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A Cognitive Model for the Representation and Acquisition of Verb Selectional Preferences

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P r o c e e d i n g s o f t h e A C L W o r k s h o p o n C o g n i t i v e A s p e c t s o f C o m p u t a t i o n a l L a n g u a g e A c q u i s i t i o n , 2007, p p 41-48.A Cognitive Model for the Representation and Acquisitionof Verb Selectional PreferencesAfra AlishahiDepartment of Computer ScienceUniversity of Toronto afra@Suzanne StevensonDepartment of Computer ScienceUniversity of Torontosuzanne@AbstractWe present a cognitive model of inducing verb selectional preferences from individ-ual verb usages.The selectional preferences for each verb argument are represented as a probability distribution over the set of semantic properties that the argument can possess—a semantic profile .The seman-tic profiles yield verb-specific conceptual-izations of the arguments associated with a syntactic position.The proposed model can learn appropriate verb profiles from a small set of noisy training data,and can use them in simulating human plausibility judgments and analyzing implicit object alternation.1IntroductionVerbs have preferences for the semantic properties of the arguments filling a particular role.For ex-ample,the verb eat expects that the object receiving its theme role will have the property of being edi-ble,among others.Learning verb selectional pref-erences is an important aspect of human language acquisition,and the acquired preferences have been shown to guide children’s expectations about miss-ing or upcoming arguments in language comprehen-sion (Nation et al.,2003).Resnik (1996)introduced a statistical approach to learning and use of verb selectional preferences.In this framework,a semantic class hierarchy for words is used,together with statistical tools,to in-duce a verb’s selectional preferences for a particu-lar argument position in the form of a distributionover all the classes that can occur in that position.Resnik’s model was proposed as a model of human learning of selectional preferences that made min-imal representational assumptions;it showed how such preferences could be acquired from usage data and an existing conceptual hierarchy.However,his and later computational models (see Section 2)have properties that do not match with certain cognitive plausibility criteria for a child language acquisition model.All these models use the training data in “batch mode”,and most of them use information theoretic measures that rely on total counts from a corpus.Therefore,it is not clear how the representa-tion of selectional preferences could be updated in-crementally in these models as the person receives more data.Moreover,the assumption that children have access to a full hierarchical representation of semantic classes may be too strict.We propose an alternative view in this paper which is more plausi-ble in the context of child language acquisition.In previous work (Alishahi and Stevenson,2005),we have proposed a usage-based computational model of early verb learning that uses Bayesian clus-tering and prediction to model language acquisition and use.Individual verb usages are incrementally grouped to form emergent classes of linguistic con-structions that share semantic and syntactic proper-ties.We have shown that our Bayesian model can incrementally acquire a general conception of the semantic roles of predicates based only on expo-sure to individual verb usages (Alishahi and Steven-son,2007).The model forms probabilistic associa-tions between the semantic properties of arguments,their syntactic positions,and the semantic primitivesof verbs.Our previous experiments demonstrated that,initially,this probability distribution for an ar-gument position yields verb-specific conceptualiza-tions of the role associated with that position.As the model is exposed to more input,the verb-based roles gradually transform into more abstract representa-tions that reflect the general properties of arguments across the observed verbs.A shortcoming of the model was that,because the prediction of the semantic roles was based only on the groupings of verbs,it could not make use of verb-specific knowledge in generating expectations about a particular verb’s arguments.That is,once it was exposed to a range of verbs,it no longer had access to the verb-specific information,only to gen-eralizations over clusters of verbs.In this paper,we propose a new version of our model that,in addition to learning general seman-tic roles for constructions,can use its verb-specific knowledge to predict intuitive selectional prefer-ences for each verb argument position.We introduce a new notion,a verb semantic profile,as a prob-ability distribution over the semantic properties of an argument for each verb.A verb semantic pro-file is predicted from both the verb-based and the construction-based knowledge that the model has learned through clustering,and reflects the prop-erties of the arguments that are observed for that verb.Our proposed prediction model makes appro-priate generalizations over the observed properties, and captures expectations about previously unseen arguments.As in other work on selectional preferences,the semantic properties that we use in our representa-tion of arguments are drawn from a standard lex-ical ontology(WordNet;Miller,1990),but we do not require knowledge of the hierarchical structure of the WordNet concepts.From the computational point of view,this makes use of an available re-source,while from the cognitive view,this avoids ad hoc assumptions about the representation of a conceptual hierarchy.However,we do require some properties to be more general(i.e.,shared by more words)than others,which eventually enables the model to make appropriate generalizations.Other-wise,the selected semantic properties are not fun-damental to the model,and could in the future be replaced with an approach that is deemed more ap-propriate to child language acquisition.Each argu-ment contributes to the semantic profile of the verb through its(potentially large)set of semantic prop-erties instead of its membership in a single class.As input to our model,we use an automatically parsed corpus,which is very noisy.However,as a result of our novel representation,the model can induce and use selectional preferences using a relatively small set of noisy training data.2Related Computational ModelsA variety of computational models for verb selec-tional preferences have been proposed,which use different statistical models to induce the preferences of each verb from corpus data.Most of these models,however,use the same representation for verb selectional preferences:the preference can be thought of as a mapping,with respect to an argument position for a verb,of each class to a real number (Light and Greiff,2002).The induction of a verb’s preferences is,therefore,modeled as using a set of training data to estimate that number.Resnik(1996)defines the selectional preference strength of a verb as the divergence between two probability distributions:the prior probabilities of the classes,and the posterior probabilities of the classes given that verb.The selectional association of a verb with a class is also defined as the contribu-tion of that class to the total selectional preference strength.Resnik estimates the prior and posterior probabilities based on the frequencies of each verb and its relevant argument in a corpus.Li and Abe(1998)model selectional preferences of a verb(for an argument position)as a set of nodes in the semantic class hierarchy with a probability distribution over them.They use the Minimum De-scription Length(MDL)principle tofind the best set for each verb and argument based on the usages of that verb in the training data.Clark and Weir(2002) alsofind an appropriate set of concept nodes to rep-resent the selectional preferences for a verb,but do so using aχ2test over corpus frequencies mapped to concepts to determine when to generalize from a node to its parent.Ciaramita and Johnson(2000) use a Bayesian network with the same topology as WordNet to estimate the probability distribution of the relevant set of nodes in the hierarchy.Abneyand Light(1999)use a different representational ap-proach:they train a separate hidden Markov model for each verb,and the selectional preference is rep-resented as a probability distribution over words in-stead of semantic classes.3The