexical items from their textual frequencies.Those items which co-occur frequently in a number of texts will positively be correlated and hence called a?ned,those of which only one(and not the other)frequently occurs in a number of texts will negatively be correlated and hence called repugnant.Di?erent degrees of word-repugnancy and word-a?nity may thus be ascertained without recurring to an investigator’s or his test-persons’word and/or world knowledge(semantic competence), but can instead solely be based upon the usage regularities of lexical items observed in a corpus of pragmatically homogeneous texts,spoken or written by real speakers/hearers in actual or intended acts of communication(communicative performance).

3The semantic space structure

Following a system-theoretic approach and taking each word employed as a potential descriptor to characterize any other word’s virtual meaning,the modi?ed correlation coe?cient can be used to map each lexical item into fuzzy subsets(Zadeh1981)of the vocabulary according to its numerically speci?ed usage regularities.Measuring the di?erences of any one’s lexical item’s usages,represented as fuzzy subsets of the vocabulary,against those of all others allows for a consecutive mapping of items onto another abstract entity of the theoretical construct.These new operationally de?ned entities—called an item’s meanings—may verbally be characterized as a function of all the di?erences of all regularities any one item is used with compared to any other item in the same corpus of discourse.

The resulting system of sets of fuzzy subsets constitutes the semantic space.As a distance-relational datastructure of stereotypically formatted meaning representations it may be interpreted topologically as a hyperspace with a natural metric.Its linguisti-cally labelled elements represent meaning points,and their mutual distances represent meaning di?erences.

The position of a meaning point may be described by its semantic environment. Tab.1shows the topological environment E UNTERNEHM ,i.e.those adjacent points being situated within the hypersphere of a certain diameter around its center meaning

point UNTERNEHM/enterprise as computed from a corpus of German newspaper texts comprising some9000tokens of360types in175texts fron the1964editions of the daily die welt.

Having checked a great number of environments,it was ascertained that they do in fact assemble meaning points of a certain semantic a?nity.Further investigation revealed(Rieger1983)that there are regions of higher point density in the semantic space,forming clouds and clusters.These were detected by multivariate and cluster-analyzing methods which showed,however,that the both,paradigmatically and syn-tagmatically,related items formed what may be named connotative clouds rather than what is known to be called semantic?elds.Although its internal relations appeared to be unspeci?able in terms of any logically deductive or concept hierarchical system, their elements’positions showed high degree of stable structures which suggested a regular form of contents-dependant associative connectedness(Rieger1981b).

4The dispositional dependency

Following a more semiotic understanding of meaning constitution,the present seman-tic space model may become part of a word meaning/world knowledge representation system which separates the format of a basic(stereotype)meaning representation from its latent(dependency)relational organization.Whereas the former is a rather static, topologically structured(associative)memory representing the data that text analysing algorithms provide,the latter can be characterized as a collection of dynamic and?ex-ible structuring processes to reorganize these data under various principles(Rieger 1981b).Other than declarative knowledge that can be represented in pre-de?ned se-mantic network structures,meaning relations of lexical relevance and semantic dispo-sitions which are heavily dependent on context and domain of knowledge concerned will more adequately be de?ned procedurally,i.e.by generative algorithms that induce them on changing data only and whenever necessary.This is achieved by a recursively de?ned procedure that produces hierarchies of meaning points,structured under given aspects according to and in dependence of their meanings’relevancy(Rieger1984b).

Corroborating ideas expressed within the theories spreading activation and the pro-cess of priming studied in cognitive psychology(Lorch1982),a new algorithm has been developed which operates on the semantic space data and generates—other than in Rieger(1982)—dispositional dependency structures(DDS)in the format of n-ary trees.Given one meaning point’s position as a start,the algorithm of least distances(LD)will?rst list all its neighbouring points and stack them by increasing distances,second prime the starting point as head node or root of the DDS-tree to be generated before,third,the algorithm’s generic procedure takes over.It will take the?rst entry from the stack,generate a list of its neighbours,determine from it the least distant one that has already been primed,and identify it as the ancestor-node to which the new point is linked as descendant-node to be primed next.Repeated succesively for each of the meaning points stacked and in turn primed in accordance with this procedure,the algorithm will select a particular fragment of the relational structure latently inherent in the semantic space data and depending on the aspect, i.e.the initially primed meaning point the algorithm is started with.Working its way through and consuming all labeled points in the space structure—unless stopped

Fig.1.1

d Fig.1.2

under conditions of given target nodes,number of nodes to be processed,or threshold of maximum distance—the algorithm transforms prevailing similarities of meanings as represented by adjacent points to establish a binary,non-symmetric,and transitive relation of semantic relevance between them.This relation allows for the hierarchical re-organization of meaning points as nodes under a prisme head in an n-ary DDS-tree (Rieger1984a).

