A MULTI-CLASS APPROACH FOR MODELLING OUT-OF-VOCABULARY WORDS
Issam Bazzi and James Glass
Spoken Language Systems Group
MIT Laboratory for Computer Science
Cambridge,Massachusetts02139,USA
{issam,glass}@https://www.doczj.com/doc/7d16771005.html,
ABSTRACT
In this paper we present a multi-class extension to our ap-proach for modelling out-of-vocabulary(OOV)words[1].Instead of augmenting the word search space with a single OOV model,we add several OOV models,one for each class of words.We present two approaches for designing the OOV word classes.The?rst ap-proach relies on using common part-of-speech tags.The second approach is a data-driven two-step clustering procedure,where the ?rst step uses agglomerative clustering to derive an initial class as-signment,while the second step uses iterative clustering to move words from one class to another in order to reduce the model per-plexity.We present experiments within the JUPITER weather in-formation domain.Results show that the multi-class model signif-icantly improves performance over using a single OOV class.For an OOV detection rate of70%,the false alarm rate is reduced from 5.3%for a single class to2.9%for an eight-class model.
1.INTRODUCTION
Current continuous speech recognition systems are designed to use a vocabulary with a?nite set of words.Given a?nite vocabulary, the presence of out-of-vocabulary(OOV)words is inevitable.In our JUPITER weather system,for example[3],the word error rate (WER)on data containing OOV words is nearly?ve times greater than on those containing only in-vocabulary(IV)words.While part of the WER increase is due to poor language modelling of out-of-domain queries,it is clear that OOV words cause recognition errors,and that an ability to identify OOV words would be bene-?cial.In this research we are exploring a tactic that incorporates an explicit OOV word model into the word-based recognizer[1], where an OOV word can be predicted by a word-level language model.The model is based on a set of subword units capable of generating new phone sequences,most importantly,those outside the vocabulary of the recognizer.In[2],we presented a method to automatically derive a set of variable-length units for the OOV model.We also presented dictionary-based methods for estimat-ing n-grams for use within the OOV network.Both of these ef-forts produced signi?cant improvements in OOV detection on our weather information task.
In this paper we present a multi-class extension to our ap-proach.Instead of augmenting the word search network with a single OOV model,we add several models,each corresponding to a class of OOV words.We explore two approaches for designing
This material is based upon work supported by a graduate fellowship from Microsoft Corporation,and by DARPA under contract N66001-99-1-8904monitored through NCCOSC.the OOV classes.The?rst approach relies on using part-of-speech (POS)tags.The second uses a two-step clustering procedure to
derive OOV word classes.Both approaches show signi?cant im-
provement in performance over the single class approach.
The remainder of the paper is organized as follows:we?rst
review the OOV recognition framework.Next we describe how
the framework can be extended to model multiple classes of OOV
words,and the two approaches for designing OOV word classes. Finally,we present and discuss the results of a series of experi-
ments in the JUPITER domain.
2.RECOGNITION FRAMEWORK
This section gives a short review of the recognition framework.
Details are presented in[1].To allow for OOV words the recog-nizer vocabulary is augmented with a generic word model W OOV.
This generic word model is considered in parallel with all other
words during recognition.The language model of the hybrid rec-
ognizer remains word-based,but now includes an entry for W OOV. Since W OOV is part of the vocabulary,the n-gram treats it like any
other word in the vocabulary.The FST representation of the rec-
ognizer search space in this OOV framework is given by:
R H=C?P?(L∪(L u?G u?T u))??G1(1) where C represents the mapping from context-dependent to context-
independent phonetic units,P represents the phonological rules,
and L is the word lexicon.The term L u?G u?T u represents the OOV model,where L u is the subword lexicon used to constrain
the OOV network to some subword units.G u is a subword n-
gram,and T u provides hard topological constraints on the model
such as imposing a minimum or maximum length requirement.G1 is the word level n-gram with the single OOV entry.The subscript 1indicates the single OOV class is modelled in the word n-gram. Note that this formulation assumes that all OOV words belong to the same class of words and hence a single model is used to handle all OOV words.
3.THE MULTI-CLASS APPROACH
One of the motivations for extending the framework to model mul-tiple classes of OOV words is to better model the contextual rela-tionship between the OOV word and its neighboring words.An-other motivation is to create multiple classes of words such that in each class,words that share the same or similar phone sequences are grouped together and used to train a class-speci?c subword n-gram language model.To extend our approach to model multiple
classes,we can construct multiple generic word models and cre-ate a search network that allows for either going through the IV branch or through any of several OOV branches each representing a class of OOV words.Suppose we have N classes of OOV words that we are interested in modelling.If we construct N subword search networks,one for each of the N classes,Equation 1can be extended as follows:
R H N =C ?P ?(L ∪(
N
i =1
L u i ?G u i ?T u i ))??G N
(2)
where in this formulation,R H N represents the collection of N +1search networks:the word-level IV search network and N sub-word search networks,each corresponding to a class of OOV words.The i th subword search network is represented by L u i ?G u i ?T u i .The word level n -gram G N includes the N different classes of OOV words.This n -gram can either use a class-speci?c language model probability or can use the same estimate for all classes.In our experiments,we explore various combinations of class-speci?c networks and word-level n -grams.Next,we describe two techniques for designing OOV classes.3.1.Part-Of-Speech OOV Classes
Class assignments in terms of POS classi?cations can be used to design the multi-class OOV model.Starting with a tagged dictio-nary,words can be broken down into multiple classes.For train-ing the word-level language model G N ,each OOV word in the training corpus is replaced with its POS tag,hence class-speci?c n -grams can be estimated.The subword-level language models,G u i can be trained on the phone sequences of all words belonging to this class of words.In designing OOV classes,we only use a small number of POS tags since many of the POS tags correspond to words that are not typical OOV words,such as function words.In order to resolve the problem of words belonging to multiple classes such as words that can be either verbs or nouns,we create intersection classes for POS tags that have signi?cant overlap.For example words that can be either nouns or verbs,such as the word book ,will belong to the class noun-verb .3.2.Automatically-Derived OOV Classes
The second approach relies on a two-step clustering procedure.Given a list of words,the goal is to break the list down into N lists,one for each of the N classes.The ?rst step is the initialization step.The goal of this ?rst step is to obtain a good initial class assignment for each of the words.The second step is an iterative clustering procedure intended to move words from one class to another in order to minimize the overall model perplexity.3.2.1.Step 1:Agglomerative Clustering
Agglomerative clustering is a bottom-up hierarchical clustering technique that starts by assigning each data point its own clus-ter.Based on some similarity measure,clusters are successively merged to form larger clusters.The process is repeated until the desired number of clusters is obtained [4].The procedure uses a similarity measure that is based on the phonetic similarity of words.Given the phone sequences of two words w i and w j ,the similarity measure d (w i ,w j )is the phone-pair edit distance be-tween the two words.This distance is the minimum number of
phone pair substitutions,deletions and insertions needed to match one word to another.This similarity measure groups words with similar phone pairs within the same cluster.Given the distance measure between individual words,we use an average similarity measure at the cluster level.At each step of the clustering proce-dure,we select for merging the pair of clusters X m and X n such that the average distance d avg (X m ,X n )is minimum:
d avg (X m ,X n )=
1c m c n
w i ∈X m w j ∈X n
d (w i ,w j )(3)
where c m and c n are the number of words in clusters X m and X n respectively.Because of the high computational requirements of this type of clustering,we run this step only on a randomly-chosen subset of the large dictionary of words.3.2.2.Step 2:Perplexity Clustering
Given the classes from Step 1,we create a class-speci?c phone bigram language model for each class.Step 2uses an iterative op-timization technique similar to K -means clustering [4].The basic idea is to move words from one class to another if such a move im-proves the value of some criterion function.For the OOV model,the criterion function we use is the word’s phone sequence perplex-ity against the various n -grams.The procedure is repeated until the change in average perplexity is smaller than some threshold or no more words change classes.
