Far CH3 Class Question

浙江省五校高三第二次联考英语试题(解析版)2015学年浙江省第二次五校联考英语试题卷说明：本试卷共5大题，满分120分，考试时间120分钟。

所有试题必须在答题卡上作答。

选择题部分（共80分）第一部分英语知识运用（共两节，满分30分）第一节单项填空（共20题；每小题0.5分，满分10分）从A、B、C、D四个选项中，选出可以填入空白处的最佳选项。

1. —Mike told me yesterday that he had been looking in vain for a job in the art gallery.—______? If I remember right, he had a chance to work there, but he turned it down.A. SorryB. So whatC. ReallyD. Why not2. David Bowie, a Britain’s most influential songwriter for decades, died on Jan10 at ______ age 69 aft er ______ long battle with cancer.A. the; aB. /; theC. an; theD. /; a3. In the terrible traffic accident the bicycle was so badly damaged that it was almost ______ repair.A. underB. withoutC. beyondD. within4. —Oh dear! Look at the mountains of trash in the street and the terrible haze these days!—Everyone’s effort counts. I ______ fireworks on New Year’s Eve!A. needn’t have playedB. mustn’t have playedC. shouldn’t have playedD. couldn’t have played5. This new app enables you to create a more efficient workflow, by ______ the time it takes to move between different programs.A. decliningB. devotingC. shorteningD. consuming6. Researchers have been determined to ______ how bird flu spreads from human to human worldwide.A. turn outB. figure outC. pick outD. check out7. ______ matters is the selection of the right website, ______ can offer the daily news and entertainment, and you can know everything in a matter of 30 minutes.A. Whatever; whichB. However; whichC. Whatever; whereD. However; where8. Because society ______ so rapidly during the last few years, the gap between the generations has become larger.A. changedB. has been changingC. had changedD. was changed9. The problem of smog is becoming increasingly serious. ______, the citizens need to wear masks when going out.A. On the contraryB. In other wordsC. As a consequenceD. On the whole10. ______ the bad weather stops me, I go for a walk every day.A. DespiteB. UntilC. AsD. Unless11. After a short break, they _____ their discussion, hoping to reach an agreement.A. accounted forB. carried onC. went afterD. ended up12. She hates _____ when someone speaks to her without looking into her eyes.A. thatB. oneC. itD. this13. He was so nervous on the stage that he found himself moving his steps _____, not like a skillful dancer.A. deliberatelyB. anxiouslyC. firmlyD. clumsily14. Happiness is a temporary state, not a(n) _____ one, according to some psychologists.A. abnormalB. optimisticC. permanentD. steady15. The signing of this agreement will ease the international _____, which might bring an end to conflicts betweencountries.A. tensionB. regulationC. processD. affection16. After years of hard work, they were finally able to ______ an apartment of their own in the smallcity with all their savings.A. preserveB. purchaseC. presentD. produce17. In recent years, video games have become more accessible, ______ mobile platforms.A. according toB. in response toC. thanks toD. contrary to18. He seems _____ on it for days, for it is almost finished.A. to workB. to have workedC. having workedD. working19. So disappointed______ to be turned back on by my closest friend that I was determined not to speak to her for the rest of my life.A. I feltB. have I feltC. I did feelD. did I feel20. — Hi, Jack. Could you spare me a minute? I have a question for you.—_____A. Yes, don’t worry.B. Of course, go ahead.C. Yes, help yourself.D. Of course, why not?第二节完形填空（共20小题；每小题1分，共20分）阅读下面短文，掌握其大意，然后从21-40各题所给的四个选项（A、B、C和D）中，选出最佳选项。