Without introducing the algorithms formally,some of their operative characteristics can well be illustrated in the sequel by a few simpli?ed examples.Beginning with the schema of a distance-like data structure as shown in the two-dimensional con?guration of11points,labeled a to k(Fig.1.1)the stimulation of e.g.points a or c will start the procedure and produce two speci?c selections of distances activated among these 11points(Fig.1.2).The order of how these particular distances are selected can be represented either by steplists(Fig.1.3),or n-ary tree-structures(Fig.1.4),or their binary transformations(Fig.1.5).It is apparent that stimulation of other points within the same con?guration of basic data points will result in similar but nevertheless di?ering trees,depending on the aspect under which the structure is accessed,i.e.the point initially stimulated to start the algorithm with.

Applied to the semantic space data of360de?ned meaning points calculated from the textcorpus of the1964editions of the German newspaper die welt the Disposi-

Step z d z a 0a→a 1e→a 2b→a 3c→b 4f→e 5g→a 6d→b 7h→g 8i→h 9k→b 10j→c Step z d z a 0c→c 1j→c 2i→c

b→c

4h→i

5k→b

→b

7g→h

8d→b

9e→a

10f→e

Fig.1.3

c k

e g

k d

Fig.1.4

tional Dependency Structure(DDS)of UNTERNEHM/enterprise is given in Fig.2as generated by the procedure described.

Beside giving distances between nodes in the DDS-tree,a numerical measure has been devised which describes any node’s degree of relevance according to that tree structure.As a numerical measure,a node’s criteriality is to be calculated with respect to its root or aspect and has been de?ned as a function of both,its distance values and its level in the tree concerned.For a wide range of purposes in processing DDS-trees, di?erent criterialities of nodes can be used to estimate which paths are more likely being taken against others being followed less likely under priming of certain meaning points.Source-oriented,contents-driven search and retrieval procedures may thus be performed e?ectively on the semantic space structure,allowing for the activation of dependency paths.These are to trace those intermediate nodes which determine the associative transitions of any target node under any speci?able aspect.

Using these tracing capabilities within DDS-trees proved particularly promising in an analogical,contents-driven form of automatic inferencing which—as opposed to logical deduction—has operationally been described in Rieger(1984c)and simulated by way of parallel processing of two(or more)dependency-trees.

d k g

i b

a d Fig.1.5

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U N T E R N E H M E N 0.000/1.00S Y S T E M 2.035/.329

L E I T E N 1.425/.188E L E K T R O 0.528/.263C O M P U T E R 0.095/.735D I P L O M 0.115/.865S C H R E I B E N 1.257/.173

K R A N K 2.875/.057

S T R A S S E 4.435/.013S T E U E R 4.427/.003

R E I S E N 3.426/.004

S C H U L E 1.150/.186W U N S C H 1.795/.094

U N T E R R I C H T 1.583.142

V E R W A L T E N 2.650/.090

V E R A N T W O R T E N 2.242/.039E N T W I C K E L N 3.405/.011W I R T S C H A F T 3.459/.011

E R W A R T E N 4.611/.002

K U R Z 4.892/.000B A D 4.650/.000

H A U S 3.983/.000

V E R B A N D 0.734/.204

S U C H E 0.720/.207

H A N D E L 4.774/.002

A U F T R A G 1.923/.089

A U S L A N D 3.043/.004

V O R S I T Z 4.267/.021

I N D U S T R I E 1.104/.230

S T E L L E 2.003/.103K O S T E N 4.644/.022G E B I E T 3.445/.063

H E R R S C H A F T 3.913/.016M A S C H I N E 3.310/.019

B R I T E 3.531/.005

H O C H 4.023/.015

B E R U F 2.521/.115

E R

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I G 1.860/.022

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V E R H A N D E L N 4.559/.050

F O L

G E 3.135/.242

A M T 5.328/.15

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“英语”语言学概论第六章笔记

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英语语用学论文.