Bayesian Verb-Learning Model3.1Overview of the ModelOur model learns the set of argument structure frames for each verb,and their grouping across verbs into constructions.An argument structure frame is a set of features of a verb usage that are both syn-tactic(the number of arguments,the syntactic pat-tern of the usage)and semantic(the semantic prop-erties of the verb,the semantic properties of each argument).The syntactic pattern indicates the word order of the verb and arguments.A construction is a grouping of individual frames which probabilisti-cally share syntactic and semantic features,and form probabilistic associations across verb semantic prop-erties,argument semantic properties,and syntactic pattern.These groupings typically correspond to general constructions in the language such as tran-sitive,intransitive,and ditransitive.For each verb,the model associates an argument position with a probability distribution over a set of semantic properties—a semantic profile.In doing so,the model uses the knowledge that it has learned for that verb,as well as the grouping of frames for that verb into constructions.The semantic properties of words are taken from WordNet(version2.0)as follows.We extract all the hypernyms(ancestors)for all the senses of the word, and add all the words in the hypernym synsets to the list of the semantic properties.Figure1shows an ex-ample of the hypernyms for dinner,and its resulting set of semantic properties.1The following sections review basic properties of the model from Alishahi and Stevenson(2005, 2007),and introduce extensions that give the model its ability to make verb-based predictions.3.2Learning as Bayesian ClusteringEach argument structure frame for an observed verb usage is input to an incremental Bayesian clustering Sense1dinner=>meal,repast=>nutriment,nourishment,nutrition,sustenance,aliment,alimentation,victuals=>food,nutrient=>substance,matter=>entitySense2dinner,dinner party=>party=>social gathering,social affair=>gathering,assemblage=>social group=>group,groupingFigure1:Semantic properties for dinner from Word-Netprocess.This process groups the new frame together with an existing group of frames—a construction—that probabilistically has the most similar semantic and syntactic properties to it.If no construction has sufficiently high probability for the new frame,then a new construction is created for it.We use the prob-abilistic model of Alishahi and Stevenson(2007)for learning constructions,which is itself an adaptation of a Bayesian model of human categorization pro-posed by Anderson(1991).It is important to note that the categories(i.e.,constructions)are not prede-fined,but rather are created according to the patterns of similarity over observed frames.Grouping a frame F with other frames participat-ing in construction k is formulated asfinding the k with the maximum probability given F: BestConstruction(F)=argmaxkP(k|F)(1) where k ranges over the indices of all constructions, with index0representing recognition of a new con-struction.Using Bayes rule,and dropping P(F)which is constant for all k:P(k|F)=P(k)P(F|k)P(F|k)=i∈FrameFeaturesP i(j|k)(3)where j is the value of the i th feature of F,and P i(j|k)is the probability of displaying value j on feature i within construction k.Given the focus here on semantic profiles,we next focus on the calcula-tion of the probabilities of semantic properties.3.3Probabilities of Semantic PropertiesThe probability in equation(3)of value j for feature i in construction k is estimated using a smoothed version of this maximum likelihood formula:P i(j|k)=count k i(j)score(S F,S k)=|S F∩S k|score of4score’s for the new frame and every member of construction k,and normalize the resulting probability over all pos-sible sets of semantic properties in our lexicon.3.4Predicting Semantic Profiles for VerbsWe represent the selectional preferences of a verb for an argument position as a semantic profile,which is a probability distribution over all the semantic properties.To predict the profile of a verb v for an argument position arg,we need to estimate the probability of each semantic property j separately: P arg(j|v)= k P arg(j,k|v)(6)∝ k P(k,v)P arg(j|k,v) Here,j ranges over all the possible semantic proper-ties that an argument can have,and k ranges over all constructions.The prior probability of having verb v in construction k,or P(k,v),takes into account two important factors:the relative entrenchment of the construction k,and the(smoothed)frequency with which v participates in k.The posterior probability P arg(j|k,v)is calcu-lated analogously to P i(j|k)in equation(4),but lim-iting the count of matching features to those frames in k that contain v:P arg(j|k,v)=verbn kv(7) where n kv is the number of frames for v participat-ing in construction k,and verbwhere j n is the set of the semantic properties for word n,and P arg(j n|v)is estimated as in equa-tion(7).However,since j n here is a set of prop-erties(as opposed to j in equation(7)being a single property),verbcount k arg(j n,v)to the sum of the sem2To our knowledge,the only work that considers selectional preferences of subjects and prepositional phrases as well as di-rect objects is Brockmann and Lapata(2003).3Computational models of verb selectional preference have been evaluated through disambiguation tasks(Li and Abe, 1998;Abney and Light,1999;Ciaramita and Johnson,2000; Clark and Weir,2002),but for to evaluate our cognitive model, the experiments from Resnik(1996)are the most interesting.45/∼dr/Tgrep26The software was provided to us by Eric Joanis,and Af-each verb usage in a sentence,we construct a frame by recording the verb in root form,the number of the arguments for that verb,and the syntactic pattern of the verb usage(i.e.,the word order of the verb and the arguments).We also record in the frame the semantic properties of the verb and each of the ar-gument heads(each noun is also converted to root form);these properties are extracted from WordNet (as discussed in Section3.1and illustrated in Fig-ure1).This process results in16,300frames which serve as input data to our learning model.4.2Formation of Semantic Profiles for Verbs After training our model on the above data,we use equation(7)to predict the semantic profile of the di-rect object position for a range of verbs.Some of these verbs,such as write and sing,have strong se-lectional preferences,whereas others,such as want and put,can take a wide range of nouns as direct object(as confirmed by Resnik’s(1996)estimated strength of selectional preference for these verbs). The semantic profiles for write and sing are dis-played in Figure2,and the profiles for want and put are displayed in Figure3.(Due to limited space,we only include the25properties that have the highest probability in each profile.)Because we extract the semantic properties of words from WordNet,which has a hierarchical structure,the properties that come from nodes in the higher levels of the hierarchy(such as entity and abstraction)appear as the semantic property for a very large set of words,whereas the properties that come from the leaves in the hierarchy are specific to a small set of words.Therefore,the general prop-erties are more likely to be associated with a higher probability in the semantic profiles for most verbs. In fact,a closer look at the semantic profiles for want and put reveals that the top portion of the semantic profile for these verbs consists solely of such gen-eral properties that are shared among a large group of words.However,this is not the case for the more restrictive verbs.The semantic profiles for write and sing show that the specific properties that these verbs demand from their direct object appear amongst the highest-ranked properties,even though only a small set of words share these properties(e.g.,content,write(0.020)abstraction(0.015)relation(0.015)communication(0.015)social relation(0.013)act(0.013)human action(0.013)human activity(0.013)auditorycommunication(0.012)music(0.010)entity(0.010)piece(0.009)composition(0.009)musicalcomposition(0.009)opus(0.009)piece of music(0.009)psychologicalfeature(0.008)cognition(0.008)knowledge(0.008)noesis(0.008)activity(0.008)content(0.008)grouping(0.008)group(0.008)amount(0.008)measure Figure2:Semantic profiles of write and sing for the direct object position.message,written communication,written language, ...for write,and auditory communication,music, musical composition,opus,...for sing).The examination of the semantic