Constraining the phrase-based, joint probability statistical translation model

英语语⾔学复习专题英语语⾔学复习专题A Review on English LinguisticsTiger ZhouSchool of Foreign Languages,Shanghai Dianji University⼀.Explain the following terms1.dualityDuality refers to the fact that language is a system,which consists of two sets of structures,or two levels.At the lower level,there is a structure of sounds,which are meaningless by themselves. At the higher level,there is a structure of meanings where the sounds of language can be grouped and regrouped into a large number of units of meaning./doc/6b316456657d27284b73f242336c1eb91b37336b.html plementary distributionWhen two or more than two allophones of the same phoneme do not distinguish meaning and occur in different phonetic environments,then the allophones are said to be in complementary distribution.3.categoryCategory refers to a group of linguistic items which fulfill the same of familiar functions in a particular language such as sentence,a noun phrase or a verb.4.phrase structure ruleA certain word can only co-occur with certain other words.There must be certain grammatical mechanism that ensures the appropriate positions that specifiers,heads and complements occupy in phrase structure.Such special type of grammatical mechanism that regulates the arrangement of elements that make up a phrase is called a phrase structure rule.5.homonymyHomonymy refers to the phenomenon that words having different meanings have the same form,i.e.different words are identical in sound or spelling,or in both.6.displacementDisplacement means that language can be used to refer to things which are present or not present,real or imagined matters in the past,present,or future,or in far away places.In other words,language can be used to refer to contexts removed from the immediate situations of the speaker.This property provides speakers with an opportunity to talk about a wide range of things, free from barriers caused by separation in time or place.7.minimal pairsWhen two different forms are identical in every way except for one sound segment which occurs in the same place in the strings,the two sound combinations are said to form a minimal pair.8.deep structureIt is the structure that is formed by the XP rule in accordance with the head’s sub-categorization properties and that corresponds most closely to the meaningful grouping of words.9.contextIt is generally considered as constituted by the knowledge shared by the speaker and the hearer.10.hyponymyHyponymy refers to the sense relation between a more general,more inclusive word and a more specific word.(The word which is more general in meaning is called the superordinate,and the more specific words are called its hyponyms.)⼆.Short AnswersChapter1Introduction2.What are the major branches of linguistics?What does each of them study?The major branches of linguistics are:(1)phonetics:it studies the sounds used in linguistic communication;(2)phonology:it studies how sounds are put together and used to convey meaning in communication;(3)morphology:it studies the way in which linguistic symbols representing sounds are arranged and combined to form words;(4)syntax:it studies the rules which govern how words are combined to form grammatically permissible sentences in languages;(5)semantics:it studies meaning conveyed by language;(6)pragmatics:it studies the meaning in the context of language use.3.In what basic ways does modern linguistics differ from traditional grammar?The general approach thus traditionally formed to the study of language over the years is roughly referred to as“traditional grammar.”Modern linguistics differs from traditional grammar in several basic ways.Firstly,modem linguistics is descriptive while traditional grammar is prescriptive.Second,modem linguistics regards the spoken language as primary,not the written.Traditional grammarians,on the other hand,tended to emphasize,maybe over-emphasize,the importance of the written word,partly because of its permanence.from traditional grammar also in that it does not force languages4.Is modern linguistics mainly synchronic or diachronic?Why?In modem linguistics,a synchronic approach seems to enjoy priority over a diachronic one. Because people believed that unless the various states of a language in different historical periods are successfully studied,it would be difficult to describe the changes that have taken place in its historical development.6.How is Saussure's distinction between langue and parole similar to Chomsky's distinction between competence and performance?Saussure's distinction and Chomsky's are very similar,they differ at least in that Saussure took a sociological view of language and his notion of langue is a matter of social conventions,and Chomsky looks at language from a psychological point of view and to him competence is a property of the mind of each individual.8.What are the main features of human language that have been specified by C.Hockett to show that it is essentially different from animal communication system?The main features of human language are termed design features.They include:a)ArbitrarinessLanguage is arbitrary.This means that there is no logical connection between meanings and sounds.A good example is the fact that different sounds are used to refer to the same object in different languages.b)ProductivityLanguage is productive or creative in that it makes possible the construction and interpretation of new signals by its users.This is why they can produce and understand an infinitely large number of sentences,including sentences they have never heard before.c)DualityLanguage consists of two sets of structures,or two levels.At the lower or the basic level there is a structure of sounds,which are meaningless by themselves.But the sounds of language can be grouped and regrouped into a large number of units of meaning,which are found at the higher level of the system.d)DisplacementLanguage can be used to refer to things which are present or not present,real or imagined matters in the past,present,or future,or in far-away places.In other words,language can be used to refer to contexts removed from the immediate situations of the speaker.This is what “displacement”means.e)Cultural transmissionWhile human capacity能⼒for language has a genetic basis,i.e.,we were all born with the ability to acquire language,the details of any language system are not genetically transmitted,but instead have to be taught and learned.Chapter2Phonology2.What is voicing and how is it caused?Voicing is a quality of speech sounds and a feature of all vowels and some consonants in English. It is caused by the vibration of the vocal cords.3.Explain with examples how broad transcription and narrow transcription differ?The transcription with letter-symbols only is called broad transcription.This is the transcription normally used in dictionaries and teaching textbooks for general purposes.The latter,i.e.the transcription with letter-symbols together with the diacritics is called narrow transcription.This is the transcription needed and used by the phoneticians in their study of speech sounds.With the help of the diacritics they can faithfully represent as much of the finedetails as it is necessary for their purpose.The example is the consonant[p].We all know that[p]is pronounced differently in the two words pit and spit.In the word pit,the sound[p]is pronounced with a strong puff of air,but in spit the puff of air is withheld to some extent.In the case of pit,the[p]sound is said to be aspirated and in the case ofspit,the[p]sound is unaspirated.This difference is not shown in broad transcription, but in narrow transcription,a small raised“h”is used to show aspiration,thus pit is transcribed as [p?it]and spit is transcribed as[spit].3.What criteria are used to classify the English vowels?Vowels may be distinguished as front,central,and back according to which part of the tongue is held highest.To further distinguish members of each group,we need to apply another criterion,i.e. the openness of the mouth.Accordingly,we classify the vowels into four groups:close vowels, semi-close vowels,semi-open vowels,and open vowels.A third criterion that is often used in the classification of vowels is the shape of the lips.In English,all the front vowels and the central vowels are unrounded vowels,i.e.,without rounding the lips,and all the back vowels,with the exception of[a:],are rounded.Then theEnglish vowels can also be classified according to the length of the sound.The long vowels are all tense vowels and the short vowels are lax vowels. 