The Cooperative Principle of Pragmatics: An Analysis of the Verbal Humour in the Sitcom Home with Kids Since the language was born, the research of it has never interrupted. Humor is the spice of our lives, leave it the life will be boring. Pragmatics is a main branch of Linguistics, with the development of pragmatic theory, linguists and hobbyists from many angles analysis on verbal humor and achieved fruitful results.While cooperative principle, as one of the most important pragmatic principle, makes a systemic study about language use and lays a solid foundation for later development of pragmatics. The violation of cooperative principle can often generate conversational implicature or achieve certain communicative effects. On the basis of previous humour and pragmatic theory research, I will analysis from the point of the cooperative principle of those verbal humour dialogue in the sitcom "Home with Kids", which reflects the daily life of a rebuilt family and attracts many Chinese audiences by its creating writing, good acting as well as its wonderful transcripts. 1. Four Maxims of the Cooperative Principle In 1975, the language philosopher H. P. Grice published a seminal article entitled “The Cooperative Principle” which created quite a stir in the linguis tic world and generated a large number of linguistic publications that are built on Grice's postulates. According to Grice, in conversational exchange, people usually try to reach a common goal by mutual efforts or at least make the conversation develop in the direction of their expectation. To achieve this, people need to cooperate with each other. Grice' theory of the cooperative principle explains how it is possible for the speaker to convey more than what is literally said and foe the hearer to understand. The four maxims of CP are Quantity Maxim, Quality Maxim, Manner Maxim and Relation Maxim. The maxim of quantity has two sub-maxims that require the cooperative

高中英语语法倒装句讲解及练习(附答案)

高中英语倒装句倒装句分为全部倒装和部分倒装 1全部倒装全部倒装是只将句子中的谓语动词全部置于主语之前。此结构通常只用于一般现在时和一般过去时。常见的结构有：1）here, there, now, then, thus等副词置于句首, 谓语动词常用be, come, go, lie, run等表示来去或状态的动词。例如：Then came the chairman. 那时总裁来了。Here is your letter. 你的信。 2). up，down，out，away，in , off, ahead放于句首Up jumped the cat and caught the mouse. Ahead sat an old woman. 3）表示地点，时间，方向等的介词短语放在句首时例如：After the head walked a group of workers. 4.“作表语的现在分词/过去分词/形容词+系动词+主语”形式的完全倒装 Seated in the front were the guests. 注意：上述全部倒装的句型结构的主语必须是名词，如果主语是人称代词则不能完全倒装。例如： Here he comes. 他来了。Away they went. 他们走开了。 2部分倒装部分倒装是指将谓语的一部分如助动词或情态倒装至主语之前。如果句子的谓语没有助动词或情态动词，则需添加助动词do, does或did，并将其置于主语之前。 1. 句首为否定或半否定的词语，如no, not, never, seldom, little, hardly, rarely, scarely, in no way, under no circumstance, at no time决不, not until…等。例如： Never have I seen such a performance. 从未见过如此糟糕的表演。 Nowhere will you find the answer to this question. 无论如何你不会找到这个问题的答案的。 Not until the child fell asleep did the mother leave the room. 母亲一直到孩子入睡后离开房间。当Not until引出主从复合句，主句倒装，从句不倒装。注意：如否定词不在句首不倒装。例如： The mother didn\'t leave the room until the child fell asleep. 典型例题 1）---Why can\'t I smoke here? ----At no time___ in the meeting-room A. is smoking permitted B.smoking is permitted C. smoking is it permitted D.does smoking permit 2）Not until the early years of the 19th century ___ what heat is. A. man did know B. man know C. didn\'t man know D. did man know 2.Not only…but also前面倒装，后面不倒装 Not only did he refuse the gift, he also severely criticized the sender. 他没有收下礼物，还狠狠批评了送礼的人。 3．Hardly/Scarcely…when, No sooner…than等，要倒装。 Hardly had she gone out when a student came to visit her. No sooner had she gone out than a student came to visit her. 典型例题No sooner___ than it began to rain heavily. A. the game began B.has the game begun C. did the game begin D.had the game begun 4so, neither, nor作部分倒装 so/neither/nor+be/can(should/will/could…..)/do(did/does)+sb表示\"也\"、\"也不\" 的句子要部分倒装。例如：典型例题---Do you know Jim quarrelled with his brother? ---I don\'t know, _____. A. nor don\'t I care B. nor do I care C. I don\'t care neither D. I don\'t care also 注意：当so引出的句子用以对上文内容加以证实或肯定时，不可用倒装结构。意为\"的确如此\"。例如： Tom asked me to go to play football and so I did. ---It\'s raining hard.---So it is. 5 only在句首后接状语用总分倒装。例如： Only in this way can you learn English well. 只有这样，你才能学好英语。如果句子为主从复合句，则主句倒装，从句不倒装。例如： Only when he is seriously ill does he ever stay in bed. 病得狠重时，他才卧床休息。注意：如果only后的词组不是状语，不需倒装。

