Speech-based information retrieval system with clarification dialogue strategy

关于人工智能的发展情况英语作文英文回答：Artificial intelligence (AI) has evolved significantly over the past few decades, making tremendous strides in various domains. Here are some key developments in AI's growth:1. Machine Learning and Deep Learning:Machine learning and deep learning algorithms have revolutionized AI. These techniques allow computers to learn patterns and make predictions from data without explicit programming. Machine learning models have achieved state-of-the-art performance in image recognition, natural language processing, and speech recognition.2. Natural Language Processing (NLP):NLP has made significant advancements, enablingcomputers to understand and generate human language. AI models can now translate languages, summarize text, answer questions, and engage in conversations. This has led to breakthroughs in customer service, information retrieval, and language-based applications.3. Computer Vision:Computer vision algorithms have made it possible for computers to "see" and interpret visual information. AI models can now identify objects, faces, and scenes with high accuracy. This has applications in security, autonomous vehicles, and medical imaging.4. Robotics:AI has played a crucial role in the advancement of robotics. AI-powered robots can now navigate complex environments, manipulate objects, and interact with humans. They are being used in manufacturing, healthcare, and space exploration.5. Generative AI:Generative AI models, such as GANs (Generative Adversarial Networks), have demonstrated the ability to generate realistic images, text, and music. These modelsare pushing the boundaries of art and entertainment andhave potential applications in content creation and design.6. Edge AI:Edge AI involves deploying AI models on devices like smartphones and embedded systems. This allows for real-time data processing and decision-making at the edge, reducing latency and improving efficiency.7. Ethical and Societal Considerations:As AI advances, it raises ethical and societal concerns regarding privacy, bias, accountability, and job displacement. It is crucial to develop ethical AI practices and policies to ensure responsible and beneficial use of AI.中文回答：人工智能发展情况。

语言学作业班级：姓名：Chapter 1 Invitations to LinguisticsI. Please illustrate the following terms.1. Arbitrariness:The forms of linguistic signs bear no natural relationship to their meaning.The different levels of arbitrariness:(1) Arbitrary relationship between the sound of a morpheme and its meaning, even with onomatopoeic words(2) Arbitrariness at the syntactic level: language is not arbitrary at the syntactic level.(3) The link between a linguistic sign and its meaning is a matter of convention.2. DualityThe property of having two levels of structures, such that units of the primary level are composed of elements of the secondary level and each of the two levels has its own principles of organization.3. Phatic communionPhatic communion refers to the social interaction of language.4. Synchronic linguistics:A synchronic description takes a fixed instant (usually, but not necessarily, the present) as its point of observation. Most grammars are of this kind.II. Please distinguish the following terms:1. Langue vs. ParoleLangue refers to the abstract linguistic system shared by all the members of a speech community, that is, the lexicon, grammar, and phonology implanted in each individual, and it is the linguist’s proper object;Parole refers to the realization of langue, the immediately accessible data. While parole constitutes the immediately accessible data, and it is a mass of confused facts, so it is not suitable for systematic investigation..(1) Langue is abstract, while parole is specific to the situation in which it occurs.(2) Langue is not actually spoken by anyone, while parole is always a naturally occurring event.(3) Langue is relatively stable, systematic and social, while parole is subject to personal, individual and situational constraints.(4) Langue is essential while parole is accessory and accidental.2. Descriptive vs. PrescriptiveThe distinction lies in prescribing how things ought to be and describing how things are.Traditional grammar was very strongly normative in grammarians tried to lay down rules for the correct use of language and settle the disputes over usage once and for all. That is prescriptive.These attitudes are still with us, though people realize nowadays the facts of usage count more than the authority-made “standards”. Thenature of linguistics as a science determines its preoccupation with description instead of prescription.3. Synchronic vs. DiachronicSynchronic description takes a fixed instant (usually, but not necessarily, the present) as its point of observation. Most grammars are of this kind.Actually synchrony is a fiction since any language is changing as the minutes pass.Diachronic linguistics is the study of a language through the course of its history.4. Competence vs. PerformanceAccording to Chomsky:A language user’s underlying knowledge about the system of rules is called his linguistic competence.Performance refers to the actual use of language or the actual realization of this knowledge in utterances in concrete situations.A speaker’s competence is stable while his performance is often influenced by psychological and social factors, so a speaker’s performance does not always or equal his supposed competence.He believes that linguists ought to study competence rather than performance.5. Langue vs. CompetenceAccording to Chomsky:Langue is a social product, a systematic inventory of rules of the language, a set of conventions for a speech community.Competence is defined from the psychological point of view, is deemedas a property of the mind of each individuals, or underlying competence as a system of generative processes.According to Hymes:He approaches language from a socio-cultural viewpoint with the aim of studying the varieties of ways of speaking on the part of individual and the community.He extended notion of competence, restricted by