4. A.Give the phonetic symbol for each of the following sound descriptions:1)voiced palatal affricate[?]2)voiceless labiodental fricative[f]3)voiced alveolar stop[d]4)front,close,short[?]5)back,semi-open,long[?:]6)voiceless bilabial stop[p]B.Give the phonetic features of each of the following sounds:1)[d]voiceless alveolar stop2)[l]voiced alveolar liquid3)[?]voiceless palatal affricate4)[w]voiced bilabial glide5)[?]back,close,(rounded)short6)[?]front,open,(unrounded)short9.Explain with examples the sequential rule,the assimilation rule,and the deletion rule.Rules that govern the combination of sounds in a particular language are called sequential rules.There are many such sequential rules in English.For example,if a word begins with a[l]or a [r],then the next sound must be a vowel.That is why[lbik][lkbi]are impossible combinations in English.They have violated the restrictions on the sequencing of phonemes.The assimilation rule assimilates one sound to another by“copying”a feature of a sequential phoneme,thus making the two phones similar.Assimilation of neighboring sounds is, for the most part,caused by articulatory or physiological processes.When we speak,we tend to increase the ease of articulation.This“sloppy慵懒的;马虎的”tendency may become regularized as rules of language.Deletion rule tells us when a sound is to be deleted although it is orthographically represented.Wehave noticed that in the pronunciation of such words as sign,design,and paradigm,there is no[g]sound although it is represented in spelling by the letter g.But in their corresponding formssignature,designation,and paradigmatic,the[g]represented by the letter g is pronounced.The rule can be stated as:Delete a[g]when it occurs before a final nasal consonant.Given the rule,the phonemic representation of the stems in sign–signature,resign–resignation,paradigm–paradigmatic will include the phoneme/g/,which will be deleted according to the regular rule if no suffix is added.10.What are suprasegmental features?How do the major suprasegmental features of English function in conveying meaning?The phonemic features that occur above the level of the segments are called suprasegmental features.The main suprasegmental features include stress,intonation,and tone.The location of stress in English distinguishes meaning.There are two kinds of stress:word stress and sentence stress.For example,a shift of stress may change the part of speech of a word from a noun,to a verb although its spelling remains unchanged.Tones are pitch variations which can distinguish meaning just like phonemes.Intonation plays an important role in the conveyance of meaning in almost every language,especially in a language like English.When spoken in different tones,the same sequence of words may have different meanings.Chapter3Morphology1.Divide the following words into their separate morphemes by placing a“+”between each morpheme and the next:a.micro+filmb.be+draggle+edc.announce+mentd.pre+digest+ione.tele+communicate+ionf.fore+fatherg.psycho+physics h.mechan+ist6.The following sentences contain both derivational and inflectional affixes.Underline all of the derivational affixes and circle(blacken)the inflectional affixes.a)The farmer’s cow s escap ed.b)It was rain ing.c)Those sock s are inexpensive.d)Jim need s the new er copy.e)The strong est rower continu ed.f)She quickly clos ed the book.g)The alphabetization went well.Chapter4Syntax1.What is syntax?Syntax is a branch of linguistics that studies how words are combined to form sentences and the rules that govern the formation of sentences.2.What is phrase structure rule?The grammatical mechanism that regulates the arrangement of elements(i.e.specifiers,heads, and complements)that make up a phrase is called a phrase structure rule.The phrase structural rule for NP,VP,AP,and PP can be written as follows:NP→(Det)N(PP)...VP→(Qual)V(NP)...AP→(Deg)A(PP)...PP→(Deg)P(NP)...The general phrasal structural rule(X stands for the head N,V,A or P):The XP rule:XP→(specifier)X(complement)3.What is category?How to determine a word’s category?Category refers to a group of linguistic items which fulfill the same or similar functions in a particular language such as a sentence,a noun phrase or a verb.To determine a word’s category,three criteria are usually employed,namely meaning, inflection and distribution.A word’s distributional facts together with information about its meaning and inflectional capabilities help identify its syntactic category.4.What is coordinate structure and what properties does it have?The structure formed by joining two or more elements of the same type with the help of a conjunction is called coordinate structures.Coordinate structures exhibits four important properties:a)There is no limit on the number of coordinated categories that can appear prior to theconjunction.b)A category at any level(a head or an entire XP)can becoordinated.c)Coordinated categories must be of the same type.d)The category type of the coordinate phrase is identical to the category type of theelements being conjoined.6.What is deep structure and what is surface structure?There are two levels of syntactic structure.The first,formed by the XP rule in accordance with the head’s sub-categorization properties,is called deep structure(or D-structure).The second, corresponding to the final syntactic form of the sentence which results from appropriate transformations,is called surface structure(or S-structure).8.The following phrases include a head,a complement,and a specifier.Draw the appropriate tree structure for each.13.The derivations of the following sentences involve the inversion transformation.Give the deep structure and the surface structure of each of thesesentences.a)Would you come tomorrow?b)What did Helen bring to the party?c)Who broke the window?Chapter5Semantics1.What are the major views concerning the study of meaning?1)The naming theory was proposed by the ancient Greek scholar Plato.According to this theory,the linguistic forms or symbols,in other words,the words used in a language are simply labels of the objects they stand for.So words are just names or labels for things.2)The conceptualist view has been held by some philosophers and linguists from ancient times.This view holds that there is no direct link between a linguistic form and what it refers to(i.e.,between language and the real world);rather,in the interpretation of meaning they are linked through the mediation of concepts in the mind.3)Contextualism holds that meaning should be studied in terms of situation,use,context ––elements closely linked with language behavior.The representative of this approach was J.R. Firth,famous British