Building a Question Classifier for a TREC-StyleQuestion Answering SystemRichard May&Ari SteinbergTopic:Question ClassificationWe define Question Classification(QC)here to be the task that,given a question,maps it to one of k classes,which provide a semantic constraint on the sought-after answer[Li02].The topic of Question Classification arises in the area of automated question-answering systems,such as those created for the TREC question answering competition.Automated question-answering systems differ from other information retrieval systems(ie.search engines) in that they do not return a list of documents with possible relevance to the topic,but rather return a short phrase containing the answer.Moreover, question answering systems take in as input queries expressed in natural language rather than the keywords traditional search engines use.In order to respond correctly to a free form factual question given a large collection of texts,any system needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer.These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.More specifically,know-ing the class(or possible classes)of the sought after answer narrows down the number of possible phrases/paragraphs a question-answering system has to consider,and thus greatly improves performance of the overall system. [Harabagiu]divides their QA system into three distinct pieces.At the core of thefirst module in the system lies a question classification task.Thus, it seems that question classification is an important subtask of automated question answering.An error in question classification will almost undoubt-edly throw offthe entire QA pipeline,so it is crucial to be able to classifiy question correctly.In our paper,we will build on the hierarchal classification discussed in[Li02] and experiment with some features of our own design.We expect that by tweaking both the classification algorithms and the choice of features,we can get improvements in this crucial QA subsystem.1ClassificationClassification MethodologyResults from[Li02]demonstrated that using aflat classifier performs just as well as a two-layer hierarchical classifier that used a coarse classifier to dispatch the classification task to a second classifier.We too,plan on using a hierarchal classifier,however,ours will differ in that we will also attempt to learn which classes are often confused,whereas[Li02]applied domain knowledge to the task and created six hand crafted super classes that then contained distinct subsets of the true classes.Classification SystemFor our code base,we leverage an existing machine learning library,MAL-LET,discussed in[McCallum].We designed a hiearchal classifier trainer, which takes in a training set and partitions it into a base train set and an advanced train set.The trainer then uses the base train set to train the coarse classifier over all the possible question types.The trainer then tests the coarse classifier on the advanced training data,to build a confusion ing a set of threshold parameters,the trainer decides if certain predicted classes have too high a confusion rate and then trains a secondary classifier on the advanced training instances that were predicted to be part of the high confusion rate classes.Figure1:Diagram of how to train a hierarchal classifierFeaturesThe majority of our creative energies were focused into feature engineering. It is a process that requires a lot of trial and error.We intend to leverage2resources in WordNet to improve on semantic understanding of the ques-tions.As input to our machine learning algorithm,the computer examines each question and derives a representation consisting of numerous features.In the end,a typical question can have as many as60features,and our set of 6000total questions can result in anywhere from30,000to120,000unique features.While it would be easy to generate far more than this,due to memory constraints we must be careful in selecting only the most useful types of features to include in this set.In addition,adding extraneous features can add noise to the data and result in weaker performance.On the other hand,large performance improvements can be gained by adding useful features to the set.Basic featuresThe most basic representation of a question is simply the individual words of that question(ignoring contextual information such as the ordering of the words).While simple,this is also by far the most important part of our program-the best indicators of certain question types are single words,and in particular question words clearly reveal a lot about the type of question being asked.On the other hand,a large amount of information would be lost by stopping here,since words can often mean many different things depending on their context.We can regain some of this contextual information by examining part of speech tags.We run a parser over the question and take the preterminal nodes of the parser as the parts of speech for each word.These parts of speech alone wouldn’t help much,so we add as features word and part of speech pairs,thus helping to disambiguate words which have different senses depending on their part of speech.Another problem that part of speech tags fail to address is feature spar-sity(in fact,part of speech and word pairs suffer even further from this problem).Some words can reveal a lot of information,but do not show up enough times in the training set to