高考数学答题得分的几个窍门_答题技巧

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1.Pragmatics is the study of language in use. Pragmatics is concerned with the study of meaning as communicated by a speaker (or writer) and interpreted by a listener (or reader). Pragmatics is the study of speaker meaning Pragmatics is the study of contextual meaning Pragmatics is the study of the expression of relative distance. Pragmatics is the study of the relationships between linguistic forms and the users of those forms. 2.Syntax is the study of the relationships between linguistic forms, how they are arranged in sequence, and which sequences are well-formed. 3.Semantics is the study of the relationships between linguistic forms and entities in the world; that is, how words literally connect to things. 4.Deixis 指示语is a technical term (from Greek) for one of the most basic things we do with utterances. It means ‘pointing’ via language.Any linguistic form used to accomplish this ‘pointing’ is called a deictic expression. Deictic expressions are also sometimes called indexicals. They are among the first forms to be spoken by very young children and can be used to indicate people via person deixis(such as, ‘me’, ‘you’), or location via spatial deixis(such as ‘here’, ‘there’), or time via temporal deixi s (such as ‘now’, ‘then’). 5.Proximal terms近指are typically interpreted in terms of the speaker’s location, or the deictic center指示中心.‘this’, ‘there’, ‘now’, ‘then’near speaker 6.Distal terms远指can simply indicate ‘away’ from speaker’, but, in some languages, can be used to distinguish between ‘near addres see’ and ‘away from both speaker and addressee’. 7.Person deixis人称指示语clearly operates on a basic three-part division, exemplified例证by the pronouns for first person, second person, and third person./ forms used to point to people, “me””you” 8.Expressions which indicate addressee higher status are described as honorifics敬语. 9.The discussion of the circumstances which lead to the choice of one of these forms rather than another is sometimes described as social deixis./forms used to indicate relative social status 10.A distinction between forms used for familiar versus a non-familiar addressee in some languages. This is known as the T/V distinction. 用复数形态来表示单数敬语，在语言中叫T-V distinction。此概念由1960 年的学者Brown 和Gilman 提出，他们将第二人称单数分为两种形态：T 形态（T-form）和V 形态（V-form），前者在非正式场合、尊称呼卑、关系亲密的人之间使用，后者在正式场合、下级称呼上级、称呼陌生人的时候使用 11.exclusive ‘we’ (speaker plus other(s), excluding addressee)； inclusive ‘we’ (speaker and addressee included). 12.spatial deixis空间指示语- the relative location of people and things is being indicated. Eg, here, there/ forms used to point to location. 13.‘Yonder’那边(more distant from speaker) ‘hither’这边(to this place) ‘thence’从那里(from that place) 14.deictic projection指示投射manipulate speaker’s location eg: I am not here now./speakers acting as if they are somewhere else. 15.psychological distance心理距离I don’t like that. it is ‘invested’ with meaning in a context by a speaker./speaker’s marking of how close or distant something is perceived感知to be. 16.temporal deixis时间指示Back in an hour. the coming week./ forms used to point to location in time 17.It is clear that the present tense is the proximal form近端形式and the past tense is the distal form远端形式. if-clauses 18.In temporal deixis, the remote or distal form can be used to communicate not only distant from current time, but also distant from current reality or facts. 19.Discourse deixis/ textual deixis语篇指示语“the use of expressions within some utterance to refer to some portion 部分of the discourse that contains that utterance (i ncluding the utterance itself)”This is what he did to me. He ripped 撕扯my shirt and hit me on the nose