linguist.4)Behaviorism attempts to define the meaning of a language form as the“situation in which the speaker utters it and the response it calls forth in the hearer.”This theory,somewhat close to contextualism,is linked with psychological interest.2.What are the major types of synonyms in English?The major types of synonyms are dialectal synonyms,stylistic synonyms,emotive or evaluative synonyms,collocational synonyms,and semantically different synonyms.3.Explain with examples“homonymy”,“polysemy”,and“hyponymy”.1)Homonymy refers to the phenomenon that words having different meanings have the same form,i.e.,different words are identical in sound or spelling,or in both.When two words are identical in sound,they are homophones.When two words are identical in spelling,they are homographs.When two words are identical in both sound and spelling,they are complete homonyms.2)While different words may have the same or similar meaning,the same one word may have more than one meaning.This is what we call polysemy,and such a word is called a polysemic word.There are many polysemic words in English,The fact is the more commonly used a word is, the more likely it has acquired more than one meaning.3)Hyponymy refers to the sense relation between a more general,more inclusive word anda more specific word.The word which is more general in meaning is called the superordinate, and the more specific words are called itshyponyms.Hyponyms of the same superordinate areco-hyponyms to each other.Hyponymy is a relation of inclusion;in terms of meaning,the superordinate includes all its hyponyms.5.Identify the relations between the following pairs of sentences:1)“T om’s wife is pregnant”presupposes“T om has a wife.”2)“My sister will soon be divorced”presupposes“My sister is a married woman.”3)“He likes seafood”is entailed by”He likes crabs.”4)“They are going to have another baby”presupposes“They have a child.”8.Try to analyze the following sentences in terms of predication analysis:1)The man sells ice-cream.MAN,ICE-CREAM(SELL)2)Is the baby sleeping?BABY(SLEEP)3)It is snowing.(SNOW)4)The tree grows well.TREE(GROW)Chapter6Pragmatics1.What does pragmatics study?How does it differ from traditional semantics?Generally speaking,pragmatics is the study of meaning in the context.It studies meaning in a dynamic way and as a process.In order to have a successful communication,the speaker and hearer must take the context into their consideration so as to effect the right meaning and intention.The development and establishment of pragmatics in the1960s and1970s resulted mainly from the expansion of the study of semantics.However,it is different from the traditional semantics.The major difference between them lies in that pragmatics studies meaning in a dynamic way,while semantics studies meaning in a static way.Pragmatics takes context into consideration while semantics does not.Pragmatics takes care of the aspect of meaning that is not accounted for by semantics.2.Why is the notion of context essential in the pragmatic study of linguistic communication?The notion of context is essential to the pragmatic study of language.It is generally considered as constituted by the knowledge shared by the speaker and the hearer.Various contents of shared knowledge have been identified,e.g.knowledge of the language they use,knowledge of what has been said before,knowledge about the world in general,knowledge about the specific situation in which linguistic communication is taking place,and knowledge about each other.Context determines the speaker’s use of language and also the heater’s interpretation of what is said to him. Without such knowledge,linguistic communication would not be possible,and without considering such knowledge,linguistic communication cannot be satisfactorily accounted for in a pragmatic sense.Look at the following sentences:(1)How did it go?(2)It is cold in here.(3)It was a hot Christmas day so we went down to the beach in the afternoon and had a good time swimming and surfing.Sentence(1)might be used in a conversation between two students talking about an examination,or two surgeons talking about an operation,or in some other contexts;(2)might be said by the speaker to ask the hearer to turn on the heater,or leave the place,or to put on more clothes,or to apologize for the poor condition of the room,depending on the situation of context;(3)makes sense only if the hearer has the knowledge that Christmas falls in summer in the southern hemisphere.3.How are sentence meaning and utterance meaning related,and how do they differ?A sentence is a grammatical concept,and the meaning of a sentence is often studied as the abstract,intrinsic property of the sentence itself in terms of predication.But if we think of a sentence as what people actually utter in the course of communication,it becomes an utterance, and it should be considered in the situation in which it is actually uttered(or used).So it is impossible to tell if“The dog is barking”is a sentence or an utterance.It can be either.It all depends on how we look at it and how we are going to analyze it.If we take it as a grammatical unit and consider it as a self-contained unit in isolation from context,then we are treating it as a sentence.If we take it as something a speaker utters in a certain situation with a certain purpose, then we are treating it as an utterance.Therefore,while the meaning of a sentence is abstract,and decontextualized,that of anutterance is concrete,and context-dependent.The meaning of an utterance is based on sentence meaning;it is the realization of the abstract meaning of a sentence in a real situation of communication,or simply in a context.Now,take the sentence“My bag is heavy”as an example. Semantic analysis of the meaning of the sentence results in the one-place predication BAG(BE HEAVY).Then a pragmatic analysis of the utterance meaning of the sentence varies with the context in which it is uttered.For example,it could be uttered by a speaker as a straightforward statement, telling the hearer that his bag is heavy.It could also be intended by the speaker as an indirect,polite request,asking the hearer to help him carry the bag.Another possibility is that the speaker is declining someone’s request for help.All these are possible interpretations of the same utterance “My bag is heavy”.How it is to be understood depends on the context in which it is uttered and the purpose for which the speaker utters it.While most utterances take the form of grammatically complete sentences,some utterances do not,and some cannot even be restored to complete sentences.5.According to Austin,what are the three acts a person is possibly performing while making an utterance.Give an example.According to Austin’s new model,a speaker might be performing three acts simultaneously when speaking:locutionary act,illocutionary act,and perlocutionary act.A locutionary act is the act of uttering words,phrases,clauses.It is the act of conveying literal meaning by means of syntax,lexicon and phonology.An illocutionary act is the act of expressing the speaker’s intention;it is the act performed in saying something.A perlocutionary act is the act performed by or resulting from saying something;it is the consequence of,or the change brought about by the utterance;it is the act performed by saying something.Let’s look at an example:You have left the door wide open.The locutionary act performed by the speaker is his utterance of the words“you”,“have”,“door”,“open”,etc.,thus expressing what the words literally mean. The illocutionary act performed by the speaker is that by making such an utterance he has