Chomsky to a knowledge of grammar, to incorporate the pragmatic ability for language use. This extended idea of competence can be called communicative competence.III. Answer the following questions in brief:1. The following are some book titles of linguistics. Can you judge thesynchronic or diachronic orientation just from the titles1) English Examined: Two Centuries of Comment on the Mother Tongue2) Protean Shape: A Study in Eighteenth-century Vocabulary and Usage3) Pejorative Sense Development in English4) The Categories and Types of Present-Day English Word-Formation5) Language in the Inner City: Studies in the Black English Vernacular 1) diachronic 2）synchronic 3）diachronic 4）synchronic5）We can’t judge whether it is synchronic or diachronic orientation just from the titles.2. What is language What is linguisticsLanguage can be defined as a system of arbitrary vocal symbols used for human communication and interaction.Linguistics is the scientific study of human language. The aims of linguistic theory: 1) what is knowledge of language (Competence) 2) howis knowledge of language acquired (Acquisition) 3) how is knowledge of language put to use (Performance/language processing). Main branches of linguistics:Phonetics, Phonology Morphology, Syntax, Semantics, Pragmatics.3. How do you understand performative function of languageThe performative function of language is primarily to change the social status of persons or the situations of events, as in marriage ceremonies, the sentencing of criminals, the blessing of children, the naming of a ship at a launching ceremony, and the cursing of enemies.The kind of language employed in performative verbal acts is usually quite formal and even ritualized.The performative function can extend to the control of reality as on some magical or religious occasions.For example, in Chinese when someone breaks a bowl or a plate the host or the people present are likely to say sui sui ping an as a means of controlling the invisible forces which the believers feel might affect their lives adversely.IV. Discuss the following question in detail.How do you interpret the viewpoint that “arbitrariness is a matter of degree”1)Arbitrary relationship between the sound of a morpheme and its meaning, even with onomatopoeic words:The dog barks bow wow in English but “汪汪汪” in Chinese.2) Arbitrariness at the syntactic level: language is not arbitrary at the syntactic level.He came in and sat down.He sat down and came in.He sat down after he came in.3) The link between a linguistic sign and its meaning is a matter of convention.Arbitrariness of language makes it potentiallycreative.Conventionality of language makes learning a languagelaborious.Chapter 2 Speech SoundsI. Complete the following statements.1. Human language enable their users to symbolize objects, events andconcepts which are not present (in time and space) at the moment of communication. This quality is labeled as __________.2. The sound [p] can be described with “voiced, __________, stop.”3. The different members of a phoneme, sounds which are phoneticallydifferent but do not make one word different from another in meaning,, are_________.4. Both semantics and ________ investigate linguistic meaning, but theyfocus on different aspects.5. If certain linguistics tries to lay down rules for the correct useof language and settle the disputes over usage once and for all, it is ___________ linguistics.6. Phones that fall into allophones of a phoneme have to satisfy twoconditions, one is they are ___________________, and another is that they should be in _____________________.7. The vowel ________ is high front tense unrounded.8. A dog cannot tell people that its master will be home in a few days,because its language does not have the feature of ___________.9. Computational linguistics often refers to the problems of________________, information retrieval, and ______________.10. Halliday proposed a theory of metafunctions of language, that is,language has ___________, ____________ and _____________ functions. II. Define the following terms.1. Manner of articulation:2. Distinctive features:3. Intonation:4. Assimilation:III. Answer the following questions briefly.1. Specify the difference between each pair of sounds using distinctivefeatures.1) [l] [ł] 2) [p h] [p] 3) [b] [d] 4) [k] [g] 5) [I][u]2. Work out the features of the following sounds.1) [t h] ________________________________________2) [w] ________________________________________3) [v] ________________________________________4) [ð] _________________________________________5) [l] __________________________________________3. In some dialects of English the following words have different vowels,as shown by the phonetic transcription. Based on these data, answer the questions that follow.A B. Cbite [bʌit] bide [ba i d] tie [ta i]rice [rʌis] rise [ra i z] by [ba i]type [tʌip] bribe [b r aib] sigh [s a i]wife [wʌif] wives [wa i vz] die [d a i]tyke [tʌik] time [ta i m] why [wa i]1) What is the difference of the sounds that end the words in columnsA and B2) How do the words in column C differ from those in column A and B3) Are [ʌi] and [a i] in complementary distribution Give your reasons.4) What are the phonetic transcriptions of (a) life and (b) lives5) What would the phonetic transcriptions of the following words be inthe dialects of English shown in the data(a) trial (b) bike (c) lice(d) fly (e) mine6) State the rule that will relate the phonemic representations to bephonetic transcriptions of the words given above.IV. Discuss the questions in details.1. Illustrate phoneme, phone and allophone.2. To what extent is phonology related ot phonetics and how do they differ。