profiles for fairly frequent verbs in the training data shows that our model can use the verb usages to predict an appro-priate semantic profile for each verb.When pre-sented with a novel verb(for which no verb-based information is available),equation(7)predicts a se-mantic profile which reflects the relative frequencies of the semantic properties among all words(due to the smoothing factor added to equation(7)),modu-lated by the prior probability of each construction. The predicted profile is displayed in Figure4.It shows similarities with the profiles for want and put in Figure3,but the general properties in this profile have an even higher probability.Since the profile for the novel verb is predicted in the absence of any evi-dence(i.e.,verb usage)in the training data,we later use it as the base for estimating other verbs’strength of selectional preference.want(0.015)entity(0.015)object(0.013)physical object(0.013)abstraction(0.011)unit(0.011)whole(0.011)whole thing(0.011)artifact(0.011)artefact(0.010)act(0.009)relation(0.008)human action(0.008)human activity(0.008)communication(0.008)social relation(0.007)substance(0.007)content(0.007)instrumentality(0.007)instrumentation(0.007)measure(0.006)amount(0.006)quantity(0.006)cause(0.006)causal agent(0.006)causal agency Figure3:Semantic profiles of want and put for the direct object position.4.3Verb-Argument Plausibility Judgments Holmes et al.(1989)evaluate verb argument plau-sibility by asking human subjects to rate sentences like The mechanic warned the driver and The me-chanic warned the engine.Resnik(1996)used this data to assess the performance of his model by com-paring its judgments of selectionalfit against the plausibility ratings elicited from human subjects.He showed that his selectional association measure for a verb and its direct object can be used to select the more plausible verb-noun pair among the two(e.g., <warn,driver>vs.<warn,engine>in the previous example).That is,a higher selectional association between the verb and one of the nouns compared to the other noun indicates that the former is the more plausible pair.Resnik(1996)used the Brown corpus as training data,and showed that his model arrives at the correct ordering of more and less plausible ar-guments in11of the16cases.We repeated this experiment,using the same16 pairs of verb-noun combinations.For each pair of <v,n1>and<v,n2>,we calculate the compati-bility measure using equation(8);these values are shown in Figure5.(Note that because these areA novel verbsee friend-30.50method-32.14read article-32.76fashion-33.33find label-32.05fever-33.30hear story-32.11issue-32.40write letter-31.37market-32.46urge daughter-36.73contrast-35.64warn driver-33.68engine-34.42judge contest-39.05climate-38.23teach language-45.64distance-45.11show sample-31.75travel-31.42expect visit-33.88mouth-32.87answer request-31.89tragedy-33.95recognize author-32.53pocket-32.62repeat comment-33.80journal-33.97understand concept-32.25session-32.93remember reply-33.79smoke-34.29Figure5:Compatibility scores for plausible vs.im-plausible verb-noun pairs.particular relationship between the verb and its argu-ment.In particular,for verbs that participate in theimplicit object alternation,the omitted object mustbe in some sense inferable or typical for that verb(Levin,1993,among others).Resnik(1996)used his model of selectional pref-erences to analyze implicit object alternations,andshowed a relationship between his measure of se-lectional preference strength and the notion of typ-icality of an object.He calculated this measurefor two groups of Alternating and Non-alternatingverbs,and showed that,on average,the Alternatingverbs have a higher strength of selectional prefer-ence for the direct object than the Non-alternatingverbs.However,there was no threshold separatingthe two groups of verbs.To repeat Resnik’s experiment,we need a mea-sure of how“strongly constraining”a semantic pro-file is.We can do this by measuring the similaritybetween the semantic profile we generate for the ob-ject of a particular verb and some“default”notion ofthe argument for that position across all verbs.Weuse the semantic profile predicted for the object po-sition of a novel verb,shown earlier in Figure4,asthe default profile for that argument position.Be-cause this profile is predicted in the absence of anyevidence in the training data,it makes the minimumassumptions about the properties of the argumentand thus serves as a suitable default.We then assumethat verbs with weaker selectional preferences havesemantic profiles more similar to the default profileAlternating verbswrite0.56sing0.71drink0.75eat0.76play0.77pour0.78watch0.78pack0.81steal0.83push0.87call0.87pull0.87explain0.87read0.88hear0.890.890.900.76Mean:||p||×||q||(9) The similarity values for the Alternating and Non-alternating verbs are shown in Figure6.The larger values represent more similarity with the base pro-file,which means a weaker selectional preference. The means for the Alternating and Non-alternating verbs were respectively0.76and0.81,which con-firm the hypothesis that verbs participating in im-plicit object alternations select more strongly for the direct objects than verbs that do not.However,like Resnik(1996),wefind that it is not possible to set a threshold that will distinguish the two sets of verbs. 5ConclusionsWe have proposed a cognitively plausible model for learning selectional preferences from instances of verb usage.The model represents verb selectional preferences as a semantic profile,which is a prob-ability distribution over the semantic properties that an argument can take.One of the strengths of our model is the incremental nature of its learning mech-anism,in contrast to other approaches which learn selectional preferences in batch mode.Here we have only reported the results for thefinal stage of learn-ing,but the model allows us to monitor the semantic profiles during the course of learning,and compare it with child data for different age groups,as we do with semantic roles(Alishahi and Stevenson,2007). We have shown that the model can predict appropri-ate semantic profiles for a variety of verbs,and use these profiles to simulate human judgments of verb-argument plausibility,using a small and highly noisy set of training data.The model can also use the pro-files to measure verb-argument compatibility,which was used in analyzing the implicit object alternation. ReferencesAbney,S.and Light,M.(1999).Hiding a semantic hierarchy in a Markov model.In Proc.of the ACL Workshop on Unsu-pervised Learning in Natural Language Processing. Alishahi,A.and Stevenson,S.(2005).A probabilistic model of early argument structure acquisition.In Proc.of the CogSci 2005.Alishahi,A.and Stevenson,S.(2007).A computational usage-based model for learning general properties of semantic roles.In Proc.of the EuroCogSci2007.Anderson,J.R.(1991).The adaptive nature of human catego-rization.Psychological Review,98(3):409–429. Brockmann,C.and Lapata,M.(2003).Evaluating and com-bining approaches to selectional preference acquisition.In Proc.of the EACL2003.Ciaramita,M.and Johnson,M.(2000).Explaining away am-biguity:Learning verb selectional preference with Bayesian networks.In Proc.of the COLING2000.Clark,S.and Weir,D.(2002).Class-based probability estima-tion using a semantic putational Linguistics, 28(2):187–206.Collins,M.(1999).Head-Driven Statistical Models for Natural Language Parsing.PhD thesis,University of Pennsylvania. Holmes,V.M.,Stowe,L.,and Cupples,L.(1989).Lexical expectations in parsing complement-verb sentences.Journal of Memory and Language,28:668–689.Levin,B.(1993).English verb classes and alternations:A pre-liminary investigation.The University of Chicago Press. Li,H.and Abe,N.(1998).Generalizing case frames using a thesaurus and the MDL putational Linguis-tics,24(2):217–244.Light,M.and Greiff,W.(2002).Statistical models for the in-duction and use of selectional preferences.Cognitive Sci-ence,26(3):269–281.MacWhinney,B.(1995).The CHILDES project:Tools for an-alyzing wrence Erlbaum.Miller,G.(1990).WordNet:An on-line lexical database.Inter-national Journal of Lexicography,17(3).Nation,K.,Marshall,C.M.,and Altmann,G.T.M.(2003).In-vestigating individual differences in children’s real-time sen-tence comprehension using language-mediated eye move-ments.J.of Experimental Child Psych.,86:314–329. Resnik,P.(1996).Selectional constraints:An information-theoretic model and its computational realization.Cognition, 61:127–199.。