expressed his intention of speaking,i.e.asking someone to close the door,or making a complaint, depending on the context.The perlocutionary act refers to the effect of the utterance.If the hearer gets the speaker’s message and sees that the speaker means to tell him to close the door,the speaker has successfully brought about the change in the real world he has intended to;then the perlocutionary act is successfully performed.7.What is indirect language use?How is it explained in the light of speech act theory?When someone is not saying in an explicit and straightforward manner what he means to say, rather he is trying to put across his message in an implicit,roundabout way,we can say he is using indirect language.8.What are the four maxims of the CP?Try to give your own examples to show how flouting these maxims gives rise to conversational implicature? Cooperative Principle,abbreviated as CP.It goes as follows:Make your conversational contribution such as required at the stage at which it occurs by the accepted purpose or direction of the talk exchange in which you are engaged.To be more specific,there are four maxims under this general principle:1)The maxim of quantitya)Make your contribution as informative as required(for the current purpose of the exchange).b)Do not make your contribution more informative than is required.2)The maxim of qualitya)Do not say what you believe to be false.b)Do not say that for which you lack adequate evidence.3)The maxim of relationa)Be relevant.4)The maxim of mannera)Avoid obscurity of expression.b)Avoid ambiguity.c)Be brief(avoid unnecessary prolixity).d)Be orderly.。

From:Proceedings of the Third International Conference onIntelligent Systems for Molecular BiologyJuly,1995AAAI PressThe value of prior knowledge in discovering motifs with MEMETimothy L.Bailey and Charles ElkanDepartment of Computer Science and EngineeringUniversity of California at San DiegoLa Jolla,California92093-0114tbailey@ and elkan@AbstractMEME is a tool for discovering motifs in sets of protein orDNA sequences.This paper describes several extensions toMEME which increase its ability tofind motifs in a totallyunsupervised fashion,but which also allow it to benefit whenprior knowledge is available.When no background knowl-edge is asserted,MEME obtains increased robustness from amethod for determining motif widths automatically,and fromprobabilistic models that allow motifs to be absent in someinput sequences.On the other hand,MEME can exploit priorknowledge about a motif being present in all input sequences,about the length of a motif and whether it is a palindrome,and(using Dirichlet mixtures)about expected patterns in in-dividual motif positions.Extensive experiments are reportedwhich support the claim that MEME benefits from,but doesnot require,background knowledge.The experiments useseven previously studied DNA and protein sequence fami-lies and75of the protein families documented in the Prositedatabase of sites and patterns,Release11.1.IntroductionMEME is an unsupervised learning algorithm for discov-ering motifs in sets of protein or DNA sequences.Thispaper describes the third version of MEME.Earlier ver-sions were described previously(Bailey&Elkan1994),(Bailey&Elkan1995a).The MEME extensions on whichthis paper focuses are methods of incorporating backgroundknowledge,or coping with its lack.For incorporating back-ground knowledge,these innovations include automatic de-tection of inverse-complement palindromes in DNA se-quence datasets,and using Dirichlet mixture priors with protein sequence datasets.Dirichlet mixture priors bring information about which amino acids share common prop-erties and thus are likely to be interchangeable in a given position in a protein motif.This paper also describes a new type of sequence model and a new heuristic for auto-matically determining the width of a motif which remove the need for the user to provide two types of information. The new sequence model type allows each each sequence in the training set to have exactly zero or one occurrences of each motif.This type of model is ideally suited to discov-ering multiple motifs in the majority of cases encounteredne o er sero or o ccurrences per dataset soccurring in each of the different positions in an occurrence of the motif.The background positions in the sequences are modeled by a single discrete random variable.If the width of the motif is ,and the alphabet for sequences is,we can describe the parameters of the twocomponents of each of the three model types in the same way as1121212............012Here,is the probability of letter occurring at eithera background position (0)or at position of a mo-tif occurrence (1),0is the parameters of the background component of the sequence model,and 1is the parameters of the motif component.Formally,the parameters of an OOPS model are the letter frequencies for the background and each column of themotif,and the widthof the motif.The ZOOPS model type adds a new parameter,,which is the prior probability of a sequence containing a motif occurrence.A TCM model,which allows any number of (non-overlapping)motif occur-rences to exist within a sequence,replaces with ,where is the prior probability that any position in a sequence is the start of a motif occurrence.DNA palindromesA DNA palindrome is a sequence whose inverse comple-ment is the same as the original sequence.DNA binding sites for proteins are often palindromes.MEME models a DNA palindrome by constraining the parameters of corre-sponding columns of a motif to be the same:11221122112211221That is,1111for 12.The last column is an inverted version of the first column,the second to last column is an inverted version of the second column,and so on.As will be described below,MEME automatically chooses whether or not to enforce the palindrome constraint,doing so only if it improves the value of the LRT-based objective function.Expectation maximizationConsider searching for a single motif in a set of sequences by fitting one of the three sequence model types to it.The dataset of sequences,each of length ,will be referred toas 12.1There are 1possible starting positions for a motif occurrence in each sequence.The starting point(s)of the occurrence(s)of the motif,if any,in each of the sequences are unknown and are represented by the the variables (called the “missing information”)11where1if a motif occurrence starts in position in sequence ,and 0otherwise.The user selects one of the three types of model and MEME attempts to maximize the likelihood function of a model of that type given the data,,where is a vector containing all the parametersof the model.MEME does this by using EM to maximize the expectation of the joint likelihood of the model given the data and the missing information,.This is done iteratively by repeating the following two steps,in order,until a convergence criterion is met.E-step:computeEM-step:solve1argmaxElogwhere is a vector containing all the parameters of the model.This process is known to converge (Dempster,Laird,&Rubin 1977)to a local maximum of the likelihood func-tion .Joint likelihood functions.MEME assumes each se-quence in the training set is an independent sample from a member of either the OOPS ,ZOOPS or TCM model families and uses EM to maximize one of the following likelihood functions.The logarithm of the joint likelihood for models of each of the three model types is as follows.For an OOPS model,the joint log likelihood islog11log1log11It is not necessary that all of the sequences be of the same length,but this assumption will be made in what follows in order to simplify the exposition of the algorithm.In particular,underthis assumption,.For a TCM model,the joint log likelihood islog111log0log11log1logThe variable used in the ZOOPS likelihood equation is defined as1.Thus,1if sequence contains a motif occurrence,and0otherwise.The conditional sequence probabilities for sequences containing a motif used by OOPS and ZOOPS models are defined as log110log∆log0where is a vector-valued indicator variable of length ,whose entries are all zero except the one corre-sponding to the letter in sequence at position,.