Learning Ontologies
to Improve Text Clustering and Classi?cation
Stephan Bloehdorn1,Philipp Cimiano1,and Andreas Hotho2
1Institute AIFB,University of Karlsruhe,D–76128Karlsruhe,Germany
2KDE Group,University of Kassel,D–34321Kassel,Germany
Abstract.Recent work has shown improvements in text clustering and classi?-cation tasks by integrating conceptual features extracted from ontologies.In this paper we present text mining experiments in the medical domain in which the on-tological structures used are acquired automatically in an unsupervised learning process from the text corpus in question.We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones.Our results show that both types of ontologies improve results on text clus-tering and classi?cation tasks,whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.
1Introduction
Text clustering and classi?cation are two promising approaches to help users organize and contextualize textual information.Existing text mining sys-tems typically use the bag–of–words model known from information retrieval (Salton and McGill(1983)),where single terms or term stems are used as fea-tures for representing the documents.Recent work has shown improvements in text mining tasks by means of conceptual features extracted from ontolo-gies(Bloehdorn and Hotho(2004),Hotho et al.(2003)).So far,however, the ontological structures employed for this task are created manually by knowledge engineers and domain experts which requires a high initial mod-elling e?ort.Research on Ontology Learning(Maedche and Staab(2001)) has started to address this problem by developing methods for the automatic construction of conceptual structures out of large text corpora in an unsuper-vised process.Recent work in this area has led to improvements concerning the quality of automatically created taxonomies by using natural language processing,formal concept analysis and clustering(Cimiano et al.(2004), Cimiano et al.(2005)).
In this paper we report on text mining experiments in which we use automatically constructed ontologies to augment the bag–of–words feature representations of medical texts.We compare results both(1)to the base-line given by the bag–of–words representation alone and(2)to results based on the MeSH Tree Structures as a manually engineered medical ontology. We show that both types of conceptual feature representations outperform
Learning Ontologies to Improve Text Clustering and Classi?cation335 the Bag–of-Words model and that results based on the automatically con-structed ontologies are highly competitive with those of the manually engi-neered MeSH Tree Structures.
The rest of this paper is organized as follows.Section2reviews related work.Section3describes our approach for automatically constructing ontol-ogy structures.Section4reviews the concept extraction strategies used to augment bag–of–words vectors.Section5?nally reports on the results of the text classi?cation and clustering experiments.We conclude in section6.
2Related Work
To date,the work on integrating background knowledge into text classi?ca-tion,text clustering or related tasks is quite heterogenous.Green(1999)uses WordNet to construct chains of related synsets from the occurrence of terms for document representation and subsequent clustering.We have reported promising results when using additional conceptual features extracted from manually engineered ontologies recently in Bloehdorn and Hotho(2004)and in Hotho et al.(2003).Other results from similar settings are reported in Scott and Matwin(1999)and Wang et.al(2003).
One of the earlier works on automatic taxonomy construction is reported in Hindle(1990)in which nouns are grouped into classes.Hearst’s seminal work on using linguistic patterns also aimed at discovering taxonomic rela-tions(Hearst(1992)).More recently,Reinberger and Spyns(2005)present an application of term clustering techniques in the biomedical domain.An overview over term clustering approaches for learning ontological structures as used in this paper is given in Cimiano et al.(2005).
Alternative approaches for conceptual representations of text documents that do not require explicit manually engineered background knowledge are for example Latent Semantic Analysis(Deerwester et al.(1990))or Prob-abilistic Latent Semantic Analysis(Cai and Hofmann(2003)).These ap-proaches mainly draw from dimension reduction techniques,i.e.they com-pute a kind of concepts statistically from term co-occurrence information.In contrast to our approach,the concept-like structures are,however,not easily human interpretable.
3Ontology Learning as Term Clustering
In this paper we adopt the approach described in Cimiano et al.(2004)and Cimiano et al.(2005)to derive concept hierarchies from text using clustering techniques.In particular we adopt a vector-space model of the texts,but using syntactic dependencies as features of the terms1instead of relying only on word co-occurrence.The approach is based on the distributional hypothesis 1Here we also refer to multi-word expressions if detected from the syntax alone.
336S.Bloehdorn et al.