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Introduction to Artificial Intelli智慧树知到课后章节答案2023年

Introduction to Artificial Intelli智慧树知到课后章节答案2023年

Introduction to Artificial Intelligence智慧树知到课后章节答案2023年下哈尔滨工程大学哈尔滨工程大学第一章测试1.All life has intelligence The following statements about intelligence arewrong（）A:All life has intelligence B:Bacteria do not have intelligence C:At present,human intelligence is the highest level of nature D:From the perspective of life, intelligence is the basic ability of life to adapt to the natural world答案:Bacteria do not have intelligence2.Which of the following techniques is unsupervised learning in artificialintelligence?（）A:Neural network B:Support vector machine C:Decision tree D:Clustering答案:Clustering3.To which period can the history of the development of artificial intelligencebe traced back?（）A:1970s B:Late 19th century C:Early 21st century D:1950s答案:Late 19th century4.Which of the following fields does not belong to the scope of artificialintelligence application?（）A:Aviation B:Medical C:Agriculture D:Finance答案:Aviation5.The first artificial neuron model in human history was the MP model,proposed by Hebb.（）A:对 B:错答案:错6.Big data will bring considerable value in government public services, medicalservices, retail, manufacturing, and personal location services. （）A:错 B:对答案:对第二章测试1.Which of the following options is not human reason:（）A:Value rationality B:Intellectual rationality C:Methodological rationalityD:Cognitive rationality答案:Intellectual rationality2.When did life begin? （）A:Between 10 billion and 4.5 billion years B:Between 13.8 billion years and10 billion years C:Between 4.5 billion and 3.5 billion years D:Before 13.8billion years答案:Between 4.5 billion and 3.5 billion years3.Which of the following statements is true regarding the philosophicalthinking about artificial intelligence?（）A:Philosophical thinking has hindered the progress of artificial intelligence.B:Philosophical thinking has contributed to the development of artificialintelligence. C:Philosophical thinking is only concerned with the ethicalimplications of artificial intelligence. D:Philosophical thinking has no impact on the development of artificial intelligence.答案:Philosophical thinking has contributed to the development ofartificial intelligence.4.What is the rational nature of artificial intelligence?（）A:The ability to communicate effectively with humans. B:The ability to feel emotions and express creativity. C:The ability to reason and make logicaldeductions. D:The ability to learn from experience and adapt to newsituations.答案:The ability to reason and make logical deductions.5.Which of the following statements is true regarding the rational nature ofartificial intelligence?（）A:The rational nature of artificial intelligence includes emotional intelligence.B:The rational nature of artificial intelligence is limited to logical reasoning.C:The rational nature of artificial intelligence is not important for itsdevelopment. D:The rational nature of artificial intelligence is only concerned with mathematical calculations.答案:The rational nature of artificial intelligence is limited to logicalreasoning.6.Connectionism believes that the basic element of human thinking is symbol,not neuron; Human's cognitive process is a self-organization process ofsymbol operation rather than weight. （）A:对 B:错答案:错第三章测试1.The brain of all organisms can be divided into three primitive parts:forebrain, midbrain and hindbrain. Specifically, the human brain is composed of brainstem, cerebellum and brain (forebrain). （）A:错 B:对答案:对2.The neural connections in the brain are chaotic. （）A:对 B:错答案:错3.The following statement about the left and right half of the brain and itsfunction is wrong （）.A:When dictating questions, the left brain is responsible for logical thinking,and the right brain is responsible for language description. B:The left brain is like a scientist, good at abstract thinking and complex calculation, but lacking rich emotion. C:The right brain is like an artist, creative in music, art andother artistic activities, and rich in emotion D:The left and right hemispheres of the brain have the same shape, but their functions are quite different. They are generally called the left brain and the right brain respectively.答案:When dictating questions, the left brain is responsible for logicalthinking, and the right brain is responsible for language description.4.What is the basic unit of the nervous system?（）A:Neuron B:Gene C:Atom D:Molecule答案:Neuron5.What is the role of the prefrontal cortex in cognitive functions?（）A:It is responsible for sensory processing. B:It is involved in emotionalprocessing. C:It is responsible for higher-level cognitive functions. D:It isinvolved in motor control.答案:It is responsible for higher-level cognitive functions.6.What is the definition of intelligence?（）A:The ability to communicate effectively. B:The ability to perform physicaltasks. C:The ability to acquire and apply knowledge and skills. D:The abilityto regulate emotions.答案:The ability to acquire and apply knowledge and skills.第四章测试1.The forward propagation neural network is based on the mathematicalmodel of neurons and is composed of neurons connected together by specific connection methods. Different artificial neural networks generally havedifferent structures, but the basis is still the mathematical model of neurons.（）A:对 B:错答案:对2.In the perceptron, the weights are adjusted by learning so that the networkcan get the desired output for any input. （）A:对 B:错答案:对3.Convolution neural network is a feedforward neural network, which hasmany advantages and has excellent performance for large image processing.Among the following options, the advantage of convolution neural network is（）.A:Implicit learning avoids explicit feature extraction B:Weight sharingC:Translation invariance D:Strong robustness答案:Implicit learning avoids explicit feature extraction;Weightsharing;Strong robustness4.In a feedforward neural network, information travels in which direction?（）A:Forward B:Both A and B C:None of the above D:Backward答案:Forward5.What is the main feature of a convolutional neural network?（）A:They are used for speech recognition. B:They are used for natural languageprocessing. C:They are used for reinforcement learning. D:They are used forimage recognition.答案:They are used for image recognition.6.Which of the following is a characteristic of deep neural networks?（）A:They require less training data than shallow neural networks. B:They havefewer hidden layers than shallow neural networks. C:They have loweraccuracy than shallow neural networks. D:They are more computationallyexpensive than shallow neural networks.答案:They are more computationally expensive than shallow neuralnetworks.第五章测试1.Machine learning refers to how the computer simulates or realizes humanlearning behavior to obtain new knowledge or skills, and reorganizes the existing knowledge structure to continuously improve its own performance.（）A:对 B:错答案:对2.The best decision sequence of Markov decision process is solved by Bellmanequation, and the value of each state is determined not only by the current state but also by the later state.（）A:对 B:错答案:对3.Alex Net's contributions to this work include: （）.A:Use GPUNVIDIAGTX580 to reduce the training time B:Use the modified linear unit (Re LU) as the nonlinear activation function C:Cover the larger pool to avoid the average effect of average pool D:Use the Dropouttechnology to selectively ignore the single neuron during training to avoid over-fitting the model答案:Use GPUNVIDIAGTX580 to reduce the training time;Use themodified linear unit (Re LU) as the nonlinear activation function;Cover the larger pool to avoid the average effect of average pool;Use theDropout technology to selectively ignore the single neuron duringtraining to avoid over-fitting the model4.In supervised learning, what is the role of the labeled data?（）A:To evaluate the model B:To train the model C:None of the above D:To test the model答案:To train the model5.In reinforcement learning, what is the goal of the agent?（）A:To identify patterns in input data B:To minimize the error between thepredicted and actual output C:To maximize the reward obtained from theenvironment D:To classify input data into different categories答案:To maximize the reward obtained from the environment6.Which of the following is a characteristic of transfer learning?（）A:It can only be used for supervised learning tasks B:It requires a largeamount of labeled data C:It involves transferring knowledge from onedomain to another D:It is only applicable to small-scale problems答案:It involves transferring knowledge from one domain to another第六章测试1.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is （）.A:Region growth method is to complete the segmentation by calculating the mean vector of the offset. B:Watershed algorithm, MeanShift segmentation,region growth and Ostu threshold segmentation can complete imagesegmentation. C:Watershed algorithm is often used to segment the objectsconnected in the image. D:Otsu threshold segmentation, also known as themaximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire image答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.2.Camera calibration is a key step when using machine vision to measureobjects. Its calibration accuracy will directly affect the measurementaccuracy. Among them, camera calibration generally involves the mutualconversion of object point coordinates in several coordinate systems. So,what coordinate systems do you mean by "several coordinate systems" here?（）A:Image coordinate system B:Image plane coordinate system C:Cameracoordinate system D:World coordinate system答案:Image coordinate system;Image plane coordinate system;Camera coordinate system;World coordinate systemmonly used digital image filtering methods:（）.A:bilateral filtering B:median filter C:mean filtering D:Gaussian filter答案:bilateral filtering;median filter;mean filtering;Gaussian filter4.Application areas of digital image processing include:（）A:Industrial inspection B:Biomedical Science C:Scenario simulation D:remote sensing答案:Industrial inspection;Biomedical Science5.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is ( ).A:Otsu threshold segmentation, also known as the maximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire imageB: Watershed algorithm is often used to segment the objects connected in the image. C:Region growth method is to complete the segmentation bycalculating the mean vector of the offset. D:Watershed algorithm, MeanShift segmentation, region growth and Ostu threshold segmentation can complete image segmentation.答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.第七章测试1.Blind search can be applied to many different search problems, but it has notbeen widely used due to its low efficiency.（）A:错 B:对答案:对2.Which of the following search methods uses a FIFO queue （）.A:width-first search B:random search C:depth-first search D:generation-test method答案:width-first search3.What causes the complexity of the semantic network （）.A:There is no recognized formal representation system B:The quantifiernetwork is inadequate C:The means of knowledge representation are diverse D:The relationship between nodes can be linear, nonlinear, or even recursive 答案:The means of knowledge representation are diverse;Therelationship between nodes can be linear, nonlinear, or even recursive4.In the knowledge graph taking Leonardo da Vinci as an example, the entity ofthe character represents a node, and the relationship between the artist and the character represents an edge. Search is the process of finding the actionsequence of an intelligent system.（）A:对 B:错答案:对5.Which of the following statements about common methods of path search iswrong（）A:When using the artificial potential field method, when there are someobstacles in any distance around the target point, it is easy to cause the path to be unreachable B:The A* algorithm occupies too much memory during the search, the search efficiency is reduced, and the optimal result cannot beguaranteed C:The artificial potential field method can quickly search for acollision-free path with strong flexibility D:A* algorithm can solve theshortest path of state space search答案:When using the artificial potential field method, when there aresome obstacles in any distance around the target point, it is easy tocause the path to be unreachable第八章测试1.The language, spoken language, written language, sign language and Pythonlanguage of human communication are all natural languages.（）A:对 B:错答案:错2.The following statement about machine translation is wrong （）.A:The analysis stage of machine translation is mainly lexical analysis andpragmatic analysis B:The essence of machine translation is the discovery and application of bilingual translation laws. C:The four stages of machinetranslation are retrieval, analysis, conversion and generation. D:At present,natural language machine translation generally takes sentences as thetranslation unit.答案:The analysis stage of machine translation is mainly lexical analysis and pragmatic analysis3.Which of the following fields does machine translation belong to? （）A:Expert system B:Machine learning C:Human sensory simulation D:Natural language system答案:Natural language system4.The following statements about language are wrong: （）。