∆121is the set of posi-tions in sequence which lie outside the occurrence of the motif when the motif starts at position.The conditional probability of a sequence without a motif occurrence under a ZOOPS model is defined as1The conditional probability of a length-subsequence gen-erated according to the background or motif component of a TCM model is defined to belog1logwhere0if0(background),and1if 1(motif).The E-step.The E-step of EM calculates the expected value of the missing information—the probability that a motif occurrence starts in position of sequence.The formulas used by MEME for the three types of model are given below.Derivations are given elsewhere(Bailey& Elkan1995b).For an OOPS model,101where001and11For a TCM model,10where1if0111otherwise. Here is a vector of pseudo-counts which is used to incor-porate background information into EM as will be described later,is the length-vector of total counts of each letter the dataset,and is the sum of the components of vector .For ZOOPS and TCM models,parameters and are reestimated during the M-step by the formula1111Finding multiple motifsAll three sequence model types supported by MEME model sequences containing a single motif(albeit a TCM model can describe sequences with multiple occurrences of the same motif).Tofind multiple,non-overlapping,different motifs in a single dataset,MEME uses greedy search.It incorporates information about the motifs already discov-ered into the current model to avoid rediscovering the same motif.The process of discovering one motif is called a pass of MEME.The three sequence model types used by MEME assume, a priori,that motif occurrences are equally likely at each position in sequence.This translates into a uniform prior probability distribution on the missing data variables .That is,initially,MEME assumes that1for all.2On the second and subsequent passes, MEME changes this assumption to approximate a multiple-motif sequence model.A new prior on each is used during the E-step that takes into account the probability that a new width-motif occurrence starting at position might overlap occurrences of the motifs found on previous passes of MEME.To help compute the new prior on we introduce vari-ables where1if a width-motif occurrence could start at position in sequence without overlap-ping an occurrence of a motif found on a previous pass. Otherwise0.1if no old motifs in10otherwisefor1and1.To compute we use another set of binary variables which encode which positions in the dataset are not contained in occurrences of previously found motifs.So, is defined as1if previous motif occurrence0otherwisefor1and1.As with the missing information variables,MEME computes and stores the expected values of the variables .Before thefirst pass of MEME,the probability that is not already contained in a motif,the expected value of,is set to one:01for1and1.These values are updated after each pass according to the formula11max1(1)where is thefinal estimate of the missing information at the end of the current pass,.Intuitively,we change the estimate of not being part of some motif by multi-plying it by the probability of it not being contained in an occurrence of the current motif.This we estimate using the most probable motif occurrence of the current width that would overlap it.We use the maximum of because occurrences of the current motif cannot overlap themselves, hence the values of are not independent,so the up-per bound on the probability used here is appropriate.The value of is then used as the value for1in equation(2)below during the next pass, 1.MEME estimates the probability of a width-motif oc-currence not overlapping an occurrence of any previous motif as the minimum of the probability of each position within the new motif occurrence not being part of an oc-currence found on a previous pass.In other words,MEME estimates1as1min11(2)The minimum is used because motif occurrences found on previous passes may not overlap(by assumption)so the values of are not independent.An approximate for-mula for reestimating in the E-step of EM which takes motifs found on previous passes into account and thus ap-proximates a multiple-motif model can be shown to be ˆE1MEME usesˆin place of in the M-step of EM and in equation(1)above.Using prior knowledge about motif columns Applied to models of the forms described above,the EM method suffers from two problems.First,if any letter fre-quency parameter is ever estimated to be zero during EM, it remains zero.Second,if the dataset size is small,the maximum likelihood estimates of the letter frequency pa-rameters tend to have high variance.Both these problems can be avoided by incorporating prior information about the possible values which the letter frequency parameters can take.Using a mixture of Dirichlet densities as a prior in the estimation of the parameters of a model of biopolymer se-quences has been proposed by Brown et al.(1993).This approach makes sense especially for proteins where many of the20letters in the sequence alphabet have similar chemical properties.Motif columns which give high probability to two(or more)letters representing similar amino acids are a priori more likely.A Dirichlet mixture density has the form 11where is a Dirichlet probabil-ity density function with parameter.A simple Dirichlet prior is the special case of a Dirichlet mixture prior where 1.MEME uses Dirichlet mixture priors as follows.In the M-step,the mean posterior estimates of the parameter vectors ,1to,are computed instead of their maximum likelihood estimates.Let be the vector of expected counts of letters in column of the mo-tif.We will consider this to be the“observed”letter counts in column of the motif.The probability of component in the Dirichlet mixture having generated the observed counts for column is calculated using Bayes rule,ΓΓfor1to.This gives the Bayes estimate of the letter probabilities for column of the motif and is used to reestimate in the M-step.Brown et al.(1993)have published several Dirichlet mix-ture densities that model well the underlying probability distribution of the letter frequencies observed in multiple alignments of protein sequences.The experiments reported in this paper use either their30-component Dirichlet mixture prior or a1-component prior where1is just the average letter frequencies in the dataset.Determining the number of model free parameters The number of free parameters in a model of any of the MEME sequence model types depends on the width of the motif and on whether or not the DNA palindrome constraintsare in force.When the width of the motifs is not specified by the user and/or when MEME is asked to check for DNA palindromes,MEME chooses the number of free parame-ters to use by optimizing a heuristic function based on the maximum likelihood ratio test(LRT).The LRT is based upon the following fact(Kendall,Stu-art,&Ord1983).Suppose we successively apply con-straints1to a model with parameters and let be the maximum likelihood estimator of when all constraints1have been applied.Then,under cer-tain conditions,the asymptotic distribution of the statistic22log929 (Abramowitz&Stegun1972).2is