allow our classifier to pick up on this.To address this problem,we use a stemmer to create a more generalized form of the word.For example,whether a verb is in present or past tense might not impact how it affects the question being asked.Another basic feature that we add is bigrams-pairs of words that occur sequentially.However,bigrams inherently suffer even worse from sparsity problems than individual words,so we use the stemmed form of the words to create the bigrams.3Onefinal basic feature that we experimented with is to take conjunctions of any other two basic features.Bigrams do not capture long-distance de-pendencies-two words that may affect each other’s meanings but are not adjacent in the sentence,so we attempted to do that with these conjunc-tions.The conjunctions did help to improve accuracy,but they resulted in unmanageably large feature set sizes,so we could not run them with the larger training corpuses or with the help of our full feature set.Parse SignaturesA more advanced feature that we added is something we call a“parse sig-nature”,though the term“sentence reduction”may be a more accurate descriptor.Our goal with these signatures was to create a representation of the entire question structure,instead of just small fragments of it.Obvi-ously,though,adding the full sentence would only help to later classify that exact sentence,so we needed a way of generalizing this.Another motivation for creating these signatures was to attempt to have some way to represent the rich grammatical information given to us by the question’s parse tree, but again without suffering from the sparsity issues that would come with using the entire tree as a feature.A parse signature can be thought of as a left-to-right readout of a parse tree.More formally,it is a set of nodes from a parse tree such that every leaf node has exactly one ancestor(or itself)in the set.The question itself would be one such signature,and the tree’s root node would be another (though neither of those would be particularly useful for our purposes).It should be clear that the number of parse signatures that can be generated from a question is enormous,so we need a way of limiting this number.Our solution is to parameterize the signature generation with the desired length of the readouts.Choosing to include all signatures of lengths1to5cap-tures the information that we desire while keeping the number of features at a reasonable level.We can further reduce the noise of this data by choosing to not traverse below certain nodes.For example,our parse trees always end in a“.”node which expands to a“?”,but since nearly every question ends with a?this means we would always be generating two almost-identical versions of each signature-one ending in.and one ending in?.To avoid this,we tell the signature generator to never traverse below a.or some other parts of speech such as CC whose exact words seem to have little impact.4WordNetThus far,all of our features have focused on syntactic considerations,but there is clearly a lot of information to be gained by looking at the semantic information in the questions.In order to accomplish this,we add WordNet information to our features.For every noun,verb,adjective,and adverb,we also add thefirst synonym that appears in its“synset”(a group of words that WordNet tells us share a meaning).While we could add every synonym, as long as we are careful to choose the same synonym each time something in a particular synset occurs there is no reason to add more than one. Perhaps more interesting than synonyms,though,are hypernyms.This is the“...is a kind of...”relationship(going from more specific to more gen-eral),e.g.“animal”for“dog”.While the idea of a more generalized form of a word is clearly a useful one,just adding a word’s direct hypernym probably wouldn’t be very helpful,since this may not be general enough(we might get that a dog is a canine and a cat is a feline instead offinding that they are both animals).Going multiple levels up also does not help,because two words can be at different levels in the tree,so while they may share some ancestor,it may be2levels up for one word and3for another and we would again not see the similarity.One solution would be to add all of a word’s hypernyms,but this would result in too much data.Our solution was instead to always take the hypernyms a certain distance from the“root”node.For example,many nouns have“entity”as their root node,but knowing that a noun is an entity isn’t much better than knowing that it’s a noun.We start at the root(such as entity)and work down several nodes in the direction of the target word until we get to ourfixed level and choose that as the hypernym to add.This way we are more likely to have related words share a hypernym,even if they are at different levels of the WordNet tree.We add such a hypernym to our feature set for every word that falls occurs below the target level.Finally,this hypernym informationfits nicely into our parse signature idea. The signature“What causes pneumonia”,for example,is probably too spe-cific,but having“What causes NN”may be too general.If we instead had “What causes HYP disease”(inserting the HYP prefix to distinguish the hypernym from the base word)as our signature this may be more ideal.We insert the hypernyms into the tree between a word’s part of speech and the word itself.For words that do not have hypernyms(because