高三英语人工智能单选题40题

高三英语人工智能单选题40题

高三英语人工智能单选题40题1. Artificial intelligence is a branch of computer science that aims to create intelligent _____.A.machinesB.devicesC.equipmentsD.instruments答案解析：A。

“machines”通常指机器，在人工智能领域常指具有一定智能的机器；“devices”一般指设备、装置；“equipments”是“equipment”的复数形式，指装备、器材；“instruments”指仪器、工具。

2. In the field of artificial intelligence, algorithms are used to train _____.A.modelsB.patternsC.shapesD.forms答案解析：A。

“models”在人工智能中常指模型，可以通过算法进行训练；“patterns”指模式、图案；“shapes”指形状；“forms”指形式。

3. Deep learning is a powerful technique in artificial intelligence that uses neural _____.worksB.systemsC.structuresanizations答案解析：A。

“networks”指网络，在深度学习中常指神经网络；“systems”指系统；“structures”指结构；“organizations”指组织。

4. Artificial intelligence can process large amounts of data to make accurate _____.A.predictionsB.forecastsC.projectionsD.expectations答案解析：A。

Context-dependence

Context-dependence

Context dependence and cultural models冯越高慧清2013-3-20Context dependency⏹The context-dependence of the wholeinternal category structurePrototypesPeripheral membersCategory scopeAttributes⏹What is '' context'' ?Context, Situation, Cognitive models，Cultural modelsWhat kind of dog is suggested to you?(1)The hunter took his gun, left the lodge and calledhis dog.(2)Right from the start of the race the dog beganchasing the rabbit.(3)She took her dog to the salon to have its curlsreset.(4)The policemen lined up with the dogs to face therioters.Context dependency Right from the startof the race the dogbegan chasing therabbit.Greyhound灰狗Context dependency She took her dogto the salon tohave its curlsreset.Poodle狮子狗Context dependency The policemenlined up with thedogs to face therioters.Alsatian阿尔萨斯狗⏹Depending on the context, the prototypes shifts.⏹In some cases the category structure(scope)of the context-dependent category is much more leaner than that of non-contextualized category.⏹The context not only determines the choice of category prototype, but also leads to an adjustment of other category members(peripheral members).⏹The context can change the weight of attributes that seem to be relevant for a certain category.⏹The context can emphasize attributes that are not prominent and even introduce new attributes which would not be mentioned at all in non-contextualized attribute-listing experiment.⏹The prototypes of cognitive categories are not fixed, but change when a particular context is introduced.⏹And the same is for catrgory boundaries.⏹More generally, the whole internal structure of a category seems to depend on the context, and in a wider sense, on our social and cultural knowledge, which is thought to be organized in cognitive and cultural models.What is "context" ?⏹From a purely linguistic point of view the context has been regarded as the linguistic material preceding and following a word or sentence.⏹The set of background assumptions that are necessary for an utterance to be intelligible. (Searle, Language philosophers and Pragmalinguists)⏹The "context of situation" and the " context of culture" are necessary for a proper understanding of utterance or text.( Malinowski, discourse-oriented approaches to language)⏹For cognitive linguists, context is a mental phenomenon. "as a context for the characterization of a semantic unit" (Langacker)Context vs Situation⏹Situation: the interactions between objects inthe real world⏹Context: a cognitive representation of the interaction between the mental concepts we have of the objects(cognitive categories).Cognitive Model⏹All the stored cognitive representations that belong to a certain field.⏹Three Properties:Open-endedInterrelatedOmnipresentOpen-ended(Incompleteness) It's very hard to describe the cognitive model of a domain and that descriptions of cognitive models are never exhaustive, but always highly selective.Interrelated(Not isolated) Cognitive models combine to build networks.OmnipresentIn every act of categorization we aremore or less consciously refering to oneor several cognitive models that we have stored.Even if we encounter a totally unfamiliar object or situation where no appropriate cognitive model is available, we will presumably try to call up similar experiences and immediately form a cognitive model.。