the functionfor the standard normal distribution(i.e.,size of the right tail),and is the difference between the number of free parameters in the model used with EM and the null model. There are1free parameters per column of,so the difference in free parameters is1for all three model types.If the DNA palindrome constraints are in force,half the parameters in1are no longer free and21.To compute the value of we need values of the likelihood functions for the given and null models and the difference in the number of free parameters between them.For the likelihood of the given model,MEME uses and1.For a TCM model,12dofor0to by2doChoose good0given and0.Run EM to convergence from chosenvalue of000.Remove outer columns of motifand/or apply palindrome constraintsto maximize.endendReport model which maximizes.Update prior probabilities toapproximate multiple-motif model.endendquantity mean(sd) name sitesprovenprotein18255hth301830protein3800crp182024DNA20011crplex34202521DNA58231Table1:Overview of the datasets used in developingMEME showing sequence type,number of sequences(N),average sequence length(L),and motif width(W).Provensites have been shown to be occurrences of the motif bylaboratory experiment(footprinting,mutagenesis,or struc-tural analysis).Total sites include the proven sites and sitesreported in the literature based primarily on sequence simi-larity with known sites.Measuring performanceWe measured the performance of the motifs discovered byMEME by using thefinal sequence model output after eachpass of as a classifier.The parameters,,of the sequencemodel discovered on a particular pass are converted byMEME into a log-odds scoring matrix and a thresh-old where log0for1and,and log1.The scoring matrixand threshold was used to score the sequences in a test setof sequences for which the positions of motif occurrencesare known.Each subsequence whose score using as aposition-dependent scoring matrix exceeds the thresholdis considered a hit.For each known motif in the test set,the positions of the hits were compared to the positions ofthe known occurrences.The number of true positive(tp),false positive(fp),true negative(tn)and false negative(fn)hits was tallied.From these,recall andprecision were computed.We also calculated the receiver operating characteristic(ROC)(Swets1988)of the MEME motifs.The ROC statisticis the integral of the ROC curve,which plots the true positiveproportion,recall,versus the falsepositive proportion,.The ROC statisticwas calculated by scoring all the positions in the test setusing the log-odds matrix,,sorting the positions byscore,and then numerically integrating over usingthe trapezoid rule.MEME motifs which were shifted versions of a knownmotif were detected by shifting all the known motif positionsleft or right the same number of positions and repeatingthe above calculations of recall,precision and ROC.Allshifts such that all predicted occurrences overlap the knownoccurrences(by exactly the same amount)were tried.ThePerformance of different model typesTable3shows the ROC motifs found by MEME in the de-velopment datasets when MEME was run with the motif width set at100for5passes.Thefirst lines for each of the three model types shows the performance of MEME without background information—DNA palindromes were not searched for and the one-component Dirichlet prior was used.As expected,the ZOOPS model type outperforms both the OOPS and TCM model types on those datasets which conform to the ZOOPS assumptions,as seen from the higher values of ROC for the ZOOPS model type(line4)compared with the OOPS model type(line1)for datasets hrp and cr-plex in Table3.Accuracy is not sacrificed when all of the sequences contain a motif occurrence:the performances of the OOPS and ZOOPS model types are virtually identical onthefirst four datasets.The TCM model type outperforms the other two model types on the farn dataset whose sequences contain multiple occurrences of multiple motifs.For comparison,the last line in Table3shows the per-formance of the motifs discovered using the Gibbs sampler (Lawrence et al.1993).The conditions of the tests were made as close as possible to those for the MEME tests us-ing the OOPS model type,except that the Gibbs sampler was told the correct width of the motifs since it requires the user to specificy the width of all motifs.With each Prosite dataset,the Gibbs sampler was told to search for5motifs, each of the width of the Prosite signature for the family,and that each sequence contained one occurrence of each motif. It was run with100independent starts(10times the default) to maximize its chances offinding good motifs.Note that we did not tell either the Gibbs sampler or MEME how many occurrences of a particular motif a particular sequence has as was done in(Lawrence et al.1993).The ROC of the MEME motifs found using the ZOOPS model type without background information is as good or better than that of the sampler motifs forfive of seven datasets.The MEME motifs found using the OOPS model type perform as well or better than those found by the Gibbs sampler with four of the seven datasets.Note once again that the Gibbs sampler was told the correct motif widths to use,whereas MEME was not.MEME using the ZOOPS model type does significantly better than the Gibbs sampler on the two ZOOPS-like datasets.The benefit of background knowledgeThe efficacy of using the DNA palindrome bias and the Dirichlet mixture prior can be seen in Table3.ROC im-proves in9out of21cases and stays the same with another 5.The improvements are substantial in the case of the least constrained model type,TCM.Forfive of seven datasets, using the background information results in the model with the best or equal-best overall ROC.The LRT-based heuristic does a good job at selecting the “right”width for the motifs in the seven non-Prosite datasets, especially when the DNA palindrome or Dirichlet mixture prior background information is used.The widths of the best motifs found by MEME are shown in Table4.With background information and the model type appropriate to12345?51152253pass with ROC over .99numberofexperiments(total75)Figure1:The pass where MEMEfinds the known Prosite motif is shown.MEME was run forfive passes using the OOPS model without any background information.‘?’means the known motif(s)were not found by MEME within five passes.the dataset,the motif widths chosen by MEME are close to the correct widths with the exception of the lip dataset.That dataset is extremely small and the motifs are faint,which explains why MEME underestimates their widths. Performance on the Prosite datasetsMEME does an excellent job of discovering the Prosite mo-tifs in training sets consisting of entire families.This is true with both the OOPS and ZOOPS model types and with or without the background information provided by the Dirich-let mixture prior.For91%of the75Prosite families,one of the motifs found by MEME run forfive passes using the OOPS model type and the simple prior corresponds to the known Prosite signature(i.e.,identifies the same sites in the dataset).MEMEfinds multiple known motifs in many of the Prosite families.The criterion we use for saying that a MEME motif identifies a known Prosite pattern is that it have ROC of at least0.99.MEME usually discovers the known motifs on early passes,as shown in Figure1.Of the75Prosite families we studied,45significantly overlap other families.We define significant overlap to mean two families sharefive or more sequences in com-mon.If we include the motifs contained in these overlap-ping families,there are135known motifs present in the75 Prosite family