they are the wrong part of speech or occur too close to the root node in the WordNet hierarchy)but do have stemmed forms we add the stemmed form instead. Again,this has the potential to dramatically increase the number of parse signatures,so we tell our signature generator to never traverse below a node5with an HYP or STM prefix.Evaluation&ResultsDatasetIn order to compare our results with previous work,we used the same dataset as[Li02].The data can be obtained at the groups website(http://l2r. /~cogcomp/Data/QA/QC/).The training data consists of5500 labelled questions.The test data is500questions taken from the TREC 10set.In both the training and test data,there are a total of50different question classes.In addition,we count an answer as correct if the best classification label output by our classifier is the true label.Hierarchal Classification ResultsAfter experimenting with a variety of classifiers(SVM,MaxEnt,NB,Deci-sion Tree)for primary and secondary classification,we decided that a mix of a Maximum Entropy course classifier with a Naive Bayesfine classifier was the best combination.Observations indicated that it was better to mix classifiers than to have the same type of classifier as both the primary and secondary.As for why a Naive Bayes secondary classifier had the best results,we can offer some intuition.By the time we reached the secondary classification level,our training data’s feature sets were extremely sparse in the size of the feature space.Thus we would want a classifier that could take in a lot of features while still being very general.The exponential family of classifiers are known to perform better than some of the other classifiers we experi-mented with given sparse training data.Unfortunately our best hierarchal classifiers still under-performed compared to theflat maximum entropy classifier we train.Under the best conditions, we choose to split the training data with75%used for the course classifier and25%for thefine classifiers.Training Set10002000300040005500Flat Classifier67.674.277.880.282Hierarchal Classifier64.67175.877.881Examining our performance data,we realize that the main loss of perfor-mance is due to reduced amount of training data fed into the primary clas-sifier.If we rescale the training set axis of the hierarchal classifier to reflect the use of only75%of the training data for the course classifier,we see that6the hierarchal classifier does quite well in comparison to theflat classifier.Figure2:Performance comparison with rescaled hierarchal training set size.Feature Set ResultsOur biggest performance boosts came from using Porter’s word stemmer, provided by[Stanford NLP]and the additional semantic categories provided by WordNet.We were disappointed that the parse signatures yielded only a small boost(0.2%)with the full training set,but expect that further performance gains may be achieved by further adjusting the parameters used to generate the signatures,as well as working to further incorporate the WordNet data into the signatures.Our performance without WordNet (80%)exceeds the performance achieved by[Li02](78.8%)without adding related words,so we expect that it should be possible to exceed[Li02]if we experimented more with the semantic information.7Figure3:Performance increase with each added feature.Comparison with Li&RothOur system was able to perform better than[Li02]at the4000training ex-ample level.However,where our system came up short was in its ability to combine features conjunctively or disjunctively.[Li02]demonstrated use of a system for expressing complex conjunctions&disjunctions of features without experiencing the combinatorial explosion often associated with this field,by cleverly selecting only useful pairs.Implementing such a combina-torial system was beyond the scope of our time constraints,since it would require a great deal of hand-tweaking.Figure4:Performance Comparison8Summary&Future WorkWith regards toward applying a hierarchal classifier to the task of question classification,both our results and[Li02]indicate that using aflat classifier is better.However,given a sufficiently large training corpus,we still believe that a hierarchal classifier should do no worse than aflat classifier because the secondary classifier could at the very least just always output the same label as the course classifier.At the present levels of training data,however, the difference in the training corpus sizes is too great to overcome.During initial testing with smaller feature sets,we did experience some performance increases by combining features as pairs.With a greater amount of memory or a clever way of pruning the pairs that get added,we are con-fident that we could significantly improve the performance of our system. In addition,we believe that the WordNet information in particular has far more potential to be explored.[Li02]was able to achieve an8%performance increase by adding information about related words,compared to our2% gain.If we had additional time to improve our utilization of WordNet we could expect to get accuracy greater than that of[Li02].References[Li02]Xin Li,Dan Roth.Learning Question Classifiers.COLING.2002. [Harabagiu]Sanda M.Harabagiu,Marius A.Pasca,Steven J.Maiorano.”Experiments with Open-Domain Textual Question Answering”[McCallum]McCallum,Andrew Kachites.”MALLET:A Machine Learning for Language Toolkit.”2002.[Stanford NLP]JavaNLP.9。