(Harris(1968))claiming that terms are semantically similar to the extent to which they share similar syntactic contexts.For this purpose,for each term in question we extract syntactic surface dependencies from the corpus.These surface dependencies are extracted by matching text snippets tagged with part–of–speech information against a library of patterns encoded as regular expressions.In the following we list syntactic expressions we use and give examples of the features extracted from these expressions,whereby a:b++ means that the count for attribute b of instance a is incremented by1: adjective modi?ers:alveolar macrophages
macrophages:alveolear++
prepositional phrase modi?ers:a defect in cell function
defect:in cell function++,cell function:defect in++
possessive modi?ers:the dorsal artery’s distal stump
dorsal artery:has distal stump++
noun phrases in subject or object position:
the bacterium suppresses various lymphocyte functions
bacterium:suppress subj++,lymphocyte function:suppress obj++ prepositional phrases following a verb:
the revascularization occurs through the common penile artery
penile artery:occurs through++
copula constructs:the alveolar macrphage is a bacterium
alveolar macrophage:is bacterium++
verb phrases with the verb to have:
the channel has a molecular mass of105kDa
channel:has molecular mass++
On the basis of these vectors we calculate the similarity between two terms t1and t2as the cosine between their corresponding vectors:cos( (t1,t2))=
t1·t2 t1 · t2 .The concept hierarchy is built using hierarchical clustering tech-
niques,in particular hierarchical agglomerative clustering(Jain et al.(1999)) and divisive Bi-Section KMeans(Steinbach et al.(2000)).While agglomera-tive clustering starts with merging single terms each considered as one initial cluster up to one single cluster Bi-Section KMeans repeatedly splits the ini-tial cluster of all terms into two until every term corresponds to a leaf cluster. The result is a concept hierarchy which we consider as a raw ontology.Due to the repeated binary merges and splits the hierarchy typically has a higher overall depth as manually constructed ones.For this reason we consider in our experiments a reasonable higher number of superconcepts than with manu-ally engineered ontologies.More details of the ontology learning process can be found in Cimiano et al.(2004)and Cimiano et al.(2005).
4Conceptual Document Representations
In our approach,we exploit the background knowledge given by the ontolo-gies to extend the bag–of–words feature vector with conceptual features on a
Learning Ontologies to Improve Text Clustering and Classi?cation337 higher semantic level.In contrast to the simple term features,these concep-
tual features overcome a number of shortcomings of the bag–of–words feature
representation by explicitly capturing multi–word expressions and conceptu-
ally generalizing expressions through the concept hierarchy.In our approach
we only consider concepts which are labelled by noun phrases.As a lot of
additional information is still hidden in the standard bag–of–words model we
use a hybrid representation using concepts and the conventional term stems.
Concept Annotation.We describe here the main aspects of the con-
cept annotation steps,the interested reader is referred to the more detailed
description in Bloehdorn and Hotho(2004).(1)Candidate Term Detection:
due to the existence of multi-word expressions,the mapping of terms to the
initial set of concepts can not be accomplished directly by compiling concept
vectors out of term vectors.We use a candidate term detection strategy that
moves a window over the input text,analyzes the window content and either
decreases the window size if unsuccessful or moves the window further if a
valid expression is detected.(2)To avoid unnecessary queries to the ontology
we analyze the part–of–speech patterns in the window and only consider noun
phrases for further processing.(3)Morphological Transformations:typically
the ontology will not contain all in?ected forms of its entries.Therefore we
use a fallback strategy that utilizes stem forms maintained in a separate index
for the ontology,if the search for a speci?c in?ected form is unsuccessful2.
Generalization.The generalization step consists in adding more general
concepts to the speci?c concepts found in the text,thus leading to some kind
of‘semantic smoothing’.The intuition behind this is that if a term like arry-
thmia appears,the document should not only be represented by the concept
[arrythmia],but also by the concepts[heart disease]and[cardiovascular disease] etc.up to a certain level of generality.This thus increases the similarity with
documents talking about some other specialization of[cardiovascular disease].
We realize this by compiling,for every concept,all superconcepts up to a
maximal distance h into the concept representation.
The result of this process is a“concept vector”that can be appended to
the classical term vector representation.The resulting hybrid feature vectors
can be fed into any standard clustering or classi?cation algorithm.
5Experiments
We have conducted extensive experiments using the OHSUMED text collec-
tion(Hersh et al.(1994))which was also used for the TREC-9?ltering track3. 2Typically,the problem of disambiguating polysemous window content has to be addressed properly(Hotho et al.(2003)).The ontologies we report on in this paper,contained only concepts that were unambiguously referred to by a single lexical entry thus eliminating the need for word sense disambiguation strategies. 3https://www.doczj.com/doc/97986188.html,/data/t9filtering.html
338S.Bloehdorn et al.
It consists of titles and abstracts from medical journals indexed with multiple MeSH descriptors and a set of queries with associated relevance judgements.