datasets.When run for5passes using the OOPS model type with the simple Dirichlet prior,MEME discovers112of these known motifs.The ZOOPS model type does better,discovering117of the135motifs.With the Dirichlet mixture prior,MEME does even better,discov-ering119out of135known motifs using either the OOPS or ZOOPS model types.Small improvements are seen in the performance of MEME motifs discovered in the Prosite datasets when themodel datasetZOOPS-likecrp lex hth lip farn0.97980.99980.9979 1.00000.9446OOPS0.9792 1.0000OOPS 1.0000 1.00000.93360.97980.99990.9992 1.00000.9112ZOOPS0.9792 1.0000ZOOPS 1.0000 1.00000.93240.92400.98950.98880.98420.9707TCM0.97860.9811TCM0.98410.99520.9880GIBBS0.88810.9672OOPS-like TCM-likehrp crplexknown width2016162020OOPS18562918PAL162424DMIX76ZOOPS18562118PAL162220DMIX76TCM11882112PAL92011DMIX77ROC recall precision relative width shift0.805(0.356) 1.297(0.753)DMIX0.992(0.031)0.758(0.325)-0.637(5.337)0.823(0.335) 1.307(0.774)DMIX0.993(0.026)0.768(0.314)-0.585(4.890) Table5:Average(standard deviation)performance and width of best motifs found by MEME in the75Prosite datasets.All 135known motifs contained in the datasets are considered.model typeOOPS0.738(0.288) 1.170(0.840)ZOOPS0.728(0.305) 1.141(0.815) DMIX,200.987(0.029)0.840(0.228)GIBBS,200.980(0.053)0.884(0.169)Dirichlet mixture prior is used.This is especially true for the datasets containing few(under20)sequences.For the36Prosite datasets we used which meet this criterion and would thus be most likely to benefit from the backgroundinformation contained in the Dirichlet mixture prior,the im-provement in ROC is statistically significant at the5%level for the OOPS model type according to a paired t-test.Themotifs discovered using the ZOOPS model type are slightlysuperior to those found with the OOPS model type.Ta-ble5shows the average performance results on the Prositedatasets when MEME is run forfive passes with variousmodel types,with or without Dirichlet priors,and required to choose the motif width in the range5100.Theperformance values are for all135known motifs containedin the75datasets,as described above.The difference in ROC between the OOPS and ZOOPS model types when thesimple Dirichlet prior is used is significant at the5%level.When the Dirichlet mixture prior is used,the difference in ROC between the two model types is not statistically sig-nificant.For both model types,whether or not the Dirichletmixture prior is used does not make a statistically significant difference in the ROC of the discovered motifs.The MEME motifs are extremely similar to the Prositesignatures.In general,they identify almost exactly the samepositions in the sequences in the families.This fact can be seen in Table5from the high ROC,relative width close to1,and small shift of the MEME motifs. GeneralizationCross-validation experiments show that the motifs discov-ered by MEME on the Prosite datasets can be expected to cor-rectly identify new members of the protein families.Table6 shows the results of2-fold cross-validation experiments onthe75Prosite families using MEME and the Gibbs sampler.Thefirst two lines of the table show the results when MEME is forced to choose the motif width.The performance of the OOPS model type is slightly better than that of the ZOOPS model type(ROC better at5%significance level).Perfor-mance is better if MEME is given background informationin the form of being told a good width(20),as seenin the third line in Table6.Then the generalization perfor-mance(cross-validated ROC)of the MEME motifs is better than that of sampler motifs at the5%significance level.In these experiments,both MEME and the Gibbs sampler were allowed to generate only one motif per training set.The Gibbs sampler was instructed to use motif width20 and250(25times the default)independent starts to ensure that the two algorithms got approximately the same number of CPU cycles.The performancefigures in Table6are based on the number of hits scored on sequences in SWISS-PROT known to be in the family,and do not require the hit to be at any particular position within the sequence.We used a threshold of18bits for determining if scores were hits.A direct comparison of the predicted generalization per-formance of motifs discovered by learning algorithms suchas MEME and the Gibbs sampler with that of the Prosite signatures is not possible.The Prosite signatures were cre-ated by hand and cannot easily be cross-validated,so their generalization performance is not known.However,the av-erage performance of the Prosite signatures on their own training sets,ROC099002,is the same as the cross-validated performance of the MEME OOPS-model motifs found when the algorithm is given a hint about the width of the motifs.This is impressive since the MEME motifs were learned from only half of the members of the families so the cross-validated ROC is likely to be an underestimate of the actual ROC of the motifs.The non-cross-validated estimate of the Prosite signature performance is likely to overestimate their actual performance on new sequences.ReferencesAbramowitz,M.,and Stegun,I.A.,eds.1972.Handbook of Mathematical Functions with Formulas,Graphs and Mathemat-ical Tables.Dover Publications,Inc.Bailey,T.L.,and Elkan,C.1994.Fitting a mixture model by expectation maximization to discover motifs in biopolymers.In Proceedings of the Second International Conference on Intelli-gent Systems for Molecular Biology,28–36.AAAI Press. Bailey,T.L.,and Elkan,C.1995a.Unsupervised learning of multiple motifs in biopolymers using EM.Machine Learning.In press.Bailey,T.L.,and Elkan,C.1995b.The value of prior knowledge in discovering motifs with MEME++.Technical Report CS95-413,Department of Computer Science,University of California, San Diego.Bairoch,A.1994.The SWISS-PROT protein sequence data bank:current status.Nucleic Acids Research22(17):3578–3580. Brown,M.;Hughey,R.;Krogh,A.;Mian,I.S.;Sjolander,K.; and Haussler,ing Dirichlet mixture priors to derive hidden Markov models for protein families.In Intelligent Systems for Molecular Biology,47–55.AAAI Press.Dempster,A.P.;Laird,N.M.;and Rubin,D.B.1977.Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society39(1):1–38.Harley,C.B.,and Reynolds,R.P.1987.Analysis of E.coli promoter sequences.Nucleic Acids Research15:2343–2361. Kendall,S.M.;Stuart,A.;and Ord,J.K.1983.The Advanced Theory of Statistics.Charles Griffin&Company Limited. Lawrence,C.E.,and Reilly,A.A.1990.An expectation maxi-mization(EM)algorithm for the identification and characteriza-tion of common sites in unaligned biopolymer sequences.PRO-TEINS:Structure Function and Genetics7:41–51. Lawrence,C.E.;Altschul,S.F.;Boguski,M.S.;Liu,J.S.; Neuwald,A.F.;and Wootton,J.C.1993.Detecting subtle sequence signals:A Gibbs sampling strategy for multiple align-ment.Science262(5131):208–214.Seber,G.A.F.1984.Multivariate observations.John Wiley& Sons,Inc.Swets,J.A.1988.Measuring the accuracy of diagnostic systems. Science270:1285–1293.。

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The Topological Field Model: A Complex Example (II)
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• Phrase Structure • A Topological Model for German • From Phrase Structure to GPSG and HPSG • German Word Order Phenomena and their Analyses in Standard-HPSG/GPSG • Discontinuous Constituents • Parsing • Syntactically Annotated Corpora and Discontinuity • Summary S → NP, VP NP → Det, N VP → V, NP, NP Det The NP N man V gave