Unit 1 How tall are you ?单元备课一、单元教学目标1、能力目标（1）能听懂会说认读本单元关于形容词比较级的单词。

（2）能听、说、读、写Let’s learn中黑体部分的内容和Read and write中要求的内容。

（3）能用目标语言完成本单元的任务型活动。

（4）能学说Chant一首和Song一首。

（5）了解ei/ai/oi，p/b,t/d的发音特点，并能试着拼读简单的规则词。

2、知识目标（1）能听、说、读、写单词及短语：taller stronger older younger shorter heaiver thinner longer bigger smaller句型：How tall are you ?I’m 164cm.You’re shorter than me. You’re 4cm taller than me .How heavy are you ?I’m 48 kg. I’m thinner than you , and shorter .（2）能够听、说、读Part A、Part B部分Let’s learn、Let’s talk中的单词和句子。

（3）了解Story time 、Good to know等部分的内容。

（4）了解字母ei/ai/oi，p/b, t/d在单词中的基本读音。

3、情感、策略、文化等有关目标（1）情感态度：培养和保持学生学习英语的积极态度和学习信心，引导学生养成良好的英语学习习惯。

（2）学习策略：加强学生英语学习策略的培养，为学生的终身学习奠定基础。

（3）文化目标：培养学生文化意识，帮助学生养成良好的日常行为习惯。

二、单元教学课时安排：六课时第一课时：Part A Let’s start / Let’s learn / Let’s play 第二课时：Part A Let’s try / Let’s talk / Let’s chant / Group work第三课时：Part A Let’s read & Part C Pronunciation / Let’s sing 第四课时：Part B Let’s learn / Let’s play & Part C Task time 第五课时：Part B Let’s try / Let’s talk / Pair work & Part C Good to know第六课时：Part B Let’s read & Part C Story time第一课时一、教学内容：Main scene Let’s start A let’s learn Let’s play二、教学目标与要求1、能够听、说、认读本课时的主要词组：taller stronger older younger shorter。

上课不懂就问的英语作文The Importance of Asking Questions in Class.In the academic world, the classroom is a vibrant ecosystem where ideas are exchanged, knowledge is gained, and understanding is deepened. Within this environment, the role of the student is paramount, and one of the mostcritical aspects of being an effective learner is the willingness to ask questions. Asking questions not only benefits the individual student but also contributes to the overall learning environment, making it a richer and more inclusive space.Firstly, asking questions helps to clarify conceptsthat may be unclear or confusing. In any subject, there are bound to be topics or ideas that are initially difficult to grasp. By voicing these doubts and uncertainties, students can gain clarity and a better understanding of the material. This is especially important in subjects that require adeep understanding of complex theories or principles. Byseeking clarification, students can ensure that they have a solid foundation upon which to build their knowledge.Secondly, asking questions fosters a culture of curiosity and inquiry. In the process of asking questions, students are encouraged to think critically and analytically, examining information from multiple angles and perspectives. This type of active learning is integral to the development of higher-order thinking skills such as critical thinking, problem-solving, and analytical reasoning. By encouraging students to question and seek answers, teachers are fostering a culture of intellectual curiosity that will serve students well throughout their academic and professional careers.Moreover, asking questions can lead to meaningful discussions and collaborations among classmates. When a student poses a question, it often sparks a conversation among peers, allowing everyone to contribute their own insights and perspectives. These discussions can be incredibly valuable, as they provide opportunities for students to learn from each other, gain new perspectives,and develop a deeper understanding of the topic. Additionally, this collaborative approach to learning can foster a sense of community and belonging within the classroom, creating a more supportive and inclusivelearning environment.Additionally, asking questions can help studentsidentify and address their own learning gaps. Byidentifying areas where they are struggling or lack understanding, students can take proactive steps to fill those gaps. This might involve seeking additional resources, such as tutorials, supplementary readings, or even one-on-one assistance from a teacher or peer. By taking ownershipof their learning process and actively seeking solutions to their problems, students are more likely to make progress and achieve success in their academic pursuits.Finally, asking questions is an essential part of the learning process because it helps students develop a growth mindset. By embracing their lack of understanding and seeking answers, students are demonstrating a willingnessto learn and grow. This mindset is crucial for academicsuccess, as it鼓励学生持续努力、面对挑战、并从失败中学习。