multilingual processing system 多语讯息处理系统multilingual translation 多语翻译multimedia 多媒体multi-media communication 多媒体通讯multiple inheritance 多重继承multistate logic 多态逻辑mutation 语音转换mutual exclusion 互斥mutual information 相互讯息nativist position 语法天生假说natural language 自然语言natural language processing (nlp) 自然语言处理natural language understanding 自然语言理解negation 否定negative sentence 否定句neologism 新词语nested structure 崁套结构network 网络neural network 类神经网络neurolinguistics 神经语言学neutralization 中立化n-gram n-连词n-gram modeling n-连词模型nlp (natural language processing) 自然语言处理node 节点nominalization 名物化nonce 暂用的non-finite 非限定non-finite clause 非限定式子句non-monotonic reasoning 非单调推理normal distribution 常态分布noun 名词noun phrase 名词组np (noun phrase) completeness 名词组完全性object 宾语{语言学}/对象{信息科学}object oriented programming 对象导向程序设计[面向对向的程序设计]official language 官方语言one-place predicate 一元述语on-line dictionary 线上查询词典 [联机词点]onomatopoeia 拟声词onset 节首音ontogeny 个体发生ontology 本体论open set 开放集operand 操作数 [操作对象]optimization 最佳化 [最优化]overgeneralization 过度概化overgeneration 过度衍生paradigmatic relation 聚合关系paralanguage 附语言parallel construction 并列结构parallel corpus 平行语料库parallel distributed processing (pdp) 平行分布处理paraphrase 转述 [释意;意译;同意互训]parole 言语parser 剖析器 [句法剖析程序]parsing 剖析part of speech (pos) 词类particle 语助词part-of relation part-of 关系part-of-speech tagging 词类标注pattern recognition 型样识别p-c (predicate-complement) insertion 述补中插pdp (parallel distributed processing) 平行分布处理perception 知觉perceptron 感觉器 [感知器]perceptual strategy 感知策略performative 行为句periphrasis 用独立词表达perlocutionary 语效性的permutation 移位petri net grammar petri 网语法philology 语文学phone 语音phoneme 音素phonemic analysis 因素分析phonemic stratum 音素层phonetics 语音学phonogram 音标phonology 声韵学 [音位学;广义语音学] phonotactics 音位排列理论phrasal verb 词组动词 [短语动词]phrase 词组 [短语]phrase marker 词组标记 [短语标记]pitch 音调pitch contour 调形变化pivot grammar 枢轴语法pivotal construction 承轴结构plausibility function 可能性函数pm (phrase marker) 词组标记 [短语标记] polysemy 多义性pos-tagging 词类标记postposition 方位词pp (preposition phrase) attachment 介词依附pragmatics 语用学precedence grammar 优先级语法precision 精确度predicate 述词predicate calculus 述词计算predicate logic 述词逻辑 [谓词逻辑]predicate-argument structure 述词论元结构prefix 前缀premodification 前置修饰preposition 介词prescriptive linguistics 规定语言学 [规范语言学] presentative sentence 引介句presupposition 前提principle of compositionality 语意合成性原理privative 二元对立的probabilistic parser 概率句法剖析程序problem solving 解决问题program 程序programming language 程序设计语言 [程序设计语言] proofreading system 校对系统proper name 专有名词prosody 节律prototype 原型pseudo-cleft sentence 准分裂句psycholinguistics 心理语言学punctuation 标点符号pushdown automata 下推自动机pushdown transducer 下推转换器qualification 后置修饰quantification 量化quantifier 范域词quantitative linguistics 计量语言学question answering system 问答系统queue 队列radical 字根 [词干;词根;部首;偏旁]radix of tuple 元组数基random access 随机存取rationalism 理性论rationalist (position) 理性论立场 [唯理论观点]reading laboratory 阅读实验室real time 实时real time control 实时控制 [实时控制]recursive transition network 递归转移网络reduplication 重叠词 [重复]reference 指涉referent 指称对象referential indices 指针referring expression 指涉词 [指示短语]register 缓存器[寄存器]{信息科学}/调高{语音学}/语言的场合层级{社会语言学}regular language 正规语言 [正则语言]relational database 关系型数据库 [关系数据库]relative clause 关系子句relaxation method 松弛法relevance 相关性restricted logic grammar 受限逻辑语法resumptive pronouns 复指代词retroactive inhibition 逆抑制rewriting rule 重写规则rheme 述位rhetorical structure 修辞结构rhetorics 修辞学robust 强健性robust processing 强健性处理robustness 强健性schema 基朴school grammar 教学语法scope 范域 [作用域;范围]script 脚本search mechanism 检索机制search space 检索空间searching route 检索路径 [搜索路径]second order predicate 二阶述词segmentation 分词segmentation marker 分段标志selectional restriction 选择限制semantic field 语意场semantic frame 语意架构semantic network 语意网络semantic representation 语意表征 [语义表示] semantic representation language 语意表征语言semantic restriction 语意限制semantic structure 语意结构semantics 语意学sememe 意素semiotics 符号学sender 发送者sensorimotor stage 感觉运动期sensory information 感官讯息 [感觉信息]sentence 句子sentence generator 句子产生器 [句子生成程序]sentence pattern 句型separation of homonyms 同音词区分sequence 序列serial order learning 顺序学习serial verb construction 连动结构set oriented semantic network 集合导向型语意网络 [面向集合型语意网络]sgml (standard generalized markup language) 结构化通用标记语言shift-reduce parsing 替换简化式剖析short term memory 短程记忆sign 信号signal processing technology 信号处理技术simple word 单纯词situation 情境situation semantics 情境语意学situational type 情境类型social context 社会环境sociolinguistics 社会语言学software engineering 软件工程 [软件工程]sort 排序speaker-independent speech recognition 非特定语者语音识别spectrum 频谱speech 口语speech act assignment 言语行为指定speech continuum 言语连续体speech disorder 语言失序 [言语缺失]speech recognition 语音辨识speech retrieval 语音检索speech situation 言谈情境 [言语情境]speech synthesis 语音合成speech translation system 语音翻译系统speech understanding system 语音理解系统spreading activation model 扩散激发模型standard deviation 标准差standard generalized markup language 标准通用标示语言start-bound complement 接头词state of affairs algebra 事态代数state transition diagram 状态转移图statement kernel 句核static attribute list 静态属性表statistical analysis 统计分析statistical linguistics 统计语言学statistical significance 统计意义stem 词干stimulus-response theory 刺激反应理论stochastic approach to parsing 概率式句法剖析 [句法剖析的随机方法]stop 爆破音stratificational grammar 阶层语法 [层级语法]string 字符串[串；字符串]string manipulation language 字符串操作语言string matching 字符串匹配 [字符串]structural ambiguity 结构歧义structural linguistics 结构语言学structural relation 结构关系structural transfer 结构转换structuralism 结构主义structure 结构structure sharing representation 结构共享表征subcategorization 次类划分 [下位范畴化] subjunctive 假设的sublanguage 子语言subordinate 从属关系subordinate clause 从属子句 [从句;子句] subordination 从属substitution rule 代换规则 [置换规则] substrate 底层语言suffix 后缀superordinate 上位的superstratum 上层语言suppletion 异型[不规则词型变化] suprasegmental 超音段的syllabification 音节划分syllable 音节syllable structure constraint 音节结构限制symbolization and verbalization 符号化与字句化synchronic 同步的synonym 同义词syntactic category 句法类别syntactic constituent 句法成分syntactic rule 语法规律 [句法规则]syntactic semantics 句法语意学syntagm 句段syntagmatic 组合关系 [结构段的;组合的] syntax 句法systemic grammar 系统语法tag 标记target language 目标语言 [目标语言]task sharing 课题分享 [任务共享] tautology 套套逻辑 [恒真式;重言式;同义反复] taxonomical hierarchy 分类阶层 [分类层次] telescopic compound 套装合并template 模板temporal inference 循序推理 [时序推理] temporal logic 时间逻辑 [时序逻辑] temporal marker 时貌标记tense 时态terminology 术语text 文本text analyzing 文本分析text coherence 文本一致性text generation 文本生成 [篇章生成]text linguistics 文本语言学text planning 文本规划text proofreading 文本校对text retrieval 文本检索text structure 文本结构 [篇章结构]text summarization 文本自动摘要 [篇章摘要] text understanding 文本理解text-to-speech 文本转语音thematic role 题旨角色thematic structure 题旨结构theorem 定理thesaurus 同义词辞典theta role 题旨角色theta-grid 题旨网格token 实类 [标记项]tone 音调tone language 音调语言tone sandhi 连调变换top-down 