A variation on the two-step automatic approach is to use the POS tags for initialization.Instead of performing the agglomera-tive clustering to initialize the word classes,we can start with the assignments from the POS tags and then perform the perplexity clustering described above.There are two advantages for such an approach.First,the initial assignment,being based on POS tags,could provide for a better starting point for the perplexity cluster-ing.The second advantage is eliminating the computational over-head of agglomerative clustering required in Step 1.3.3.Related Work
The only work we are aware of on the use of multi-class mod-els for OOV recognition is the approach presented in [5].In this work,Gallwitz et al.constructed ?ve word categories that in-cluded cities,regions,and surnames.In addition,they de?ned a category for rare words that are not in the ?rst ?ve,as well as one for garbage words such as word fragments.Unlike our approach,they used very simple acoustic models for each of the OOV cat-egories:a ?at model that consisted of a ?xed number of HMM states with identical probability density functions.
4.EXPERIMENTS AND RESULTS
All of the experiments for this work are within the JUPITER weather information domain [3].A set of context-dependent diphone acous-tic models was used,whose feature representation was based on the ?rst 14MFCCs averaged over 8regions near hypothesized phonetic boundaries.Diphones were modeled using mixtures of diagonal Gaussians with a maximum of 50Gaussians per model.The word lexicon consisted of a total of 2,009words,many of which have multiple pronunciations.The training set consisting of 88,755utterances was used to train both the acoustic and the language models.The test set consisted of 2,029utterances,314
of which contained OOV words (most of the OOV utterances had only one OOV word).For the baseline OOV model,we used the dictionary con?guration [2]where the subword lexicon is simply the phoneme set and the subword bigram is trained on phoneme se-quences of all words in the LDC PRONLEX dictionary.PRONLEX
Fig.1.ROC curves for the three OOV models discussed.The behavior of the OOV model was measured by observing the OOV detection and false alarm rates on the test set as the cost of entering the OOV model C OOV was varied,thereby obtaining the receiver operating characteristic (ROC)over a range of OOV detection and false alarm rates.Figure 1shows the ROC curves for the three different models:a baseline single-class model,the POS eight-class model,and the automatically derived model.We will discuss each of the curves in the following sections.In order to quantify the ROC behavior,a ?gure of merit (FOM)was com-puted which measured the area under the ROC curve over the 0%to 10%false alarm rates.For our work we are most interested in the ROC region with low false alarm rates,since this produces a small degradation in recognition performance on IV data.4.1.The POS Model
In order to get the POS tags of words in PRONLEX ,we used the related dictionary COMLEX which contains a total of 22POS tags.The majority of the words in PRONLEX belong to one of ?ve main classes:nouns,verbs,adjectives,adverbs,and names.However,a signi?cant overlap exists between the noun and verb classes,as well as between the adjective and verb classes.For our POS OOV model,we chose a model with eight classes:the ?ve classes above,the two intersection classes noun-verb and adjective-verb and a backup class that covers OOV words that either are untagged or do not belong to any of the other seven classes.To build the eight OOV models,we use the phone-level lexicon for all eight classes.For each class we train its phone bigram using phone sequences of words that belong to the class.
Table 1shows the detection results for the POS multi-class model.The multi-class extension can be done in one of three ways:(1)only at the language model level by having multiple OOV n -
grams,(2)only at the OOV model level by having multiple OOV networks,one for each class,(3)both at the language model and the OOV model level.The table shows the three possible cases as well as the baseline.The ?rst result in the table is the baseline sys-tem with an FOM of 0.64.The second case involve using the eight classes for language modelling,but still using the same OOV for all classes,https://www.doczj.com/doc/7d16771005.html,ing G 8and one OOV network.The for this condition is 0.65,only slightly better than the base-The third case involves creating multiple OOV networks but the same language model n -grams for all classes.The FOM this case is 0.68,a signi?cant improvement over the baseline class model.Adding the language model classes to this con-does not improve performance.This is the fourth case the table,where the FOM stays at 0.68.
Condition
G 1n -gram G 8n -gram 1OOV network 0.640.658OOV networks
0.68
0.68
1.FOM detection results on different con?gurations of the model.
The FOM results show that the improvement from the POS model is due mainly to using multiple OOV networks not multiple word n -gram OOV classes.This ?nding could be speci?c to the JUPITER domain since it is a fairly simple recog-nition task where most of the OOV words are either names or weather terms (nouns).Hence the bene?t from the OOV neigh-boring context is limited and does not help improve https://www.doczj.com/doc/7d16771005.html,rge vocabulary unconstrained domains may bene?t more from multiple OOV n -gram classes.An important aspect of the POS model is its ability to identify the type or POS tag of the OOV word.A manual examination of the correctly detected OOV words showed that 81%of the detected OOV words are recognized with the correct POS tag.