Ontologies and Preprocessing Steps:In our experiments we used domain ontologies that were extracted automatically from the text corpus on the one hand and the Medical Subject Headings(MeSH)Tree Structures Ontology as a competing manually engineered ontology on the other.The automatically extracted ontologies were built according to the process de-scribed in section3using the1987portion of the collection,i.e.a total of 54,708documents.The actual concept hierarchy was built using hierarchi-cal agglomerative clustering or divisive Bi-Section KMeans.In overview,we performed experiments with the following con?gurations:
agglo-7000:automatically constructed ontology,linguistic contexts for the7,000 most frequent terms4,taxonomy creation via agglomarative clustering;
bisec-7000:automatically constructed ontology,linguistic contexts for the7,000 most frequent terms4,taxonomy creation via Bi-Section KMeans divisive clus-tering;
bisec-14000:automatically constructed ontology,linguistic contexts for the14,000 most frequent terms,taxonomy creation via Bi-Section KMeans divisive clus-tering;
mesh:manually constructed ontology compiled out of the Medical Subject Head-ings(MeSH)5containing more than22,000concepts enriched with synonymous and quasi-synonymous language expressions.
In all experiments,term stems6were extracted as a?rst set of features from the documents.Conceptual features were extracted as a second set of features using the ontologies above and a window length of3.
Text Classi?cation Setting:For the experiments in the text classi?-cation setting,we also used the1987portion of the OHSUMED collection. Two thirds of the entries were randomly selected as training documents while the remainder was used as test set,resulting in a training corpus containing 36,369documents and a test corpus containing18,341documents.The as-signed MeSH terms were regarded as categories for the documents and binary classi?cation was performed on the top50categories that contained the high-est number of positive training documents.In all cases we used AdaBoost (Freund and Schapire(1995))with1000iterations as classi?cation algorithm and binary weighting for the feature vectors.As evaluation measures for text 4More accurately,we used the intersection of the10,000most frequent terms with the terms present in the MeSH Thesaurus,resulting in approx.7,000distinct terms here.
5The controlled vocabulary thesaurus of the United States National Library of Medicine(NLM),https://www.doczj.com/doc/97986188.html,/mesh/
6In these experiments,term stem extraction comprises the removal of the standard stopwords for English de?ned in the SMART stopword list and stemming using the porter stemming algorithm.
Learning Ontologies to Improve Text Clustering and Classi?cation339
macro-averaged(in%)
Ontology Con?guration Error Prec Rec F1BEP
[none]term00.5352.6035.7442.5645.68
agglo-7000term&concept.sc1000.5352.4836.5243.0746.30
agglo-7000term&concept.sc1500.5352.5736.3142.9546.46
agglo-7000term&concept.sc2000.5352.4936.4443.0246.41
bisec-7000term&concept.sc1000.5253.3936.7943.5646.92
bisec-7000term&concept.sc1500.5254.3637.3244.2647.31
bisec-7000term&concept.sc2000.5255.1236.8743.8647.25
bisec-14000term&concept.sc1000.5351.9236.1242.6045.35
bisec-14000term&concept.sc1500.5352.1736.8643.2045.74
bisec-14000term&concept.sc2000.5253.3736.8543.6045.96
mesh term&concept00.5253.6537.5644.1947.31
mesh term&concept.sc500.5252.7237.5743.8747.16
micro-averaged(in%)
Ontology Con?guration Error Prec Rec F1BEP
[none]term00.5355.7736.2543.9446.17
agglo-7000term&concept.sc1000.5355.8336.8644.4146.84
agglo-7000term&concept.sc1500.5355.9536.6744.3046.99
agglo-7000term&concept.sc2000.5355.7636.7944.3346.97
bisec-7000term&concept.sc1000.5256.5937.2544.9247.49
bisec-7000term&concept.sc1500.5257.2437.7145.4647.76
bisec-7000term&concept.sc2000.5257.1837.2145.0847.68
bisec-14000term&concept.sc1000.5354.8836.5243.8545.86
bisec-14000term&concept.sc1500.5355.2737.2744.5246.27
bisec-14000term&concept.sc2000.5256.3937.2744.8746.44
mesh term&concept00.5256.8137.8445.4347.78
mesh term&concept.sc500.5255.9437.9445.2147.63
Table1.Performance Results in the Classi?cation Setting.
classi?cation we report classi?cation error,precision,recall,F1-measure and breakeven point7.
Table1summarizes some of the classi?cation results.In all cases,the integration of conceptual features improved the results,in most cases at a signi?cant level.The best results for the learned ontologies could be achieved with the bisec-7000ontology and a superconcept integration depth of15 resulting in44.26%macro-avg.F1which is comparable to the results for the MeSH ontology.
Text Clustering Setting:For the clustering experiments we?rst com-piled a corpus which contains only one label per document.We used the106 queries provided with the OHSUMED collection and regarded every answer set of a query as a cluster.We extracted all documents for all queries which occur in only one query.This results in a dataset with4389documents and 106labels(clusters).Evaluation measures for Text Clustering are entropy, purity,inverse purity,and F1-measure7.