由上而下 [自顶向下]topic 主题topicalization 主题化 [话题化]trace 痕迹trace theory 痕迹理论training 训练transaction 异动 [处理单位]transcription 转写 [抄写;速记翻译]transducer 转换器transfer 转移transfer approach 转换方法transfer framework 转换框架transformation 变形 [转换]transformational grammar 变形语法 [转换语法] transitional state term set 转移状态项集合transitivity 及物性translation 翻译translation equivalence 翻译等值性translation memory 翻译记忆transparency 透明性tree 树状结构 [树]tree adjoining grammar 树形加接语法 [树连接语法] treebank 树图数据库[语法关系树库]trigram 三连词t-score t-数turing machine 杜林机 [图灵机]turing test 杜林测试 [图灵试验]type 类型type/token node 标记类型/实类节点type-feature structure 类型特征结构typology 类型学ultimate constituent 终端成分unbounded dependency 无界限依存underlying form 基底型式underlying structure 基底结构unification 连并 [合一]unification-based grammar 连并为本的语法 [基于合一的语法] universal grammar 普遍性语法universal instantiation 普遍例式universal quantifier 全称范域词unknown word 未知词 [未定义词]unrestricted grammar 非限制型语法usage flag 使用旗标user interface 使用者界面 [用户界面]valence grammar 结合价语法valence theory 结合价理论valency 结合价variance 变异数 [方差]verb 动词verb phrase 动词组 [动词短语]verb resultative compound 动补复合词verbal association 词语联想verbal phrase 动词组verbal production 言语生成vernacular 本地话v-o construction (verb-object) 动宾结构vocabulary 字汇vocabulary entry 词条vocal track 声道vocative 呼格voice recognition 声音辨识 [语音识别]vowel 元音vowel harmony 元音和谐 [元音和谐]waveform 波形weak verb 弱化动词whorfian hypothesis whorfian 假说word 词word frequency 词频word frequency distribution 词频分布word order 词序word segmentation 分词word segmentation standard for chinese 中文分词规范word segmentation unit 分词单位 [切词单位]word set 词集working memory 工作记忆 [工作存储区]world knowledge 世界知识writing system 书写系统x-bar theory x标杠理论 ["x"阶理论]zipf's law 利夫规律 [齐普夫定律]。

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AI会议的总结（by南大周志华）说明: 纯属个人看法, 仅供参考. tier-1的列得较全, tier-2的不太全, tier-3的很不全.同分的按字母序排列. 不很严谨地说, tier-1是可以令人羡慕的, tier-2是可以令人尊敬的,由于AI的相关会议非常多, 所以能列进tier-3的也是不错的tier-1:IJCAI (1+): International Joint Conference on Artificial Intelligence AAAI (1): National Conference on Artificial IntelligenceCOLT (1): Annual Conference on Computational Learning TheoryCVPR (1): IEEE International Conference on Computer Vision and Pattern RecognitionICCV (1): IEEE International Conference on Computer VisionICML (1): International Conference on Machine LearningNIPS (1): Annual Conference on Neural Information Processing SystemsACL (1-): Annual Meeting of the Association for Computational LinguisticsKR (1-): International Conference on Principles of Knowledge Representation and ReasoningSIGIR (1-): Annual International ACM SIGIR Conference on Research and Developm ent in Information RetrievalSIGKDD (1-): ACM SIGKDD International Conference on Knowledge Discovery and Data MiningUAI (1-): International Conference on Uncertainty in Artificial Intelligence*Impact factor (According to Citeseer 03):IJCAI ：1.82 (top 4.09 %)AAAI ：1.49 (top 9.17%)COLT：1.49 (top 9.25%)ICCV ：1.78 (top 4.75%)ICML ：2.12 (top 1.88%)NIPS ：1.06 (top 20.96%)ACL ：1.44 (top 10.07%)KR ：1.76 (top 4.99%)SIGIR ：1.10 (top 19.08%)Average：1.56 (top 8.02%)IJCAI (1+): AI最好的综合性会议, 1969年开始, 每两年开一次, 奇数年开. 因为AI 实在太大, 所以虽然每届基本上能录100多篇（现在已经到200多篇了），但分到每个领域就没几篇了，象achine learning、computer vision这么大的领域每次大概也就10篇左右, 所以难度很大. 不过从录用率上来看倒不太低,基本上20%左右, 因为内行人都会掂掂分量, 没希望的就别浪费reviewer的时间了. 最近中国大陆投往国际会议的文章象潮水一样, 而且因为国内很少有能自己把关的研究组, 所以很多会议都在complain说中国的低质量文章严重妨碍了PC的工作效率. 在这种情况下, 估计这几年国际会议的录用率都会降下去. 另外, 以前的IJCAI是没有poster的, 03年开始, 为了减少被误杀的好人, 增加了2页纸的poster.值得一提的是, IJCAI是由貌似一个公司的”IJCAI Inc.”主办的(当然实际上并不是公司, 实际上是个基金会), 每次会议上要发几个奖, 其中最重要的两个是IJCAI Research Excellence Award 和Computer & Thoughts Award, 前者是终身成就奖, 每次一个人, 基本上是AI的最高奖(有趣的是, 以AI为主业拿图灵奖的6位中, 有2位还没得到这个奖), 后者是奖给35岁以下的青年科学家, 每次一个人. 这两个奖的获奖演说是每次IJCAI的一个重头戏.另外, IJCAI 的PC member 相当于其他会议的area chair, 权力很大, 因为是由PC member 去找reviewer 来审, 而不象一般会议的PC member 其实就是reviewer. 为了制约这种权力, IJCAI的审稿程序是每篇文章分配2位PC member, primary PC member去找3位reviewer, second PC member 找一位.AAAI (1): 美国人工智能学会AAAI的年会. 是一个很好的会议, 但其档次不稳定, 可以给到1+, 也可以给到1-或者2+, 总的来说我给它”1″. 这是因为它的开法完全受IJCAI制约: 每年开, 但如果这一年的IJCAI在北美举行, 那么就停开. 所以, 偶数年里因为没有IJCAI, 它就是最好的AI综合性会议,但因为号召力毕竟比IJCAI要小一些, 特别是欧洲人捧AAAI场的比IJCAI少得多(其实亚洲人也是), 所以比IJCAI还是要稍弱一点, 基本上在1和1+之间; 在奇数年, 如果IJCAI不在北美, AAAI自然就变成了比IJCAI低一级的会议(1-或2+), 例如2005年既有IJCAI又有AAAI, 两个会议就进行了协调, 使得IJCAI的录用通知时间比AAAI的deadline早那么几天, 这样IJCAI落选的文章可以投往AAAI.在审稿时IJCAI 的PC chair也在一直催, 说大家一定要快, 因为AAAI 那边一直在担心IJCAI的录用通知出晚了AAAI就麻烦了.COLT (1): 这是计算学习理论最好的会议, ACM主办, 每年举行. 计算学习理论基本上可以看成理论计算机科学和机器学习的交叉, 所以这个会被一些人看成是理论计算机科学的会而不是AI的会. 我一个朋友用一句话对它进行了精彩的刻画: “一小群数学家在开会”. 因为COLT的领域比较小, 所以每年会议基本上都是那些人. 这里顺便提一件有趣的事, 因为最近国内搞的会议太多太滥, 而且很多会议都是LNCS/LNAI出论文集, LNCS/LNAI基本上已经被搞臭了, 但很不幸的是, LNCS/LNAI中有一些很好的会议, 例如COLT.CVPR (1): 计算机视觉和模式识别方面最好的会议之一, IEEE主办, 每年举行. 虽然题目上有计算机视觉, 但个人认为它的模式识别味道更重一些. 事实上它应该是模式识别最好的会议, 而在计算机视觉方面, 还有ICCV 与之相当. IEEE一直有个倾向, 要把会办成”盛会”, 历史上已经有些会被它从quality很好的会办成”盛会”了. CVPR搞不好也要走这条路. 这几年录的文章已经不少了. 最近负责CVPR会议的TC的chair发信说, 对这个community来说, 让好人被误杀比被坏人漏网更糟糕, 所以我们是不是要减少好人被误杀的机会啊? 所以我估计明年或者后年的CVPR就要扩招了.ICCV (1): 介绍CVPR的时候说过了, 计算机视觉方面最好的会之一. IEEE主办, 每年举行.ICML (1): 机器学习方面最好的会议之一. 现在是IMLS主办, 每年举行. 参见关于NIPS的介绍. NIPS (1): 神经计算方面最好的会议之一, NIPS主办, 每年举行. 值得注意的是, 这个会每年的举办地都是一样的, 以前是美国丹佛, 现在是加拿大温哥华; 而且它是年底开会, 会开完后第2年才出论文集,也就是说, NIPS’05的论文集是06年出. 会议的名字“Advances in Neural Information ProcessingSystems”, 所以, 与ICML\ECML这样的”标准的”机器学习会议不同, NIPS里有相当一部分神经科学的内容, 和机器学习有一定的距离. 但由于会议的主体内容是机器学习, 或者说与机器学习关系紧密, 所以不少人把NIPS看成是机器学习方面最好的会议之一. 这个会议基本上控制在Michael Jordan的徒子徒孙手中, 所以对Jordan系的人来说, 发NIPS并不是难事, 一些未必很强的工作也能发上去, 但对这个圈子之外的人来说, 想发一篇实在很难, 因为留给”外人”的口子很小. 所以对Jordan系以外的人来说, 发NIPS的难度比ICML更大. 换句话说, ICML比较开放, 小圈子的影响不象NIPS那么大, 所以北美和欧洲人都认, 而NIPS则有些人(特别是一些欧洲人, 包括一些大家)坚决不投稿. 这对会议本身当然并不是好事, 但因为Jordan系很强大, 所以它似乎也不太care. 最近IMLS(国际机器学习学会)改选理事, 有资格提名的人包括近三年在ICML\ECML\COLT发过文章的人, NIPS则被排除在外了. 无论如何, 这是一个非常好的会.ACL (1-): 计算语言学/自然语言处理方面最好的会议, ACL (Association of Computational Linguistics) 主办, 每年开.KR (1-): 知识表示和推理方面最好的会议之一, 实际上也是传统AI(即基于逻辑的AI) 最好的会议之一. KR Inc.主办, 现在是偶数年开.SIGIR (1-): 信息检索方面最好的会议, ACM主办, 每年开. 这个会现在小圈子气越来越重. 信息检索应该不算AI, 不过因为这里面用到机器学习越来越多, 最近几年甚至有点机器学习应用会议的味道了, 所以把它也列进来.SIGKDD (1-): 数据挖掘方面最好的会议, ACM主办, 每年开. 这个会议历史比较短, 毕竟, 与其他领域相比,数据挖掘还只是个小弟弟甚至小侄儿. 在几年前还很难把它列在tier-1里面, 一方面是名声远不及其他的top conference响亮, 另一方面是相对容易被录用. 