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The relationship between semantics and pragmatics As a branch of linguistics, semantics can be simply defined as the study of linguistic meaning, which studies literal, structural or lexical meaning conveyed by words, phrases and sentences. What we concerned with is the denotation of the word, namely the relationship between words , phrases and sentences not the possible connotations. It is context independent, de-contextualized.and it deals with what is said. while pragmatics is a study which can be defined as the analysis of meaning in a particular context, which studies non-literal, implicit meaning. It deals with the relations between language and context that are basic to language understanding. pragmatics is context dependent, contextualized. What we concerned is what is communicated by a speaker and interpreted by a listener. Through the definition of semantics and pragmatics, we can see that semantics and pragmatics both concern the meaning of language. But they research the different sides of language, and they have different study methods. The study objects of semantics is words, phrases and sentences, what we need to know is their meaning, sense , reference and presupposes. Different words may have the same or similar meaning, the same one word may have more than one meaning. And one word may belong to another word. So we can conclude the words to these relations, Such as synonymy, polysemy, homonymy,hyponymy and antonymy. And when we talk about the sense relations between sentences ,we may think of presupposes. Whether it is right under the presupposes. All those are semantics. It would not change with the

“人机对话”考试操作及答题技巧

一、考试简介自2008年度起全科医学、临床专业（专业代码为026至084）以及中药学初级（士）、初级（师）、中级（专业代码分别为002、014、091）、中医护理学初级（师）、中级（专业代码分别为016、098）各专业“基础知识”、“相关专业知识”、“ 专业知识”和“专业实践能力”4个科目考试均改用“人机对话”方式进行；其他49个专业4个科目仍采用纸笔作答方式进行考试。在今后考试中会继续增加机考专业范围。人机对话考试是国内外医学考试发展的方向，是借助计算机及网络技术对考试进行实施、管理的一种测试形式；它可以根据考试设计的需求，有针对性地进行命题、组卷，并完成试题呈现、接受答案、计分、数据分析以及结果解释等一系列环节。其特点包括：①形象性：人工智能装臵、模拟系统的运用使得人机对话考试更加生动、直观；②简易性：通过计算机系统，省略了考生涂卡环节及繁杂的评卷工作，节约了大量的时间和费用；③安全性：可随机组卷，将备选答案顺序打乱，有效地防范作弊行为；④科学性：更及时地检测考试的信度和效度，确保考试数据的准确性，排除人为因素的影响，使考试成绩真实可靠。⑤经济性：人机对话考试减少了试卷的印刷、运送等过程，可以节省大量的人力、物力。人机对话考试系统提供的是一种接近“傻瓜式”的操作，在整个考试过程，考生通过简单的键盘和鼠标操作就能完成作答，因此并不需要过多的担心。即便如此，考前熟悉考试系统操作和题型仍然是必要的。

人机对话考试的题型与纸笔考试一样，均为客观选择题。人机对话考试的新题型（案例分析题）将主观题客观化，一方面继承了选择题的优点，如高信度和高效度，快速出成绩，更容易做数据分析并提供信息反馈等；另一方面，案例分析题着重考查考生综合应用知识的能力，通过计算机实现作答的不可逆性，更接近现实临床情景。人机对话考试突破了传统考试方法表达试题形式的限制，它利用声音、录像、图形等多媒体形式，真正做到视觉和听觉相结合，文字和画面相结合，借助典型病例和各种生动的画面(如典型体征、X线、心电图、超声心动图等)营造接近临床实际的环境，通过计算机显示，考生边观察边分析边判断边回答问题，能较全面、真实地反映出考生解决临床问题的能力和水平。人机对话的考试方式还可以准确地控制考试时间，设定的时间一旦用尽后，计算机将自动收卷，任何考生无法继续作答，确保了考试时间对所有考生的公平性。在人机对话考试整个过程中，计算机屏幕下方会显示答题进度和考试剩余时间，方便考生控制答题速度和掌握时间。对于参加考试的卫生专业技术人员而言，除掌握专业知识和专业实践能力外，还应掌握计算机的基本操作，熟悉人机对话考试形式、题型和特点，方能取得好成绩。二、考试操作及答题技巧（一）考试操作