考研英语二模拟试题及答案解析(17)

考研英语二模拟试题及答案解析(17)

考研英语二模拟试题及答案解析（17）(1~20/共20题)Section ⅠUse of EnglishDirections:Read the following text. Choose the best word(s) for each numbered blank and mark A, B, C or D on ANSWER SHEET 1.A new economics paper has some old-fashioned advice for people navigating the stresses of life: Find a spouse who is also your best friend. Social scientists have long known that __1__ people tend to be happier, but they debate whether that is because marriage causes happiness or simply because happier people are more__2__ to get married. The new paper, __3__ by the National Bureau of Economic Research, controlled for pre-marriage happiness levels. It__4__that being married makes people happier and more satisfied __5__ their lives than those who remain single—particularly during the most stressful periods, like __6__ crises.Even as fewer people are marrying, the disadvantages of remaining single have broad__7__. It´s important__8__marriage is increasingly a force behind inequality.__9__marriages are more common among educated, high-income people, and increasingly out of reach for those who are not. That divide appears to__10__not just people´s income and family stability, but also their happiness and stress levels.A quarter of today´s young adults will have never married by 2030, which would be the highest__11__in modern history, according to Pew Research Center.__12__both remaining unmarried and divorcing are more common among less-educated, lower-income people.__13__, high-income people still marry at high rates and are less likely to divorce.Those whose lives are most difficult could__14__most from marriage, according to the economists who wrote the new paper, John Helliwell and Shawn Grover. "Marriage may be most important when there is that stress in life and when things are going__15__," Mr. Grover said.__16__ marital happiness long outlasted the honeymoon period.__17__some social scientists have argued that happiness levels are innate, so people return to their natural level of well-being__18__joyful or upsetting events, the researchers found that the benefits of marriage persist. One__19__for that might be the role of friendship within marriage. Those who__20__their spouse or partner to be their best friend get about twice as much life satisfaction from marriage as others, the study found.第1题A.singleB.engagedC.marriedD.divorced第2题A.eagerB.likelyC.easyD.excited第3题A.madeB.studiedC.foundD.published第4题A.deniedB.concludedC.doubtedD.imagined第5题A.withB.fromC.byD.in第6题A.financialB.midlifeC.academicD.quarterlife第7题A.necessitiesB.transmissionsC.implicationsD.significances 第8题A.whenB.becauseC.soD.if第9题A.StableB.NormalC.ConstantD.Poor第10题A.affectB.promoteC.lowerD.control第11题A.figureB.rateC.scoreD.share第12题A.EvenB.StillC.YetD.And第13题catedB.IntelligentC.DignifiedD.Knowledgeable第14题A.stealB.chooseC.benefitD.suffer第15题A.wrongB.badC.greatD.well第16题A.FortunatelyB.UnexpectedlyC.IntriguinglyD.Surprisingly第17题A.UnlessB.HenceC.UntilD.Though第18题A.afterB.beforeC.duringD.within第19题A.optionB.reasonC.resultD.tendency第20题A.believeB.seeC.considerD.regard下一题(21~25/共20题)Section ⅡReading ComprehensionDirections :Read the following four terts. Answer the questions below each text by choosing [A], [B],[C]or [D].Mark your answers on ANSWER SHEET 1.The Nobel Prize is a mysterious incarnation of power and authority, an anointed ritual whose claims are accepted as part of the order of things. The Nobel Prize is at once a relic of the past and a self-admiring mirror of our democratized, scientized, secularized modern culture. After a century of existence, the Nobel has become a problematic part of modern history: it helps shape our perception of ourselves for better or worse.Like monarchy, the Nobel Prize surrounds itself with mystery and extraordinary secretiveness. Indeed, the media have more easily breached the privacy of the British royal family than that of the Nobel institution.The prizes present themselves as if handed down from eternity. But as even a cursory inspection will reveal, the juries that pick the laureates have often shown bias, lapses of judgment and bitter infighting. In the sciences, a number of quarrels, scandals, and even lawsuits have erupted over claims to priority or credit for collaborations honored by Nobles. And while widely admired, the science prizes have also been charged with swaying research goals and funding, however inadvertently, and more insidiously with corrupting scientific ambitions by the lure of Nobel fames. Such controversies, together with public dissent from several prizes, have been part of the Nobel history since its beginning. All prizes stir arguments; the Nobel´s fame simply magnifies this hugely.The very glory and stature of the Nobel Prizes prompt some sharp questions. Should such high endeavors of the human spirit as science, literature, and peace be treated as competitions, however exalted? Should these priceless efforts be paid the enormous, though "honorary," price lavished on the winners? Would it matter if there never had been a Nobel Price? Or if it vanished tomorrow? The problem is that the prices are not merely awards and medals but are aspects of power woven into our lives: it was once and future Nobelists who built the nuclear bombs that still hang fatefully over us; Nobelists now play an important part in public and military policy.It is also true that the Nobel Prizes show modern fame: the Nobel Prize pays honor to some of the highest human adventures in nature and matter, creativity and justice. Where else, moreover, can both the unfamed and the general public find a replacement for the authority and coherence, to whatever degree, the Nobel has come to possess? In a world and age as inwardly fractured as ours this is a question not lightly dismissed.第21题The Nobel has become a problematic part of modern history because ______.A.it reflects the modern world and results in many follow-up questionsB.it dismisses authority and coherenceC.the standard for the prize is unclear and arouses many questionsD.the prize cannot be seen as a driving force for social development第22题Which of the following statements is true about the Nobel Prize?A.The prizes need to be diversified.B.The prizes are unexpected to the winners.C.The prize manifests authority and coherence.D.Prejudice does not exist among the panel of judges any longer.第23题The author´s attitude towards Nobel Prize is______.A.ambiguousB.negativeC.objectiveD.critical第24题The word "fractured" (Line 5, Paragraph 4) probably imply that______.A.alienation is seen as one of the problems of modern worldB.people are stratified and separated by modern technologyC.the Nobel Prize is accepted by people from all walks of lifeD.the world is diversified and Nobel does not necessarily mean authority第25题Which of the following would be the best title for the text?A.The Authority of NobelB.On the Glory of NobelC.Nobel and the Modern WorldD.A mirror of Modern Culture上一题下一题(26~30/共20题)Section ⅡReading ComprehensionDirections :Read the following four terts. Answer the questions below each text by choosing [A], [B],[C]or [D].Mark your answers on ANSWER SHEET 1.Psychologists have known for a century that individuals vary in their cognitive ability. But are some groups, like some people, reliably smarter than others? In order to answer that question, we grouped 697 volunteer participants into teams of two to five members. Each team worked together to complete a series of short tasks, which were selected to represent the varied kinds of problems that groups are called upon to solve in the real world. One task involved logical analysis, another brainstorming; others emphasized coordination, planning and moral reasoning. Individual intelligence, as psychologists measure it, is defined by its generality: People with good vocabularies, for instance, also tend to have good math skills, even though we often think of those abilities as distinct. The results of our studies showed that this same kind of general intelligence also exists for teams. On average, the groups that did well on one task did well on the others, too. In other words, some teams were simply smarter than others.We found the smartest teams were distinguished by three characteristics. First, their members contributed more equally to the team´s discussions, rather than letting one or two people dominate the group. Second, their members scored higher on a test called Reading the Mind in the Eyes, which measures how well people can read complex emotional states from images of faces with only the eyes visible. Finally, teams with more women outperformed teams with more men. This last effect, however, was partly explained by the fact that women, on average, were better at "mindreading" than men.In a new study, we replicated these earlier findings. We randomly assigned each of 68 teams to complete our collective intelligence test in one of two conditions. Half of the teams worked face to face. The other half worked online, with no ability to see any of their teammates. We wanted to see whether groups that worked online would still demonstrate collective intelligence, andwhether social ability would matter as much when people communicated purely by typing messages into a browser.And they did. Online and off, some teams consistently worked smarter than others. More surprisingly, the most important ingredients for a smart team remained constant regardless of its mode of interaction: members who communicated a lot, participated equally and possessed good emotion-reading skills.