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语⾔学第四章知识点总结胡壮麟版Chapter 44.1 syntactic（句法的）relations4.1.1 positional relation(位置关系）For language to fulfill its communicative function, it must have a way to mark the grammatical roles of the various phrase that can occur in a clause.Positional relation or word order refers to the sequential（有序的）arrangement of words in a language.Positional relation are a manifestation(表现）of one aspect of syntagmatic relationsWord order is among the three basic ways (word order genetic and classification) to classify language words.Six possible types of language SVO VSO SOV OVS VOS English is SVO.4.1.2 relation of substitutability(可代替性）Firstly relation of substitutability refers to classes or sets of words substitutable of each other grammatically in sentence with the same structure.Secondly it refers to groups of more than one word which may be jointly substitutable grammatically for a single word of a particular set.This is what Saussure called associative(联想的）relations or in Hjemslev’s paradigmatic(纵聚性的) relation.4.1.3 relation of co-occurrence(共现）Means words of different sets of clauses may permit pr require the occurrence of a word of another set or class to from a sentence or a particular part of a sentence.Thus relations of co-occurrence partly belong to syntagmatic rations partly to paradigmatic relations.4.2 grammatical construction and its constituents4.2.1 Grammatical constructionAny syntactic string of words ranging from sentences over phrases structures to certain complex lexemes(词位）4.2.2 immediate constituents(直接成分）Constituent is a part of a larger linguistic unit. Several constituents together form a construction: SNP VPDe t V NPDe tThe girl ate the appleThis is tree diagram. 在句⼦结构分析中，成分⽤来指任何语⾔单位，⽽该单位⼜是更⼤语⾔单位的⼀部分，如在The girl ate the apple 本⾝的（A）the boy（B) ate the apple (C）都是⼀个成分，成分可以和其他成分组合组成更⼤的单位，如果两个成分B（the boy )C (ate the apple)结合起来形成⼀个更⾼的成分AWord-levelN=nounA=adjectiveV=verbP=prepositionDet=determinerAdv=adverbConj=conjunctionPhrasalNP=noun phraseAP=adjective phraseVP=verb phrasePP=preposition phraseS=sentence or clauseTo dismantle a grammatical constructure is this way is called immediate constituents or IC analysis.Bracketing is not as common in use, but it is an economic notation in representing the constituent/phrase structure of a grammatical unit.(((The) (girl)) ((ate) ((the) (apple))))2.3 Endocentric and Exocentric ConstructionsEndocentric construction is one whose distribution is functionally equivalent to that of one or more of its constituents, i.e., a word or a group of words, which serves as a definable centre or head.Usually noun phrases, verb phrases and adjective phrases belong to endocentric types because the constituent items are subordinate to the Head.4.2.3.Endocentric and Exocentric Constructions4.2.3.1Endocentric（相信结构）Endocentric construction is one whose distribution is functionally equivalent to that of one or more of its constituents, i.e., a word or a group of words, which serves as a definable centre or head.Usually noun phrases, verb phrases and adjective phrases belong to endocentric types because the constituent items are subordinate to the Head.4.2.3.1 Exocentric（并列结构）Exocentric construction refers to a group of syntactically related words where none of the words is functionally equivalent to the group as a whole, that is, there is no definable “Centre”or “Head”inside the group, usually includingthe basic sentence,the prepositional phrase,the predicate (verb + object) construction, andthe connective (be + complement) construction.2.4 Coordination and SubordinationEndocentric constructions fall into two main types, depending on the relation between constituents:Coordination （并列）is a common syntactic pattern in English and other languages formed by grouping together two or more categories of the same type with the help of a conjunction such as and, but and or .Coordination of NPs:[NP the lady] or [NP the tiger]Coordination of VPs:[VP go to the library] and [VP read a book ]Coordination of PPs:[PP down the stairs] and [PP out the door ]Coordination of APs:[AP quite expensive] and [AP very beautiful]Coordination of Ss:[S John loves Mary] and [S Mary loves John too].Subordination（从属）refers to the process or result of linking linguistic units so that they have different syntactic status, one being dependent upon the other, and usually a constituent of the other.The subordinate constituents are words which modify the head. Consequently, they can be called modifiers.Clauses can be used as subordinate constituents. There are three basic types of subordinate clauses:4.3. Syntactic（句法）FunctionThe syntactic function shows the relationship between a linguistic form and other parts of the linguistic pattern in which it is used.Names of functions are expressed in terms of subjects, objects, predicators, modifiers, complements, etc.4.3.1 SubjectIn English, the subject of a sentence is often said to be the agent, or the doer of the action, while the object is the person or thing acted upon by the agent.In order to account for the case of subject in passive voice, we have two other terms “grammatical subject”and “logical subject”Word orderSubject ordinarily precedes the verb in the statement:Pro-formsThe first and third person pronouns in English appear in a special form when the pronoun is a subject, which is not used when the pronoun occurs in other positions:Agreement with the verbIn the simple present tense, an -s is added to the verb when a third person subject is singular, but the number and person of the object or any other element in the sentence have no effect at all on the form of the verbContent questionsIf the subject is replaced by a question word (who or what), the rest of the sentence remains unchanged, as in4.4Category （范畴）The term category refers to the defining properties of these general units:Categories of the noun: number, gender, case and countabilityCategories of the verb: tense, aspect, voice4.4.1 NumberNumber is a grammatical category used for the analysis of word classes displaying such contrasts as singular, dual, plural, etc.4.4.2Gende(性）Such contrasts as “masculine : feminine : neuter”, “animate : inanimate”, etc. for the analysis of word classes.4.4.3Case（格）The case category is used in the analysis of word classes to identify the syntactic relationship between words in a sentence.4.4.4Agreement.Agreement (or concord) may be defined as the requirement that the forms of two or more words of specific word classes that stand in specific syntactic relationship with one another shall also, be characterized by the same paradigmatically marked category (or categories).4.5Phrase, Clause and Sentence4.5.1phrasePrase is a single element of structure containing more than one word, and lacking the subject-predicate structure typical of clauses.4.5.3SentenceBasic sentence types: QuirkSVC Mary is kind.a nurse.SV A Mary is here.in the house.SV The child is laughing.SVO Somebody caught the ball.SVOC We have proved him wrong.a fool.SVOA I put the plate on the table.SVOO She gives me expensive presents.4.6Recursiveness(递归性）Recursiveness mainly means that a phrasal constituent can be embedded within anotherconstituent having the same category, but it has become an umbrella term such important linguistic phenomena as coordination and subordination, conjoining and embedding, hypotactic and paratactic.Theoretically, there is no limit to the embedding of one relative clause into another relative clause, so long as it does not become an obstacle to successful communication.4.6.1ConjoiningConjoining: coordination.Conjunctions: and, but, and or.联系⼀个⼩句或者其他并列或链接的过程，通过这种过程组成的句⼦即并列4.6.2Embedding（嵌⼊）Embedding: subordination.Main clauses and subordinate clauses.Three basic types of subordinate clauses:Relative clause:Complement clause:Adverbial clause:。