The impact of the multi-class model on the word error rate (WER)is similar to that of a single class model.For all reported con?gurations,the relationship between overall OOV false alarm rate and WER on IV test data is approximately linear.The WER increases slowly from the baseline WER of 10.9%at 0%false alarm rate to under 11.5%at 10%false alarm rate.4.2.The Automatically-Derived Model
For Step 1in this approach,agglomerative clustering was done on 1,000randomly chosen words from PRONLEX to produce 8ini-tial classes.Figure 2shows the change in the weighted average perplexity of the multi-class model as a function of the iteration number for two initial conditions:the clusters obtained from ag-glomerative clustering in Step 1,and the clusters based on the POS tags.The clustering was iterated until the change in perplexity was less than 0.05.At that point,very few words moved from one class to another.Figure 2shows that the multi-class model perplexity improved from 12.5to 10.2for the POS initialization and from 13.2to 10.3for the agglomerative clustering initialization.
In these experiments,we use a word n -gram with a single OOV class.There are two reasons for using multiple classes.The ?rst is the fact that we did not get much gain from multiple n -gram OOV classes with the POS model.The second reason is that while the words in these automatically derived classes may be pho-
Fig.2.Weighted average perplexity of the multi-class model in
terms of the clustering iteration number.
OOV Model Number of Classes FOM
Baseline10.64
POS80.68
PP Clus(AggClus Init)80.71
PP Clus(POS Init)80.72 Table2.FOM detection results for various multi-class models. netically similar because of the way they are derived,they do not
necessarily share similar contexts.
Detection results are summarized in Table2and Figure1.As shown,the automatic OOV model with the POS initialization out-performs the single class model as well the POS model.Note that using POS tags for initialization is only slightly better than us-ing agglomerative clustering.Over the baseline system using one class,the multi-class model improves the FOM by over11%(from 0.64to0.72).For example,at an OOV detection rate of70%,the false alarm rate is reduced from5.3%for a single class to2.9%for an eight-class model.The FOM of0.72for the multi-class model with POS initialization is also better than the0.70FOM result we reported in[2]with the mutual information approach where a sin-gle OOV model was used but with multi-phone sublexical units.
4.2.1.Varying the Number of Classes
Figure3shows the performance of the automatic multi-class model as a function of the number of classes N.We compared using1, 2,4,8,16,and32classes.Figure3shows that most of the gain is obtained in going from one to two classes where the FOM jumps from0.64to over0.69.The bene?t from using more classes dimin-ishes as N increases.Going from8to16and then to32classes gives only a slight improvement in the FOM.This behavior could be speci?c to JUPITER,and other unconstrained domains may ben-e?t more from a larger number of classes.Fig.3.The FOM performance of the automatic model as a func-tion of the number of classes.
5.CONCLUSIONS AND FUTURE WORK
In this paper we presented a multi-class extension to our approach for modelling OOV words.Instead of augmenting the word search network with a single OOV model,we added several OOV models, each corresponding to a class of OOV words.We presented two approaches for designing the OOV classes.The?rst approach re-lies on using common POS tags to design the OOV classes,while the second approach uses a two-step clustering procedure.The ex-perimental results showed signi?cant improvement over using the single class model reported earlier[1,2].
In future work we plan to explore combining the multi-class approach with using multi-phone units within each OOV network. We also plan to explore using our approach for detecting out-of-domain utterances.Finally,we plan to investigate using a second-stage search with a large off-line dictionary to determine the iden-tity of the OOV words after they are detected.
6.REFERENCES
[1]I.Bazzi and J.Glass,“Modelling out-of-vocabulary words
for robust speech recognition,”in Proc.Intl.Conf.on Spoken Language Processing,Beijing,Oct.2000,pp.401–404. [2]I.Bazzi and J.Glass,“Learning units for domain-
independent out-of-vocabulary word modelling,”in Proc.
European Conf.on Speech Communication and Technology, Aalborg,Sept.2001,pp.61–64.
[3]V.Zue,et al.,“J UPITER:A telephone-based conversational
interface for weather information,”IEEE Trans.on Speech and Audio Processing,88(1),2000.
[4]R.Duda,and P.Hart,“Pattern Classi?cation and Scene
Analysis,”John Wiley&Sons New York,1973.
[5] F.Gallwitz,E.Noeth,and H.Niemann(1996).“A cat-
egory based approach for recognition of out-of-vocabulary words.”in Proc.Intl.Conf.on Spoken Language Processing, Philadelphia,Oct.1996,pp.228–231.