第26题It can be inferred from the first paragraph that______.A.some groups are really smarter than othersB.the 697 volunteer participants need to complete a series of short tasks togetherC.the selected short tasks must have practical significanceD.logical analysis and brainstorming are important in each task第27题According to psychologists, individual intelligence______.A.is characterized by generalityB.is related to math skillsC.is not related to teamsD.is key to smarter teams第28题According to the author, the characteristics of smarter teams include all the following EXCEPT ______.A.the members have relatively equal contribution to the team´ s discussionsB.the members have a higher IQC.the members have a stronger ability of reading complex facial expressionsD.there are more female members than other teams第29题In a new study, the other half of people work online because______.A.it´ s necessary to ensure the accuracy of the experimentB.online collaboration is becoming more and more importantC.the experimenters want to see whether collective intelligence will be showedD.the experimenters want to prove social ability is vital to every team第30题The best title for the passage may be______.A.How to Develop a Smarter TeamB.Why Some Teams Are Smarter Than OthersC.The Characteristics of Smarter TeamD.What Factors can Affect a Team上一题下一题(31~35/共20题)Section ⅡReading ComprehensionDirections :Read the following four terts. Answer the questions below each text by choosing [A], [B],[C]or [D].Mark your answers on ANSWER SHEET 1.Many of the tech industry´s biggest companies, like Amazon, Google, IBM and Microsoft, are jockeying to become the leader for artificial intelligence(A.I.). In the industry´s term, thecompanies are engaged in a "platform war."A platform, in technology, is essentially a piece of software that other companies build on and that consumers cannot do without. Become the platform and huge profits will follow. Microsoft dominated personal computers because its Windows software became the center of the consumer software world. Google has come to dominate the Internet through its ubiquitous search bar. If true believers in A.I. are correct that this long-promised technology is ready for the mainstream, the company that controls A.I. could steer the tech industry for years to come. "Whoever wins this race will dominate the next stage of the information age," said Pedro Domingos, a machine learning specialist and the author of "The Master Algorithm," a 2015 book that contends that A.I. and big-data technology will remake the world.In this fight—no doubt in its early stages—the big tech companies are engaged in tit-for-tat publicity stunts, circling the same start-ups that could provide the technology pieces they are missing and, perhaps most important, trying to hire the same brains. Fei-Fei Li, a Stanford University professor who is an expert in computer vision, said one of her Ph.D. candidates had an offer for a job paying more than $1 million a year, and that was only one of four from big and small companies.For years, tech companies have used man-versus-machine competitions to show they are making progress on A.I. In 1997, an IBM computer beat the chess champion Garry Kasparov. Five years ago, IBM went even further when its Watson system won a three-day match on the television trivia show "Jeopardy!" Today, Watson is the centerpiece of IBM´s A.I. efforts.By 2020, the market for machine learning applications will reach $40 billion, IDC, a market research firm, estimates. And 60 percent of those applications, the firm predicts, will run on the platform software of four companies—Amazon, Google, IBM and Microsoft. Intelligent software applications will become commonplace, said Jeff Dean, a computer scientist who oversees Google´s A.I. development. "And machine learning will touch every industry."第31题The reason for Microsoft controlling personal computers is that______.A.it has formed a good platformB.its software has gained a lot of moneyC.its Windows software became popular among consumersD.its software has been widely used in search bar第32题Google can steer the tech industry in the future on premise that______.A.it has controlled the InternetB.A.I. has prepared to be the mainstreamC.it has won in the information ageD.A.I. and big-data technology have become mature第33题The text takes "Fei-Fei Li" as an example in Paragraph 3 to show that______.A.the competition between tech companies is fierceB.the candidates from famous universities are more popularC.the higher the pay is, the more likely the candidate is to accept the offerD.perhaps tech companies are striving for the same talents第34题In which way the tech companies have used to show their progress?petitions between men and machines.B.Chess games. shows.D.The profitability of new products.第35题What´s the author´ s attitude towards the future of A.I.?A.Negative.B.Positive.C.Unclear.D.Indifferent.上一题下一题(36~40/共20题)Section ⅡReading ComprehensionDirections :Read the following four terts. Answer the questions below each text by choosing [A], [B],[C]or [D].Mark your answers on ANSWER SHEET 1.Some futurologists have assumed that the vast upsurge of women in the workforce may portend a rejection of marriage. Many women, according to this hypothesis, would rather work than marry. The converse of this concern is that the prospects of becoming a multi -paycheck household could encourage marriages. In the past, only the earnings and financial prospects of the man counted in the marriage decision. Now, however, the earning ability of a woman can make her more attractive as a marriage partner. Data show that economic downturns tend to postpone marriage because the parties cannot afford to establish a family or are concerned about rainy days ahead. As the economy rebounds, the number of marriages also rises. Coincident with the increase in women working outside the home is the increase in divorce rates. Yet, it may be wrong to jump to any simple cause-and-effect conclusions. The impact of a wife´ s work on divorce is no less cloudy than its impact on marriage decisions. The realization that she can be a good provider may increase the chances that a working wife will choose divorce over an unsatisfactory marriage. But the reverse is equally plausible. Tensions grounded in financial problems often play a key role in ending a marriage. Given high unemployment, inflationary problems, and slow growth in real earnings, a working wife can increase household income and relieve some of these pressing financial burdens. By raising a family´ s standard of living, a working wife may strengthen her family´ s financial and emotional stability. Psychological factors also should be considered. For example, a wife blocked from a career outside the home may feel caged in the house. She may view her only choice as seeking a divorce. On the other hand, if she can find fulfillment through work outside the home, work and marriage can go together to create a stronger and more stable union.Also, a major part of women´s inequality in marriage has been due to the fact that, in most cases, men have remained the main breadwinners. With higher earning capacity and status occupations outside of the home comes the Capacity to exercise power within the family. A working wife may rob a husband of being the master of the house. Depending upon how the couple reacts to these new conditions, it could create a stronger equal partnership or it could create new insecurities.