但现在它被列在tier-1应该是毫无疑问的事情了.UAI (1-): 名字叫”人工智能中的不确定性”, 涉及表示\推理\学习等很多方面, AUAI (Association of UAI) 主办, 每年开.tier-2:AAMAS (2+): International Joint Conference on Autonomous Agents and Multiagent SystemsECCV (2+): European Conference on Computer VisionECML (2+): European Conference on Machine LearningICDM (2+): IEEE International Conference on Data MiningSDM (2+): SIAM International Conference on Data MiningICAPS (2): International Conference on Automated Planning and SchedulingICCBR (2): International Conference on Case-Based ReasoningCOLLING (2): International Conference on Computational LinguisticsECAI (2): European Conference on Artificial IntelligenceALT (2-): International Conference on Algorithmic Learning TheoryEMNLP (2-): Conference on Empirical Methods in Natural Language ProcessingILP (2-): International Conference on Inductive Logic ProgrammingPKDD (2-): European Conference on Principles and Practice of Knowledge Discovery in Databases*Impact factor (According to Citeseer 03):ECCV ：1.58 (top 7.20 %)ECML ：0.83 (top 30.63 %)ICDM ：0.35 (top 59.86 %)ICCBR ：0.72 (top 36.69 %)ECAI ：0.69 (top 38.49 %)ALT ：0.63 (top 42.91 %)ILP ：1.06 (top 20.80 %)PKDD ：0.50 (top 51.26 %)Average：0.80 (top 32.02%)AAMAS (2+): agent方面最好的会议. 但是现在agent已经是一个一般性的概念, 几乎所有AI有关的会议上都有这方面的内容, 所以AAMAS下降的趋势非常明显.ECCV (2+): 计算机视觉方面仅次于ICCV的会议, 因为这个领域发展很快, 有可能升级到1-去.ECML (2+): 机器学习方面仅次于ICML的会议, 欧洲人极力捧场, 一些人认为它已经是1-了. 我保守一点, 仍然把它放在2+. 因为机器学习发展很快, 这个会议的reputation上升非常明显.ICDM (2+): 数据挖掘方面仅次于SIGKDD的会议, 目前和SDM相当. 这个会只有5年历史, 上升速度之快非常惊人. 几年前ICDM还比不上PAKDD, 现在已经拉开很大距离了.SDM (2+): 数据挖掘方面仅次于SIGKDD的会议, 目前和ICDM相当. SIAM的底子很厚, 但在CS里面的影响比ACM和IEEE还是要小, SDM眼看着要被ICDM超过了, 但至少目前还是相当的.ICAPS (2): 人工智能规划方面最好的会议, 是由以前的国际和欧洲规划会议合并来的. 因为这个领域逐渐变冷清, 影响比以前已经小了.ICCBR (2): Case-Based Reasoning方面最好的会议. 因为领域不太大, 而且一直半冷不热, 所以总是停留在2上.COLLING (2): 计算语言学/自然语言处理方面仅次于ACL的会, 但与ACL的差距比ICCV-ECCV和ICML-ECML大得多.ECAI (2): 欧洲的人工智能综合型会议, 历史很久, 但因为有IJCAI/AAAI压着,很难往上升.ALT (2-): 有点象COLT的tier-2版, 但因为搞计算学习理论的人没多少, 做得好的数来数去就那么些group, 基本上到COLT去了, 所以ALT里面有不少并非计算学习理论的内容.EMNLP (2-): 计算语言学/自然语言处理方面一个不错的会. 有些人认为与COLLING相当, 但我觉得它还是要弱一点.ILP (2-): 归纳逻辑程序设计方面最好的会议. 但因为很多其他会议里都有ILP方面的内容, 所以它只能保住2-的位置了.PKDD (2-): 欧洲的数据挖掘会议, 目前在数据挖掘会议里面排第4. 欧洲人很想把它抬起来, 所以这些年一直和ECML一起捆绑着开, 希望能借ECML把它带起来.但因为ICDM和SDM, 这已经不太可能了. 所以今年的PKDD和ECML虽然还是一起开, 但已经独立审稿了(以前是可以同时投两个会, 作者可以声明优先被哪个会考虑, 如果ECML中不了还可以被 PKDD接受).tier-3:ACCV (3+): Asian Conference on Computer VisionDS (3+): International Conference on Discovery ScienceECIR (3+): European Conference on IR ResearchICTAI (3+): IEEE International Conference on Tools with Artificial IntelligencePAKDD (3+): Pacific-Asia Conference on Knowledge Discovery and Data MiningICANN (3+): International Conference on Artificial Neural NetworksAJCAI (3): Australian Joint Conference on Artificial IntelligenceCAI (3): Canadian Conference on Artificial IntelligenceCEC (3): IEEE Congress on Evolutionary ComputationFUZZ-IEEE (3): IEEE International Conference on Fuzzy SystemsGECCO (3): Genetic and Evolutionary Computation ConferenceICASSP (3): International Conference on Acoustics, Speech, and Signal ProcessingICIP (3): International Conference on Image ProcessingICPR (3): International Conference on Pattern RecognitionIEA/AIE (3): International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert SystemsIJCNN (3): International Joint Conference on Neural NetworksIJNLP (3): International Joint Conference on Natural Language ProcessingPRICAI (3): Pacific-Rim International Conference on Artificial Intelligence*Impact factor (According to Citeseer 03):ACCV ：0.42 (top 55.61%)ICTAI ：0.25 (top 69.86 %)PAKDD ：0.30(top 65.60 %)ICANN ：0.27 (top 67.73 %)AJCAI ：0.16 (top 79.44 %)CAI ：0.26 (top 68.87 %)ICIP ：0.50 (top 50.20 %)IEA/AIE ：0.09 (top 87.79 %)PRICAI ：0.19 (top 76.33 %)Average：0.27 (top 68.30%)ACCV (3+): 亚洲的计算机视觉会议, 在亚太级别的会议里算很好的了.DS (3+): 日本人发起的一个接近数据挖掘的会议.ECIR (3+): 欧洲的信息检索会议, 前几年还只是英国的信息检索会议.ICTAI (3+): IEEE最主要的人工智能会议, 偏应用, 是被IEEE办烂的一个典型. 以前的quality还是不错的, 但是办得越久声誉反倒越差了, 糟糕的是似乎还在继续下滑, 现在其实3+已经不太呆得住了.PAKDD (3+): 亚太数据挖掘会议, 目前在数据挖掘会议里排第5.ICANN (3+): 欧洲的神经网络会议, 从quality来说是神经网络会议中最好的, 但这个领域的人不重视会议,在该领域它的重要性不如IJCNN.AJCAI (3): 澳大利亚的综合型人工智能会议, 在国家/地区级AI会议中算不错的了.CAI (3): 加拿大的综合型人工智能会议, 在国家/地区级AI会议中算不错的了.CEC (3): 进化计算方面最重要的会议之一, 盛会型. IJCNN/CEC /FUZZ-IEEE这三个会议是计算智能或者说软计算方面最重要的会议, 它们经常一起开, 这时就叫WCCI (World Congress on Computational Intelligence). 但这个领域和CS其他分支不太一样, 倒是和其他学科相似, 只重视journal, 不重视会议, 所以录用率经常在85%左右, 所录文章既有quality非常高的论文, 也有入门新手的习作.FUZZ-IEEE (3): 模糊方面最重要的会议, 盛会型, 参见CEC的介绍.GECCO (3): 进化计算方面最重要的会议之一, 与CEC相当，盛会型.ICASSP (3): 语音方面最重要的会议之一, 这个领域的人也不很care会议.ICIP (3): 图像处理方面最著名的会议之一, 盛会型.ICPR (3): 模式识别方面最著名的会议之一, 盛会型.IEA/AIE (3): 人工智能应用会议. 一般的会议提名优秀论文的通常只有几篇文章, 被提名就已经是很高的荣誉了, 这个会很有趣, 每次都搞1、20篇的优秀论文提名, 专门搞几个session做被提名论文报告, 倒是很热闹.IJCNN (3): 神经网络方面最重要的会议, 盛会型, 参见CEC的介绍.IJNLP (3): 计算语言学/自然语言处理方面比较著名的一个会议.PRICAI (3): 亚太综合型人工智能会议, 虽然历史不算短了, 但因为比它好或者相当的综合型会议太多, 所以很难上升.列list只是为了帮助新人熟悉领域, 给出的评分或等级都是个人意见, 仅供参考. 特别要说明的是:1. tier-1 conference上的文章并不一定比tier-3的好, 只能说前者的平均水准更高.2. 研究工作的好坏不是以它发表在哪儿来决定的, 发表在高档次的地方只是为了让工作更容易被同行注意到. tier-3会议上发表1篇被引用10次的文章可能比在tier-1会议上发表10篇被引用0次的文章更有价值. 所以, 数top会议文章数并没有太大意义, 重要的是同行的评价和认可程度.3. 很多经典工作并不是发表在高档次的发表源上, 有不少经典工作甚至是发表在很低档的发表源上. 原因很多, 就不细说了.4. 会议毕竟是会议, 由于审稿时间紧, 错杀好人和漏过坏人的情况比比皆是, 更何况还要考虑到有不少刚开始做研究的学生在代老板审稿.5. 会议的reputation并不是一成不变的，新会议可能一开始没什么声誉，但过几年后就野鸡变凤凰，老会议可能原来声誉很好，但越来越往下滑.6. 只有计算机科学才重视会议论文, 其他学科并不把会议当回事. 但在计算机科学中也有不太重视会议的分支.7. Politics无所不在. 你老板是谁, 你在哪个研究组, 你在哪个单位, 这些简单的因素都可能造成决定性的影响. 换言之, 不同环境的人发表的难度是不一样的. 了解到这一点后, 你可能会对high-level发表源上来自low-level单位名不见经传作者的文章特别注意(例如如果<计算机学报>上发表了平顶山铁道电子信息科技学院的作者的文章,我一定会仔细读).8. 评价体系有巨大的影响. 不管是在哪儿谋生的学者, 都需要在一定程度上去迎合评价体系, 否则连生路都没有了, 还谈什么做研究. 以国内来说, 由于评价体系只重视journal, 有一些工作做得很出色的学者甚至从来不投会议. 另外, 经费也有巨大的制约作用. 国外很多好的研究组往往是重要会议都有文章. 但国内是不行的, 档次低一些的会议还可以投了只交注册费不开会, 档次高的会议不去做报告会有很大的负面影响, 所以只能投很少的会议. 这是在国内做CS研究最不利的地方. 我的一个猜想：人民币升值对国内CS研究会有不小的促进作用(当然, 人民币升值对整个中国来说利大于弊还是弊大于利很难说).。