小说鉴赏方法: 小说的鉴赏必须要注意三点:(1)要了解小说所反映的社会背景,作者写作、 发表时的社会背景及作者的写作意图,在此基础上,领会小说所揭示的主题思想; (2)要认真分析人物形象,通过对有关描写方法的研究,剖析人物形象,理 解典型环境中的典型性格,从而深入领会小说的思想与艺术; (3)要研究小说的故事情节、线索、结构就是怎样为塑造形象、表达主题服 务 的。 第一、分析典型人物 分析人物可从以下几个方面入手 ①注意人物外貌特征的描写。如祥林嫂的外貌。 ②注意人物语言的个性化。如鲁四老爷的个性化语言,就反映出她伪善、冷酷、自私的性格特征。 ③注意人物的行动描写。如药里刽子手康大叔卖人血馒头的一系列动作,就刻画出她凶残、贪婪的丑恶形象。 ④注意人物的心理描写。如项链中对玛蒂尔德大段心理描写,就揭示了她小资产阶级的虚荣心。 ⑤刻画人物形象的方法。 第二、分析故事情节。
情节就是叙事性文学作品中所描写的事件及其发展变化的过程,它由一系列具 有矛盾冲突的具体事件组成。 分析故事情节可从以下几个方面入手: ①理清结构。小说的情节大体由开端、发展、高潮、结局几部分构成,可以据此划分层次。 ②寻找线索。线索就是情节发展中的脉胳,有时还就是矛盾冲突的焦点。通过寻找线索,有利于掌握情节,有时也有利于抓住小说的矛盾冲突,便于把握主题。 ③抓住场面。情节由场面组成。有的小说情节复杂,有的小说情节不连贯, 在这种情况下,抓住场面分析就可以不受情节、结构的限制,直接掌握作品的中心事件与主要矛盾。 第三、分析典型环境。 ①典型环境就是指文学作品中形成人物性格并驱使人物行动的生活场所,就 是一 定历史时代、社会现实及其发展趋势的具体体现。 ②典型环境就是指文艺作品中典型人物所生活的形成其性格并驱使其行动 的 特定环境。 典型环境主要就是社会环境,也包括自然环境。 分析典型环境从以下方面入手:
A Rose for Emily 的评析(2010-06-21 23:49:34)转载▼ 标签：文化 威廉.福克纳和他的《献给爱米丽的玫瑰》 摘要：福克纳把南方的历史和现实社会作为自己创作源泉而成为美国南方文学的代表。《献给爱米丽的玫瑰》通过爱米丽的爱情悲剧揭示了新旧秩序的斗争及没落贵族阶级的守旧心态，福克纳运用神秘、暗语、象征、时序颠倒等写作手法来揭示这一主题。 关键词：威廉·福克纳；献给爱米丽的玫瑰；南方小说 一、威廉·福克纳的南方情结 威廉·福克纳(William Faulkner，1897-1962)是美国文学史上久负盛名的作家之一，生于密西西比州一个在内战中失去财富和地位的没落的南方种植园家庭。福克纳的大多数作品都以美国南方为背景，强调南方主题和南方意识。在他19部长篇小说和75篇短篇小说中，绝大多数小说的故事都发生在他虚构的美国的约克纳帕塔法县(Yoknapatawpha county）和杰弗生镇。这些作品所展示的生活画卷 和人物形象构成了福克纳笔下的“约克纳帕塔法世系”。 “约克纳帕塔法世系”是以该县家族的兴衰、变迁为主题，故事所跨越的时间上起自印地安人与早期殖民者交往的岁月，止于第二次世界大战后，长约二百年。他的世系小说依南方家系人物的生活而展开，以南方浓郁的泥土气息伴随着因工业文明而带来的焦虑、惶惑、无奈，把一百多年来即从1800年到第二次世界大战之后社会发展过程中，南方人所独有的情感和心态通过独特的艺术方式展示出来，可谓一部“南方生活的史诗”。在这部史诗的字里行间，留下了作家的血与泪之痕：割不断爱恋南方古老精神的一片深情，可又抵御不了现代文明进程的必然性。正如福克纳所说：“我爱南方，也憎恨它。这里有些东西我本不喜欢。但是我生在这里，这是我的家。因此，我愿意继续维护它，即便是怀着憎恨。”这种矛盾恰好构成了福克纳情感意识及其小说世界的无穷魅力。结果，约克纳帕塔法县成了旧南方的象征，而福克纳也借此成功地表现了整个南方社会的历史和意识。 二、《献给爱米丽的玫瑰》暗示南方的腐朽没落 福克纳素以长篇小说著称于世，但其短篇小说，无论艺术构思、意境创造、人物塑造抑或语言风格、结构艺术等，均可与其长篇小说互论短长，而其最著名的短篇之一《献给爱米丽的玫瑰》 A rose for Emily则对理解、研究福克纳的主要作品，即“约克纳帕塔法世系”具有十分重要的意义。在这篇小说中，作者以凝练的笔触、独特的结构成功地塑造了爱米丽·格里尔森Emily Grieson这个艺术典型。 《献给爱米丽的玫瑰》是福克纳1930年4月发表的被誉为最负盛名的短篇小说。故事发生在约克纳帕塔法县的杰弗生镇，小说分为五小部分，在这五个写作空间里，运用回忆来描写爱米丽·格里尔森（Emily Grieson)神秘的一生，讲述了一位被剥夺了与他人建立正常人际关系的妇女如何逃避现实以至于精神失常的故事，表现了新旧南方价值观念之间的冲突。爱米丽的个性冲突、所做所为源于她南方古老而辉煌的家庭背景，福克纳用神秘、暗语、象征等写作方法来揭示爱米丽是南方腐朽传统的象征。故事的叙述采用了时序颠倒的手法，这一手法不仅使小说结构奇突，情节跳跃，更为重要的是其具有深化主题思想的艺术功效。作者用这种手法打破时空界限，把过去和现在直接放在一起，在它们之间形成鲜明的对照，从而使读者深深地感到时代的变迁和传统价值观念的沦丧。 小说一开始就告诉读者爱米丽死了，全镇的人都去送丧。她的死象征着南方古老传统、价值观念、生活方式的彻底灭亡和消失。为展示新旧双方的矛盾和以爱米丽为代表的贵族阶级虽大势已去却拒不接受社会变革的心态，作者选取了一个极为典型的事件——纳税事件。
(一) 假定你是红星中学初三学生李华。你的美国朋友Jim在给你的邮件中提到他对中国新近出现的一种共享单车“mobike”很感兴趣，并请你做个简要介绍。请你给Jim回信，内容包括： 1. 这种单车的使用方法(如：APP查看车辆、扫码开锁等); 2. 这种单车的优势; 3. 你对这种单车的看法。 注意：1. 词数不少于80; 2. 开头和结尾已给出，不计入总词数。 提示词：智能手机smartphone, 二维码the QR code 参考范文 Dear Jim, I’m writing to tell you more about the new form of sharing bike mobike mentioned in your latest letter. It’s very convenient to use if you have a smartphone. What you do is find a nearest mobikethrough the APP, scan the QR code on the bike, and enjoy your trip. Compared to other forms of sharing bike, the greatest advantage of mobike is that you can easily find one and never worry about where to park it. It is becoming a new trend as a means of transportation, which relieves the traffic pressure and does good to the environment as well. Hope to ride a mobike with you in China. Yours, Li Hua (二) 最近很多大城市都投放了共享单车(shared bikes)，比如摩拜单车(Mobike)、Ofo共享单车等。由于它们方便停放，骑车也能起到锻炼身体的作用，作为代步工具很受大家欢迎。但是，各地也出现了很多毁车现象，比如刮掉车上的二维码(QR code)、上私锁等。 你对这种现象怎么看?你对共享单车公司有什么建议吗?写一篇符合逻辑的英语短文，80词左右。 参考词汇：bike-sharing companies 共享单车公司，Mobike 和Ofo 是两家共享单车公司，convenience 方便，register登记 参考范文 The shared bikes like Mobike and Ofo bring great convenience to people. You needn’t lock them by simply using your smart phone. They can take you where the subway and bus don’t go. And they can be left anywhere in public for the next user. However, bad things happen. Some people damage the QR code on the bike, or use their own lock, which causes trouble to other users. In my opinion, it’s difficult to turn these people’s ideas in a short time. Therefore, bike-sharing companies like Mobike and Ofo need to do something. For example, those who damage the bike should pay for their actions. Also, because people use their real name toregister as a user, it’s a good way to connect to one’s personal credit. In the end, what I want to say is to take good care of public services. (三) 共享单车(bicycle sharing)已成为时下最热的话题之一，请你就这一话题写一篇短文。内容须包括三方面：1. 共享单车蓬勃发展，成为社会热潮;2. 共享单车带来便利，但也存在问题;3. 我对解决问题的建议。 参考范文 Bicycle Sharing With the development of technology, bicycle sharing comes into people's lives. It becomes more and more popular and much news reported it. At the same time, we should see that there are some problems caused by bicycle sharing. On one side, bicycle sharing makes it very convenient of people traveling. You can find a bicycle anywhere at any time when you want to go out for a cycling, and the price of one trip is very low. It can save time for people. On the other side, its management is not perfect. Even kids can open the lock and ride the bicycle, there is no doubt that such behavior is very dangerous.