第36题The word "portend"(Sentence 1, Para. 1)is closest in meaning to "______".A.defyB.signalC.suffer fromD.result from第37题It is said in the passage that when the economy slides,______.A.men would choose working women as their marriage partnersB.more women would get married to seek financial securityC.even working women would worry about their marriagesD.more people would prefer to remain single for the time being第38题If women find fulfillment through work outside the home,______.A.they are more likely to dominate their marriage partnersB.their husbands are expected to do more houseworkC.their marriage ties can be strengthenedD.they tend to put their career before marriage第39题One reason why women with no career may seek a divorce is that______.A.they feel that they have been robbed of their freedomB.they are afraid of being bossed around by their husbandsC.they feel that their partners fail to live up to their expectationsD.they tend to suspect their husbands´ loyalty to their marriage第40题Which of the following statements can best summarize the author´s view in the passage?A.The stability of marriage and the divorce rate may reflect the economic situation of the country.B.Even when economically independent, most women have to struggle for real equality in marriage.C.In order to secure their marriage women should work outside the home and remain independent.D.The impact of the growing female workforce on marriage varies from case to case.上一题下一题(41~45/共5题)Part BDirections :Read the following tert and decide whether each of the statements is true or false. Choose T if the state ment is true or F if the statement is not true. Mark your answers on ANSWER SHEET 1.[A]Think about how your attitude change will affect your life[B]Look for a role model[C]Be clear about your traits[D]Identify and understand what you want to change[E]Believe that you are able to change[F]Get rid of the pessimistic friends[G]Choose the right company"Our attitude toward life determines life´s attitude towards us." We´ve all heard about the power of our attitude, and that it´s our attitude that determines how much we succeed in life.If you look around you, you will see that people with a positive attitude enjoy life more and are generally happier and more successful than those who walk around grumpy and pessimistic. Our attitude is the driving force in our lives—it can either push you to do great things or pull you down to your demise.All the things that you have been through, all the people you have met and interacted with can have an impact on your attitude. If you think that all these factors have molded you into a person with a poor attitude towards life, there is no need to worry as there is always an opportunity for change. Let me share with you how I did it.__41__The first step towards change is clearly understanding what needs to be changed. Setting clear goals is the key to success in any endeavor. When it comes to changing your attitude, you need to do an honest and in-depth self-evaluation so you could point out exactly which of your traits need to be improved or totally changed.__42__We all need to know that what we´re trying to accomplish can in fact be achieved; that we can be more optimistic, more social or more patient. Find someone who has the kind of attitude that you want to have, and let his or her life give you inspiration and encouragement to move beyond your temporary failures in your journey towards becoming a better person.__43__To be able to overcome all the difficulties that lie ahead of you in your journey towards self betterment, you need to figure out exactly what this supposed change could bring to your life. Will changing your attitude mean a happier family or social life? Will a change in your attitude mean a more successful career or business? Fix your mind on the things that would come as a result of your attitude change and you will have a greater chance of reaching your goal.__44__As they say, "Bad company corrupts good character." You don´ t expect yourself to be able to change if you go on surrounding yourself with people who possess all the negative traits that you want to change. Consider befriending new people, especially those who are optimistic and have a healthy attitude towards life. You will see that your effort to change will be easier with these kinds of people as friends.__45__Often, the greatest obstacle between us and our goals is ourselves or our inability to trust in what we are able to do. If you don´t believe in yourself or believe that you or your life can change, it just won´t happen—you will either never start, or give up quickly so you won´t have even given yourself the opportunity to succeed.It cannot be denied that a positive attitude is very important for living a successful and satisfying life, so it is only right to strive to have a positive attitude.第41题第42题第43题第44题第45题上一题下一题(1/1)Part CDirections: Read the following text carefully and then translate the underlined segments into Chinese. (10 points)第46题For a child, happiness has a magical quality. I remember making hide-outs in newly cut hay, playing cops and robbers in the woods,getting a speaking part in the school play. For adults, happiness is complicated. As far as I am concerned, happiness is "the capacity for enjoyment". The more we can enjoy what we have, the happier we are. It´ s easy to overlook the pleasure we get from loving and being loved, the company of friends, the freedom to live where we please, even good health.While happiness may be more complex for us, the solution is the same as ever. Happiness isn´t about what happens to us; it´ s about how we perceive what happens to us. It´ s the knack of finding a positive for every negative, and viewing a set-back as a challenge. It´ s not wishing for what we don´ t have, but enjoying what we do possess._____________上一题下一题(1/1)Section WritingPart A第47题Suppose you have damaged your friend´ s computer when you lived in his house a few days ago. Write him a letter to1)make an apology, and2)suggest a solution.You should write about 100 words on the ANSWER SHEET.Do not use your own name. Use "Li Ming" instead.Do not write the address.(10 points)____________上一题下一题(1/1)Part B第48题Write an essay based on the following pie chart. In your writing, you should1)interpret the chart, and2)give your comments.You should write about 150 words on the ANSWER SHEET.(15 points)图片_______________上一题交卷交卷答题卡答案及解析(1~20/共20题)Section ⅠUse of EnglishDirections:Read the following text. Choose the best word(s) for each numbered blank and mark A, B, C or D on ANSWER SHEET 1.A new economics paper has some old-fashioned advice for people navigating the stresses of life: Find a spouse who is also your best friend. Social scientists have long known that __1__ people tend to be happier, but they debate whether that is because marriage causes happiness or simply because happier people are more__2__ to get married. The new paper, __3__ by the National Bureau of Economic Research, controlled for pre-marriage happiness levels. It__4__that being married makes people happier and more satisfied __5__ their lives than those who remain single—particularly during the most stressful periods, like __6__ crises.Even as fewer people are marrying, the disadvantages of remaining single have broad__7__. It´s important__8__marriage is increasingly a force behind inequality.__9__marriages are more common among educated, high-income people, and increasingly out of reach for those who are not. That divide appears to__10__not just people´s income and family stability, but also their happiness and stress levels.A quarter of today´s young adults will have never married by 2030, which would be the highest__11__in modern history, according to Pew Research Center.__12__both remaining unmarried and divorcing are more common among less-educated, lower-income people.__13__, high-income people still marry at high rates and are less likely to divorce.Those whose lives are most difficult could__14__most from marriage, according to the economists who wrote the new paper, John Helliwell and Shawn Grover. "Marriage may be most important when there is that stress in life and when things are going__15__," Mr. Grover said.__16__ marital happiness long outlasted the honeymoon period.__17__some social scientists have argued that happiness levels are innate, so people return to their natural level of well-being__18__joyful or upsetting events, the researchers found that the benefits of marriage persist. One__19__for that might be the role of friendship within marriage. Those who__20__their spouse or partner to be their best friend get about twice as much life satisfaction from marriage as others, the study found.第1题A.singleB.engagedC.marriedD.divorced参考答案： C 您的答案：未作答答案解析：此处意为“社会科学家向来知道，______人士往往更幸福”。