Abbreviated Journal Title;ISSN;2004 Total Cites;Impact Factor;Immediacy Index;2004 Articles;Cited Half-LifeACM COMPUT SURV;0360-0300;1469;10.037;0.083;12;6.4;J MACH LEARN RES;1532-4435;621;5.952;0.438;48;2.5;BIOINFORMA TICS;1367-4803;11390;5.742;0.715;564;3.4;HUM-COMPUT INTERACT;0737-0024;610;4.778;2.917;12;7.0;IEEE T PATTERN ANAL;0162-8828;12485;4.352;0.416;154;8.5;ANNU REV INFORM SCI;0066-4200;296;4.292;0.333;12;5.7;ACM T INFORM SYST;1046-8188;914;4.097;1.421;19;6.2;VLDB J;1066-8888;755;4.000;0.333;21;4.0;IEEE T MED IMAGING;0278-0062;6297;3.922;0.426;141;5.9;IEEE T EVOLUT COMPUT;1089-778X;1012;3.688;0.421;38;4.6;ARTIF INTELL;0004-3702;5632;3.570;0.625;64;9.8;MACH LEARN;0885-6125;3610;3.258;0.925;40;8.0;J COMPUT BIOL;1066-5277;1467;3.241;0.130;69;4.2;MED IMAGE ANAL;1361-8415;1057;3.212;0.389;36;5.4;EVOL COMPUT;1063-6560;1083;3.206;0.643;14;6.4;J MOL GRAPH MODEL;1093-3263;2579;3.036;0.362;58;8.3;QUANTUM INFORM COMPU;1533-7146;427;3.035;1.345;29;2.3;INT J COMPUT 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COMMUN;1536-1284;228;2.189;0.154;39;2.4;IEEE T NEURAL NETWOR;1045-9227;4682;2.178;0.304;135;7.1;PATTERN RECOGN;0031-3203;5667;2.176;0.300;203;7.8;ARTIF LIFE;1064-5462;546;2.150;0.846;26;5.9;BIOL CYBERN;0340-1200;3346;2.142;0.321;78;>10.0;J AM SOC INF SCI TEC;1532-2882;2254;2.086;0.216;97;6.2;J ARTIF INTELL RES;1076-9757;946;2.045;0.382;34;6.6;IEEE T INFORM THEORY;0018-9448;11182;2.029;0.241;324;9.4; THEOR PRACT LOG PROG;1471-0684;109;2.024;0.350;20;2.2; IEEE T IMAGE PROCESS;1057-7149;4950;2.011;0.321;134;6.2; COMPUT CHEM;0097-8485;864;1.923;;0;7.2;COMPUT INTELL;0824-7935;560;1.923;0.576;33;7.4;ACM T COMPUT SYST;0734-2071;788;1.917;0.000;10;>10.0; CHEMOMETR INTELL LAB;0169-7439;2282;1.899;0.148;115;6.7; QSAR COMB SCI;1611-020X;145;1.882;0.224;76;1.4;ACM T PROGR LANG SYS;0164-0925;1294;1.875;0.111;27;>10.0; COMMUN ACM;0001-0782;7907;1.865;0.137;161;>10.0; DISTRIB PARALLEL DAT;0926-8782;255;1.850;0.389;18;5.2; ACM T DATABASE SYST;0362-5915;870;1.846;0.286;21;>10.0; INFORM MANAGE-AMSTER;0378-7206;1013;1.815;0.056;72;5.4; J COMPUT PHYS;0021-9991;11917;1.777;0.258;306;>10.0; NEURAL NETWORKS;0893-6080;3474;1.736;0.248;105;8.3; IEEE T VIS COMPUT GR;1077-2626;666;1.694;0.213;61;5.5; COMPUT CHEM ENG;0098-1354;3806;1.678;0.245;237;7.0; COMPUT LINGUIST;0891-2017;803;1.657;0.125;16;9.2; COMPUT BIOL CHEM;1476-9271;106;1.655;0.333;42;1.4;J MOL MODEL;0948-5023;604;1.638;0.224;49;3.6;IBM SYST J;0018-8670;834;1.636;0.714;42;5.6;IEEE T MULTIMEDIA;1520-9210;511;1.634;0.210;81;3.7;IEEE COMPUT GRAPH;0272-1716;1208;1.602;0.419;43;8.4; IEEE T INF TECHNOL B;1089-7771;389;1.575;0.292;48;3.8;SAR QSAR ENVIRON RES;1062-936X;393;1.546;0.243;37;3.5; INFORMS J COMPUT;1091-9856;407;1.522;0.128;39;5.5; COMPUT PHYS COMMUN;0010-4655;6062;1.515;0.340;188;9.1; IND MANAGE DA TA SYST;0263-5577;491;1.504;0.043;70;3.7; IEEE T SOFTWARE ENG;0098-5589;3088;1.503;0.333;69;>10.0; FOUND COMPUT MA TH;1615-3375;64;1.500;0.083;12;;IEEE SOFTWARE;0740-7459;1157;1.481;0.200;55;7.1;DECIS SUPPORT 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MODEL;0895-7177;1215;0.479;0.068;191;6.2; APPL INTELL;0924-669X;181;0.477;0.000;38;4.5;INT J NUMER METH FL;0271-2091;1940;0.476;0.068;177;8.1; ACM T DES AUTOMA T EL;1084-4309;139;0.475;0.067;15;4.6;J SUPERCOMPUT;0920-8542;161;0.474;0.133;60;4.6;ROBOT AUTON SYST;0921-8890;511;0.468;0.000;76;5.6;IEE PROC-SOFTW;1462-5970;72;0.466;0.045;22;;INT J COMPUT GEOM AP;0218-1959;190;0.463;0.000;21;6.6; COMPUT MUSIC J;0148-9267;238;0.459;0.167;18;>10.0;ASLIB PROC;0001-253X;112;0.456;0.000;35;4.4;COMPUT INFORM;1335-9150;46;0.456;0.000;4;;REAL-TIME IMAGING;1077-2014;143;0.455;0.028;36;5.2; INFORM PROCESS LETT;0020-0190;1680;0.4532006计算机类SCI期刊及影响因子2008-11-21 19:18Abbreviated Journal Title= =Impact= =FactorINT J COMPUT VISION= =6.085 ACM T INFORM SYST= =5.059 BIOINFORMA TICS= =4.894MIS QUART= =4.731IEEE T PATTERN ANAL= =4.306 ACM COMPUT SURV= =4.13ACM T GRAPHIC= =4.081J AM MED INFORM ASSN= =3.979 IEEE T EVOLUT COMPUT= =3.77 IEEE T MED IMAGING= =3.757 NEUROINFORMATICS= =3.541J CHEM INF MODEL= =3.423VLDB 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英语单词按长度排序Sorting English Words by Length.The task of sorting English words by length is a fundamental operation in computer science and linguistics. It involves arranging a set of words in ascending or descending order based on the number of characters they contain. While this task may seem straightforward, it has numerous applications in various fields, including text processing, information retrieval, and natural language processing.In this article, we will explore the concept of sorting English words by length in detail. We will discussdifferent approaches to achieve this task, including basic sorting algorithms and optimized techniques. We will also touch upon the importance of this task in real-world scenarios and how it can be leveraged in various applications.Basic Sorting Algorithms.The most basic approach to sorting English words by length is to use a sorting algorithm such as Bubble Sort, Insertion Sort, or Selection Sort. These algorithms compare the lengths of adjacent words and swap them if necessary to ensure that the words are arranged in the correct order.For example, consider the following set of words: "apple", "banana", "cherry", "date", and "elderberry". Using a basic sorting algorithm, we can sort these words by length as follows:1. Initial list: "apple", "banana", "cherry", "date", "elderberry"2. After sorting: "date", "apple", "cherry", "banana", "elderberry"As you can see, the words are now arranged in ascending order based on their length.However, while these basic sorting algorithms are effective for small datasets, they can be inefficient for larger lists of words. This is because their time complexity is typically O(n^2), where n is the number of words in the list. For larger datasets, this can lead to significant processing delays.Optimized Techniques.To address the limitations of basic sorting algorithms, more efficient techniques have been developed for sorting English words by length. One such technique is the use of a counting sort variant called radix sort.Radix sort is a non-comparative sorting algorithm that works by distributing elements into buckets based on individual digits or, in this case, characters. By applying radix sort to the lengths of words, we can achieve a time complexity of O(n + k), where n is the number of words and k is the maximum word length. This makes radix sort much faster than basic sorting algorithms for larger datasets.Here's