题王处士郊居 唐代 半依云渚半依山，爱此令人不欲还。负郭田园九八顷， 向阳茅屋两三间。寒松纵老风标在，野鹤虽饥饮啄闲。 一卧江村来早晚，著书盈帙鬓毛斑。 这是一首兰于：的诗 翻译 赏析 个人作品 主题 题材集中是白居易讽喻诗的艺术特色之一。他一般只选择最典型的一件事，突出一个主题，“一吟悲一事”，主题非常明确。为使主题更明确传达给读者，或诗题下加小序点明主题，或“卒章显其志”突出主题。其次，白诗的艺术特色还表现在刻画人物上，他能抓住人物的特征，用白描方法勾勒出鲜明生动的人物形象。但白诗的诗意并不浅显，他常以浅白之句寄托讽喻之意，取得怵目惊心的艺术效果。《轻肥》一诗描写了内臣、大夫、将军们赴会的气概和席上酒食的丰盛，结句却写道：“是岁江南旱，衢州人食人”，这是一幅多么惨烈的情景。
闲适诗和讽喻诗是白居易特别看重的两类诗作，二者都具有尚实、尚俗、务尽的特点，但在内容和情调上却很不相同。讽喻诗志在“兼济”，与社会政治紧相兰联，多写得意激气烈；闲适诗则意在“独善”，“知足保和，吟玩性情” ，仍而 表现出淡泊平和、闲逸悠然的情调。 白居易的闲适诗在后代有很大影响，其浅切平易的语言风格、淡泊悠闲的意绪情调，都曾屡屡为人称道，但相比之下，这些诗中所表现的那种退避政治、知足保和的“闲适”思想，以及归趋佛老、效法陶渊明的生活态度，因与后世文人的心理较为吺吅，所以影响更为深远。如白居易有“相争两蜗角，所得一牛毛”、“蜗 牛角上争何事，石火光中寄此身”的诗句，而“后之使蜗角事悉稽之”。即以宋人所取名号论，“醉翁、迂叟、东坡之名，皆出于白乐天诗云”。宋人周必大指出：“本朝苏文忠公不轻许可，独敬爱乐天，屡形诗篇。盖其文章皆主辞达，而忠厚好施，刚直尽言，与人有情，于物无着，大略相似。谪居黄州，始号东坡，其原必起于乐天忠州之作也。”凡此种种，都展示出白居易及其诗的影响轨迹。 诗歌理论 白居易的思想，综吅儒、佛、道三家，以儒家思想为主导。孟子说的“达则兼济天下，穷则独善其身”是他终生遵循的信条。其“兼济”之志，以儒家仁政为主，也包括黄老之说、管萧之术和申韩之法；其“独善”之心，则吸取了老庄的知足、齐物、逍遥观念和佛家的“解脱”思想。二者大致以白氏被贬江州司马为界。白居易不仅留下近三千首诗，还提出一整奖诗歌理论。他把诗比作果树，提出“根情、苗言、华声、实义”的观点，他认为“情”是诗歌的根本条件，“感人心者莫先乎情”，而情感的产生又是有感于事而系于时政。因此，诗歌创作不能离开现实，必须取材于现实生活中的各种事件，反映一个时代的社会政治状况。他继承了《诗经》以来的比兴美刺传统，重视诗歌的现实内容和社会作用。强调诗歌揭露、批评政治弊端的功能。他在诗歌表现方法上提出一系列原则。《与元九书》中他提出了著名的“文章吅为时而著，歌诗吅为事而作”的现实主义创作原则。
小说的基本常识 （一）小说的基本常识 1、小说是通过人物、情节和环境的具体描写来反映现实生活的一种文学体裁。人物、情节、环境是小说的三要素。 2、小说刻画人物形象的方法包括：语言描写、肖像描写、行动描写、心理描写和细节描写。 3、小说中的环境包括自然环境和社会环境。 4、小说的叙述方式包括顺叙、倒叙、插叙等。 (二)小说的特点 1、完整的故事情节 2、鲜明的人物形象 3、典型的环境 4、深刻的主题 5、精巧的构思 (三)高考命题要点 1.把握故事情节 2.揣摩人物形象 3.注意环境描写 4.概括主题内容 5.品味语言特色 6.分析写作技巧 一、小说情节的把握 把握好故事情节，是读懂小说的关键，是欣赏小说艺术特点的基础，也是整体感知文章的起点。命题者在为小说命题时，也必定以此为出发点，先从整体上设置理解文章内容的试题。 （一）高考中有关小说情节的命题指向 1、用一句话或简明的语句概括故事情节，或文中共写了哪几件事，请依次加以概括； 2、对某一情节的特点和作用进行分析； 3、对情节的高潮部分或结尾部分作用的理解； 4、小说在叙述故事情节过程中顺叙、倒叙、插叙等方法的使用； 5、哪一个情节最吸引你； 6、情节的合理性探究。（二）小说中情节的作用 1、交代人物活动的环境； 2、设置悬念，引起读者阅读的兴趣； 3、为后面的情节发展作铺垫或埋下伏笔； 4、照应前文； 5、线索或推动情节发展； 6、刻画人物性格； 7、表现主旨或深化主题。（三）情节安排的方式及作用 1、就全文来说有一波三折式。作用是引人入胜，扣人心弦，增强故事的戏剧性、可读性。 2、就开头结尾来说有首尾呼应式。作用是使结构紧密、完整。 3、就开头来说有倒叙式（把结局放到开头来写），如《祝福》，先写祥林嫂的死，然后再写祥林嫂是怎样一步步被封建礼教逼向死亡之地的。起到制造悬念的作用。 4、就结尾来说有戛然而止，留下空白式。此外，还有出人意料式、悲剧、喜剧式等。 5、贯穿情节的线索。可作线索的小说因素有：事、物、人、情、时间、空间，如《药》中的“人血馒头”、《故乡》中的“我”等。 （四）分析小说情节的入手方式： 1、抓住场面； 2、寻找线索； 3、理清小说的结构。 （五）分析小说情节时的注意事项 1、情节的发展变化是矛盾冲突发展的体现，分析小说的情节时必须抓住主要的矛盾冲突。 2、分析情节不是鉴赏小说的目的，而是手段，是为理解人物性格、把握小说主题服务的。所以，在分析情节的过程中，要随时注意体会它对人物性格的形成及对揭示小说主题的作用。 （六）情节安排的顺序及作用 1、顺叙：按时间（空间）顺序来写，情节发展脉络分明，层次清晰。
[标签:标题] 篇一：关于汽车的英语作文 好的 Nowadays, with the rapid improvement of people’s living standards, cars have become an indispensable part of people's lives,so that more and more people have a car of their own, especially in cities. It brings some benefits for us but also causes many problems at the same time. For one thing,there’s no doubt that cars provide much convenience for people to go where they want to quickly and easily. Especially on weekday,driving a car can save a lot of time for us to go to work.When some places are too far away from our home, driving our own car is also convenient, we can go wherever we want. However,for another, too many cars will lead to the pressure of public transport, a series of problems will appear.First of all,it will bring about more air pollution,a large amount of polluted air given off by cars do great harm to our health.What’s more, as the existing roads are not so wide for the increasing number of cars,undoubtedly,traffic jams will become more and more serious. Last but not least, cars also place burden on the public facilities in providing more parking lots. As far as I am concerned,everything has its advantages and disadvantages. It’s high time that effective action must be token to limit the ever growing number of cars, the government should take measures to control the air pollution from the cars. Some roads should be widened and more new roads should be constructed. Only in this way，will people benefit from the popularity of cars. 坏的 Nowadays, with the rapid improvement of people's living standards, cars have become an indispensable part of people's lives,so that more and more people have a car of their own, especially in cities.It brings some benefit for us but also causes many problems at the same time. For one thing,it's no doubt that that cars provide much convenience for people to go where they want to quickly and easily. Especially on weekday,driving a car can save a lot of time for us to go to work.When some places are too far away from our home, driving our own car is also convenient, we can go wherever we want. However,for another, too many cars will lead to the pressure of public transport, a series of problems will appear.First of all,it will bring about more air pollution,a large amount of polluted air given off by cars do great harm to our health .What's more, as the existing roads are not so wide for the increasing number of cars,undoubtedly,traffic jams will become more and more serious. Last but not least, cars also place burden on the public facilities in providing more parking lots. As far as I am concerned,everything has its advantages and disadvantages. It's high time that effective action must be token to limit the ever growing number of cars, the government should take measures to control the air pollution from the cars. Some roads should be widened and more new roads should be constructed. Only in this way,will people benefit from the popularity of cars. 篇二：雅思作文高分范文：私家车的利与弊 智课网IELTS备考资料 雅思作文高分范文：私家车的利与弊
高考复习小说鉴赏有妙招课堂实录完美版 -CAL-FENGHAI-(2020YEAR-YICAI)_JINGBIAN
高考复习小说鉴赏有妙招课堂实录 导入新课： 我们知道，多年来，散文在高考语文文学作品阅读中占据老大哥的位置，但是随着高考命题逐渐呈现出多元化趋势，小说也开始来分得一杯羹。 生：没有。 师：但是山东省并非按兵不动，它也有动作。请看大屏幕： 08年的山东考试说明:“了解诗歌、散文、小说、戏剧等文学体裁的基本特征及主要手法.” 09年的山东考试说明:“了解小说、散文、诗歌、戏剧等文学体裁的基本特征及主要手法”。 师：大家看出门道了吗？ 生：把小说放到了最前面。 师：这说明什么？ 生：可能要考小说。 师：英雄所见略同。我也有同感。再加上09年山东省并没有考小说，这样看来明年似乎是非考不行了。但是我们不怕！我们今天的历史使命就是来解决这个问题的！今天我要教给大家两个鉴赏小说的妙招。等你掌握了这两个妙招，你肯定会这样想：真怕他明年不考小说！ 那么现在我要考考大家，小说三要素是什么？ 生：人物、情节、环境。 师：我这两个妙招就是针对三要素设置的，请看教学目标： 1、明确小说环境描写的作用。 2、学会抓住细节分析人物形象人物形象。 环境是人物活动的广阔的大舞台。我们先看环境。 请大家看学案第一部分，为大家设置了三段环境描写，先请大家齐读前两段祝福的开头和结尾，要求声音洪亮，读出感情。 师生共同研讨祝福的开头和结尾： 《祝福》开头： 旧历的年底毕竟最像年底，村镇上不必说，就在天空中也显出将到新年的气象来。灰白色的沉重的晚云中间时时发出闪光，接着一声钝响，是送灶的爆竹；近处燃放的可就更强烈了，震耳的大音还没有息，空气里已经散满了幽微的火药
“A Rose for Emily” is divided into five sections. The first section opens with a description of the Grierson house in Jefferson. The narrator mentions that over the past 100 years, Miss Emily Grierson’s home has fall into disrepair and become “an eyesore among eyesores.” The first sentence of the story sets the tone of how the citizens of Jefferson felt about Emily: “When Miss Emily Grierson died, our whole town went to the funeral: the men through a sort of respectful affection for a fallen monument, the women mostly out of curiosity to see the inside of her house, which no one save an old manservant – a combined gardener and cook – had seen in at least ten years.” It is known around town that Emily Grierson has not had guests in her home for the past decade, except her black servant who runs errands for her to and from the market. When a new city council takes over, however, they begin to tax her once again. She refuses to pay the taxes and appear before the sheriff, so the city authorities invite themselves into her house. When confronted on her tax evasion, Emily reminds them that she doesn't have to pay taxes in Jefferson and to speak to Colonel Sartoris, although he had died 10 years before. In section two, the narrator explains that the Griersons had always been a very proud Southern family. Mr. Grierson, Emily’s father, believes no man is suitable for his daughter and doesn't allow her to date. Emily is largely dependent upon her father, and is left foundering when he dies. After Mr. Grierson's death, Emily does not allow the authorities to remove his body for three days, claiming he is still alive. She breaks down and allows authorities to take the body away for a quick burial. Section three introduces Emily’s beau, Homer Barron, a foreman from the north. Homer comes to Jefferson with a crew of men to build sidewalks outside the Grierson home. After Emily and Homer are seen driving through town several times, Emily visits a druggist. There, she asks to purchase arsenic. The druggist asks what the arsenic is for since it was required of him to ask by law. Emily does not respond and coldly stares him down until he looks away and gives her the arsenic. When Emily opens the package, underneath the skull and bones sign is written, "For Rats." Citizens of Jefferson believe that Miss Emily is going to commit suicide since Homer has not yet proposed in the beginning of section four. The townspeople contact and invite Emily's two cousins to comfort her. Shortly after their arrival, Homer leaves and then returns after the cousins leave Jefferson. After staying in Jefferson for one night, Homer is never seen again. After Homer’s disappearance, Emily begins to age, gain weight, and is rarely seen outside of her home. Soon, Miss Emily passes away.