how radix sort can be used to sort English words by length:1. Find the maximum length among all the words in the list. Let's call this length L.2. Create L empty buckets, labeled from 0 to L-1.3. Iterate through each word in the list:Determine the length of the current word.Place the word into the bucket corresponding to its length.4. Concatenate the words from each bucket in order to form the sorted list.For example, using radix sort on the same set of words as before ("apple", "banana", "cherry", "date", "elderberry"):1. Maximum length is 7 (for "elderberry").2. Create 7 empty buckets: Bucket 0, Bucket 1, ..., Bucket 6.3. Iterate through each word:"apple" goes into Bucket 5."banana" goes into Bucket 6."cherry" goes into Bucket 6."date" goes into Bucket 4."elderberry" goes into Bucket 7.4. Concatenate the buckets: Bucket 0 (empty), Bucket 1 (empty), ..., Bucket 4 ("date"), Bucket 5 ("apple"), Bucket 6 ("banana", "cherry"), Bucket 7 ("elderberry").The resulting list is: "date", "apple", "cherry","banana", "elderberry", which is the same as the outputfrom the basic sorting algorithms but achieved more efficiently.Applications of Sorting English Words by Length.Sorting English words by length finds applications in various scenarios, including:1. Text Processing: In text editors and word processors, sorting words by length can help users find and managewords more efficiently. For example, a user might want to quickly identify the shortest or longest words in a document.2. Information Retrieval: In search engines and databases, sorting words by length can aid in filtering and ranking search results. By prioritizing shorter words or longer words, search engines can improve the relevance and quality of their search results.3. Natural Language Processing: In natural languageprocessing tasks such as sentiment analysis, part-of-speech tagging, and machine translation, sorting words by length can provide valuable insights into the structure and meaning of text. For instance, shorter words tend to be more frequent and carry less specific meaning than longer words.Conclusion.Sorting English words by length is a fundamental operation in computer science and linguistics that has numerous applications in various fields. Basic sorting algorithms such as Bubble Sort and Insertion Sort are effective for small datasets but can be inefficient for larger lists of words. Optimized techniques like radix sort offer improved performance for larger datasets by leveraging the lengths of words instead of comparing them directly. By understanding and applying these sorting techniques, we can effectively manage and analyze text data in various real-world scenarios.。

语言学作业班级：姓名：Chapter 1 Invitations to LinguisticsI. Please illustrate the following terms.1. Arbitrariness:The forms of linguistic signs bear no natural relationship to their meaning.The different levels of arbitrariness:(1) Arbitrary relationship between the sound of a morpheme and its meaning, even with onomatopoeic words(2) Arbitrariness at the syntactic level: language is not arbitrary at the syntactic level.(3) The link between a linguistic sign and its meaning is a matter of convention. 2. DualityThe property of having two levels of structures, such that units of the primary level are composed of elements of the secondary level and each of the two levels has its own principles of organization.3. Phatic communionPhatic communion refers to the social interaction of language.4. Synchronic linguistics:A synchronic description takes a fixed instant (usually, but not necessarily, the present) as its point of observation. Most grammars are of this kind.II. Please distinguish the following terms:1. Langue vs. ParoleLangue refers to the abstract linguistic system shared by all the members of a speech community, that is, the lexicon, grammar, and phonology implanted in each individual, and it is the linguist’s proper object;Parole refers to the realization of langue, the immediately accessible data. While parole constitutes the immediately accessible data, and it is a mass of confused facts, so it is not suitable for systematic investigation..(1) Langue is abstract, while parole is specific to the situation in which it occurs.(2) Langue is not actually spoken by anyone, while parole is always a naturally occurring event.(3) Langue is relatively stable, systematic and social, while parole is subject to personal, individual and situational constraints.(4) Langue is essential while parole is accessory and accidental.2. Descriptive vs. PrescriptiveThe distinction lies in prescribing how things ought to be and describing how things are.Traditional grammar was very strongly normative in character.The grammarians tried to lay down rules for the correct use of language and settle the disputes over usage once and for all. That is prescriptive.These attitudes are still with us, though people realize nowadays the facts of usage count more than the authority-made “standards”. The nature of linguistics as a science determines its preoccupation with description instead of prescription.3. Synchronic vs. DiachronicSynchronic description takes a fixed instant (usually, but not necessarily, the present) as its point of observation. Most grammars are of this kind.Actually synchrony is a fiction since any language is changing as the minutes pass.Diachronic linguistics is the study of a language through the course of its history.4. Competence vs. PerformanceAccording to Chomsky:A language user’s underlying knowledge about the system of rules is called his linguistic competence.Performance refers to the actual use of language or the actual realization of this knowledge in utterances in concrete situations.A speaker’s competence is stable while his performance is often influenced by psychological and social factors, so a speaker’s performance does not always or equal his supposed competence.He believes that linguists ought to study competence rather than performance.5. Langue vs. CompetenceAccording to Chomsky:Langue is a social product, a systematic inventory of rules of the language, a set of conventions for a