汽车的重要性《英语作文》 The automobile has become one of the most important means/ways of transportation in the world since it was invented. The automobile has completely changed the lifestyles of almost all the people in the world. In the past, animals like horses and camels were used for traveling and transporting goods. Automobiles are more comfortable and faster. Automobiles have also made it possible for us to transport large quantities of goods and people at the same time. Besides, the invention of the automobile has provided jobs for millions of people all over the world. 翻译： 汽车已经成为世界上最重要的交通工具之一，因为它是发明的。汽车已经完全改变了世界上几乎所有的人的生活方式。 在过去，像马和骆驼的动物被用来运送货物。汽车更舒适，更快速。汽车也使我们能够在同一时间运送大量货物和人。 此外，汽车的发明为全世界上百万的人提供了工作。
小说鉴赏方法： 小说的鉴赏必须要注意三点：（1)要了解小说所反映的社会背景，作者写作、 发表时的社会背景及作者的写作意图，在此基础上，领会小说所揭示的主题思想； （2)要认真分析人物形象，通过对有关描写方法的研究，剖析人物形象，理解典型环境中的典型性格，从而深入领会小说的思想和艺术； （3)要研究小说的故事情节、线索、结构是怎样为塑造形象、表达主题服务的。 第一、分析典型人物 分析人物可从以下几个方面入手 ①注意人物外貌特征的描写。如祥林嫂的外貌。 ②注意人物语言的个性化。如鲁四老爷的个性化语言，就反映出他伪善、冷酷、自私的性格特征。 ③注意人物的行动描写。如药里刽子手康大叔卖人血馒头的一系列动作，就刻画出他凶残、贪婪的丑恶形象。 ④注意人物的心理描写。如项链中对玛蒂尔德大段心理描写，就揭示了她小资产阶级的虚荣心。 ⑤刻画人物形象的方法。 第二、分析故事情节。
情节是叙事性文学作品中所描写的事件及其发展变化的过程，它由一系列具 有矛盾冲突的具体事件组成。 分析故事情节可从以下几个方面入手： ①理清结构。小说的情节大体由开端、发展、高潮、结局几部分构成，可以据此划分层次。 ②寻找线索。线索是情节发展中的脉胳，有时还是矛盾冲突的焦点。通过寻找线索，有利于掌握情节，有时也有利于抓住小说的矛盾冲突，便于把握主题。 ③抓住场面。情节由场面组成。有的小说情节复杂，有的小说情节不连贯， 在这种情况下，抓住场面分析就可以不受情节、结构的限制，直接掌握作品的中心事件和主要矛盾。 第三、分析典型环境。 ①典型环境是指文学作品中形成人物性格并驱使人物行动的生活场所，是一 定历史时代、社会现实及其发展趋势的具体体现。 ②典型环境是指文艺作品中典型人物所生活的形成其性格并驱使其行动的 特定环境。 典型环境主要是社会环境，也包括自然环境。 分析典型环境从以下方面入手： 第四、分析小说的社会作用 小说的社会作用分为认识作用、教育作用、美感作用。
汽车英文演讲稿 篇一：汽车英语演讲稿 Good morning everyone ,today, the topic of my lecture（演讲） is “Do you love automobiles（'tmbilz）”. before I start my speech ,I want to ask a question,did there anybody present had seen the movie”the Fast and the Furious['fjrs]”.There are many cars of different styles in the movie,for example ,when you see the movie,you can find roadsters（跑车） just like Porsche（保时捷） GT3,sports utility (通用的）vehicles （车）SUV such as Volkswagen ['f:lks,va:gn]Touareg ['twɑ:reɡ](大众途锐）,even armored cars ,all of them are so powerful and beautiful. When I was a child,I dreamed I can have a car like FORD GT 40,this racing bike（跑车） is amazing and fantastic,but as time goes on ,my hobby is changing,now my favorite car is Jeep Rubicon,this is a car I really want ,it’s dynamic performance（性能） and off-road （越野） performance is top-ranking（一流的） ,which other ordinary SUV is unable to compare; the shape and color of this car is magnificent (华丽的） [mg'nfs()nt],and it’s gear-shift system（齿轮转
白居易讽喻诗中以植物为题诗 摘要：白居易曾将自己的诗歌分为讽喻、闲适、感伤和杂律四类。在 他的一百七十三首讽喻诗中，有一些是以植物为题材的诗歌。它们或 借植物来讽喻社会现状；或借植物喻古讽今；较多的则是以植物比喻 一类人。这些诗就艺术成就而言不算是上乘佳作，但它们给我们刻画 了当时形形色色的人物形象，是当时社会环境的一个缩影。 关键词：白居易讽喻诗植物 一、概述 白居易曾将自己的诗分成讽喻、闲适、感伤和杂律四大类。他给自己 的讽喻诗所下的概念，见于《与元九书》中：“自拾遗来，凡所适所感，关于美刺兴比者；又自武德讫元和，因事立题，题为新乐府者， 共一百五十首，谓之讽喻诗。”中国论文联盟编辑。 在白居易的一百七十三首讽喻诗中，有一部分是以植物为题目的。这 一类诗一共有二十三首。它们分别是：《云居寺孤桐》、《京兆府新 栽莲》、《白牡丹》、《紫藤》、《杏园中枣树》、《文柏床》、 《庐山桂》、《湓浦竹》、《东林寺白莲》、《答<桐花>》、《和<松树>》、《有木诗八首》、《涧底松》、《牡丹芳》、《隋堤柳》、 《草茫茫》。其中，《草茫茫》虽以植物为题，但只是以“草茫茫” 起兴，并非以之为题材。所以，在白居易的讽喻诗中，以植物为题材 的诗共有二十二首。在这二十二首诗中，以树为题材的有《云居寺孤桐》、《杏园中枣树》、《文柏床》、《庐山桂》、《湓浦竹》、 《和<松树>》、《有木诗八首》、《涧底松》、《隋堤柳》等十六首。剩下的六首则是以花藤为题。 二、分类 这二十二首诗，可以分为两类：一类以植物来讽喻一种社会现状；另 一类则是以植物来比喻一类人。
（一）以植物讽喻社会现状 以植物来讽喻社会现状的有两首——《牡丹芳》和《隋堤柳》。《牡 丹芳》从诗的开头到“浓姿贵彩信奇绝，杂卉乱花无比方。石竹金钱 何细碎，芙蓉芍药苦寻常”都是描写牡丹的国色天香；正因为如此，“遂使王公与卿士，游花冠盖日相望”；而这种现象，“三代以还文 胜质，人心重华不重实。重华直至牡丹芳，其来有渐非今日。”作者 所讽刺的，正是这种“重华不重实”的社会现状，他期望的是“少回 卿士爱花心，同似吾君忧稼穑”。民以食为天，农业是一个国家的根本，但是当时的上层社会却偏爱牡丹这种名贵花卉，且互相攀比；对 于农作物却毫不关心。诗中虽然有“元和天子忧农桑，恤下动天天降祥。去岁嘉禾生九穗，田中寂寞无人至。今年瑞麦分两岐，君心独喜 无人知”，但若不是皇室也对牡丹情有独钟，怎会有如此的社会影响。作者如此说，也只是为了能委婉地提出谏言，不要触了皇帝的逆鳞罢了。 《隋堤柳》和《牡丹芳》有所不同，与其说它是讽刺社会现状，不如 说是借古讽今。诗的最末句“后王何以鉴前王？请看隋堤亡国树”也 说明了这首诗的写作目的是借隋炀帝荒淫误国的旧事来告诫当朝皇帝 不要重蹈覆辙。试想，若是当时的皇帝在这方面做得很好，作者又何 必写下这首讽喻诗。所以说，它也从侧面反映了当时的社会状况，可 以与《牡丹芳》放在一类。 （二）以植物喻人 以植物来比喻人的则有二十首。根据这些植物所比喻的人的特征：贤者、小人和介于二者之间才干较为平庸的人，这些诗又可以分为三部分。 1.以植物喻贤者 以植物比喻贤者的诗有十一首：《云居寺孤桐》、《京兆府新栽莲》、《白牡丹》、《杏园中枣树》、《文柏床》、《庐山桂》、《湓浦竹》、《东林寺白莲》、《答<桐花>》、《和<松树>》、《涧底松》。
鉴赏小说的基本的方法 (一) 小说的基本常识 1、小说是通过人物、情节和环境的具体描写来反映现实生活的一种文学体裁。人物、情节、环境是小说的三要素。 2、小说刻画人物形象的方法包括：语言描写、肖像描写、行动描写、心理描写和细节描写。 3、小说中的环境包括自然环境和社会环境。 4、小说的叙述方式包括顺叙、倒叙、插叙等。 (二)小说的特点 1、完整的故事情节 2、鲜明的人物形象 3、典型的环境 4、深刻的主题 5、精巧的构思 (三)高考命题要点 1.把握故事情节 2.揣摩人物形象 3.注意环境描写 4.概括主题内容 5.品味语言特色 6.分析写作技巧 一、小说情节的把握 把握好故事情节，是读懂小说的关键，是欣赏小说艺术特点的基础，也是整体感知文章的起点。命题者在为小说命题时，也必定以此为出发点，先从整体上设置理解文章内容的试题。 （一）高考中有关小说情节的命题指向 1、用一句话或简明的语句概括故事情节，或文中共写了哪几件事，请依次加以概括； 2、对某一情节的特点和作用进行分析； 3、对情节的高潮部分或结尾部分作用的理解； 4、小说在叙述故事情节过程中顺叙、倒叙、插叙等方法的使用； 5、哪一个情节最吸引你； 6、情节的合理性探究。 （二）小说中情节的作用 1、交代人物活动的环境； 2、设置悬念，引起读者阅读的兴趣； 3、为后面的情节发展作铺垫或埋下伏笔； 4、照应前文； 5、线索或推动情节发展； 6、刻画人物性格； 7、表现主旨或深化主题。 （三）情节安排的方式及作用 1、就全文来说有一波三折式。作用是引人入胜，扣人心弦，增强故事的戏剧性、可读性。 2、就开头结尾来说有首尾呼应式。作用是使结构紧密、完整。 3、就开头来说有倒叙式（把结局放到开头来写），如《祝福》，先写祥林嫂的死，然后再写祥林嫂是怎样一步步被封建礼教逼向死亡之地的。起到制造悬念的作用。 4、就结尾来说有戛然而止，留下空白式。此外，还有出人意料式、悲剧、喜剧式等。 5、贯穿情节的线索。可作线索的小说因素有：事、物、人、情、时间、空间，如《药》中的“人血馒头”、《故乡》中的“我”等。 （四）分析小说情节的入手方式： 1、抓住场面； 2、寻找线索； 3、理清小说的结构。 （五）分析小说情节时的注意事项 1、情节的发展变化是矛盾冲突发展的体现，分析小说的情节时必须抓住主要的矛盾冲突。 2、分析情节不是鉴赏小说的目的，而是手段，是为理解人物性格、把握小说主题服务的。所以，在分析情节的过程中，要随时注意体会它对人物性格的形成及对揭示小说主题的作用。 （六）情节安排的顺序及作用