speech community.Competence is defined from the psychological point of view, is deemed as a property of the mind of each individuals, or underlying competence as a system of generative processes.According to Hymes:He approaches language from a socio-cultural viewpoint with the aim of studying the varieties of ways of speaking on the part of individual and the community.He extended notion of competence, restricted by Chomsky to a knowledge of grammar, to incorporate the pragmatic ability for language use. This extended idea of competence can be called communicative competence.III. Answer the following questions in brief:1. The following are some book titles of linguistics. Can you judge the synchronic ordiachronic orientation just from the titles?1) English Examined: Two Centuries of Comment on the Mother Tongue2) Protean Shape: A Study in Eighteenth-century Vocabulary and Usage3) Pejorative Sense Development in English4) The Categories and Types of Present-Day English Word-Formation5) Language in the Inner City: Studies in the Black English Vernacular1) diachronic 2）synchronic 3）diachronic 4）synchronic5）We can’t judge whether it is synchronic or diachronic orientation just from the titles.2. What is language? What is linguistics?Language can be defined as a system of arbitrary vocal symbols used for human communication and interaction.Linguistics is the scientific study of human language. The aims of linguistic theory: 1) what is knowledge of language? (Competence) 2) how is knowledge of language acquired? (Acquisition) 3) how is knowledge of language put to use? (Performance/language processing). Main branches of linguistics:Phonetics, Phonology Morphology, Syntax, Semantics, Pragmatics.3. How do you understand performative function of language?The performative function of language is primarily to change the social status of persons or the situations of events, as in marriage ceremonies, the sentencing of criminals, the blessing of children, the naming of a ship at a launching ceremony, and the cursing of enemies.The kind of language employed in performative verbal acts is usually quite formal and even ritualized.The performative function can extend to the control of reality as on some magical or religious occasions.For example, in Chinese when someone breaks a bowl or a plate the host or the people present are likely to say sui sui ping an as a means of controlling the invisible forces which the believers feel might affect their lives adversely.IV. Discuss the following question in detail.How do you interpret the viewpoint that “arbitrariness is a matter of degree”?1)Arbitrary relationship between the sound of a morpheme and its meaning, even with onomatopoeic words:The dog barks bow wow in English but “汪汪汪” in Chinese.2) Arbitrariness at the syntactic level: language is not arbitrary at the syntactic level.⏹He came in and sat down.⏹He sat down and came in.⏹He sat down after he came in.3) The link between a linguistic sign and its meaning is a matter of convention.⏹Arbitrariness of language makes it potentially creative.⏹Conventionality of language makes learning a languagelaborious.Chapter 2 Speech Sounds I. Complete the following statements.1. Human language enable their users to symbolize objects, events and conceptswhich are not present (in time and space) at the moment of communication. This quality is labeled as __________.2. The sound [p] can be described with “voiced, __________, stop.”3. The different members of a phoneme, sounds which are phonetically differentbut do not make one word different from another in meaning,, are_________. 4. Both semantics and ________ investigate linguistic meaning, but they focus ondifferent aspects.5. If certain linguistics tries to lay down rules for the correct use of language andsettle the disputes over usage once and for all, it is ___________ linguistics.6. Phones that fall into allophones of a phoneme have to satisfy two conditions, oneis they are ___________________, and another is that they should be in _____________________.7. The vowel ________ is high front tense unrounded.8. A dog cannot tell people that its master will be home in a few days, because itslanguage does not have the feature of ___________.9. Computational linguistics often refers to the problems of ________________,information retrieval, and ______________.10. Halliday proposed a theory of metafunctions of language, that is, language has___________, ____________ and _____________ functions.II. Define the following terms.1. Manner of articulation:2. Distinctive features:3. Intonation:4. Assimilation:III. Answer the following questions briefly.1. Specify the difference between each pair of sounds using distinctive features.1) [l] [ł ] 2) [p h] [p] 3) [b] [d] 4) [k] [g] 5) [I] [u]2. Work out the features of the following sounds.1) [t h] ________________________________________2) [w] ________________________________________3) [v] ________________________________________4) [ð] _________________________________________5) [l] __________________________________________3. In some dialects of English the following words have different vowels, as shownby the phonetic transcription. Based on these data, answer the questions that follow.A B. Cbite [bʌi t]bide [ba i d]tie [ta i]rice [rʌi s]rise [ra i z]by [ba i]type [tʌi p]bribe [b r aib] sigh [s a i]wife [wʌi f]wives [wa i vz]die [d a i]tyke [tʌi k]time [ta i m]why [wa i]1) What is the difference of the sounds that end the words in columns A and B?2) How do the words in column C differ from those in column A and B?3) Are [ʌi] and [a i] in complementary distribution? Give your reasons.4) What are the phonetic transcriptions of (a) life and (b) lives?5) What would the phonetic transcriptions of the following words be in the dialectsof English shown in the data?(a) trial (b) bike (c) lice(d) fly (e) mine6) State the rule that will relate the phonemic representations to be phonetictranscriptions of the words given above.IV. Discuss the questions in details.1. Illustrate phoneme, phone and allophone.2. To what extent is phonology related ot phonetics and how do they differ?。