Table2presents the results of the text clustering task,averaged over 20repeated clusterings with random initialization.With respect to macro-averaging,the integration of conceptual features always improves results and also does so in most cases with respect to micro-averaging.Best macro-averaged results were achieved for the bisec-14000ontology with20super-7For a review of evaluation measures refer to Sebastiani(2002)in the text classi-?cation setting and to Hotho et al.(2003)in the text clustering setting.
340S.Bloehdorn et al.
macro-averaged(in%)
Ontology Con?guration Entropy F1Inv.Purity Purity
[none]terms2,667419,41%17,22%22,24%
agglo-7000term&concept.sc12,632619,47%17,68%21,65%
agglo-7000term&concept.sc102,580819,93%17,55%23,04%
agglo-7000term&concept.sc202,582819,88%17,69%22,70%
bisec-7000term&concept.sc12,589619,84%17,72%22,53%
bisec-7000term&concept.sc102,536120,17%17,38%24,02%
bisec-7000term&concept.sc202,532120,01%17,38%23,59%
bisec-14000term&concept.sc12,570619,96%17,76%22,80%
bisec-14000term&concept.sc102,438221,11%17,68%26,18%
bisec-14000term&concept.sc202,455720,77%17,46%25,67%
mesh term&concept.sc12,413521,63%17,70%27,78%
mesh term&concept.sc102,388021,93%17,64%28,98%
micro-averaged(in%)
Ontology Con?guration Entropy F1Inv.Purity Purity
[none]terms3,1210814,89%14,12%15,74%
agglo-7000term&concept.sc13,110215,34%14,56%16,21%
agglo-7000term&concept.sc103,137415,21%14,43%16,08%
agglo-7000term&concept.sc203,132515,27%14,62%15,97%
bisec-7000term&concept.sc13,129915,48%14,84%16,18%
bisec-7000term&concept.sc103,153315,18%14,46%15,98%
bisec-7000term&concept.sc203,173414,83%14,23%15,48%
bisec-14000term&concept.sc13,147915,19%14,63%15,80%
bisec-14000term&concept.sc103,197214,83%14,33%15,37%
bisec-14000term&concept.sc203,201914,67%14,07%15,36%
mesh term&concept.sc13,212314,92%14,91%14,93%
mesh term&concept.sc103,236114,61%14,64%14,59%
Table2.Performance Results in the Clustering Setting. concepts.These result is competitive to the one we obtained with the mesh ontology.Surprisingly the best micro avg.results could be found for the strategy adding a single superconcept only.
6Conclusion
The contribution of this paper is twofold.We presented a novel approach for integrating higher-level semantics into the document representation for text mining tasks in a fully unsupervised manner that signi?cantly improves results.In contrast to other approaches,the discovered conceptual structures are well understandable while not based on manually engineered resources. On the other hand,we see our approach as a new way of evaluating learned ontologies in the context of a given text clustering or classi?cation applica-tion.Further work is directed towards improving the automatically learned ontologies on the one hand.On the other,it will aim at a tighter integration of the conceptual knowledge,including the exploration of more?ne-grained and unparameterized generalization strategies.
Acknowledgements This research was partially supported by the European Commission under contract IST-2003-506826SEKT(https://www.doczj.com/doc/97986188.html,) and the by the German Federal Ministry of Education,Science,Research and Tech-nology(BMBF)in the project SmartWeb(http://smartweb.dfki.de).
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阅读（一）： 我想 我想，为了自己，也为了您，为了你们。 以前在一本书上看到这样一段话—“每个人都是一座孤岛，能够决定自己的流放。遭遇风雨，心路不开，愁思就会绵长。直到有一天，撞见阳光??????” 因此，我觉得自己就是一座孤岛，当初决定了自己的流放，一路走来，遭遇了风雨，心路不开，愁思绵长，想到了您，想到了你们，就撞见了属于自己的阳光。从此，就有了信仰；从此，就有了期望；从此，就有了坚持。 我出生在一个平凡的家庭，却生活在一个特殊的家庭。 我原本有个圆满的家庭，之后却是守住了一个单亲的家庭。 我本就是一个平凡的人，一路走来却总是有一段不平凡的经历。 我从不认命，可在有时候，我又不得不低头，正因我没有潜质去抵抗，因此，每一次低头，我都暗暗告诉自己，我要变强，变强，好好地活着，活出自己来。 二十一年的生活平淡着，也体会到了所谓的艰辛； 二十一年的人生平凡着，也领悟到了所谓的无奈； 二十一年的生命平庸着，也明白到了所谓的命运。 我就像一只渺小而又无力的蚂蚁，在生态平衡中，没有自己的领域，奋力去追寻属于自己的领土，却一路坎坷，一路艰险。没有尽头，却也固执的不肯认命，不肯低头。 其实，有时候，自己都不知道为了什么还在这样坚持，为了什么还在继续的走下去。 以前刚上大学的时候，有些迷茫，有些害怕，甚至有些自卑。为何自卑？在亲戚面前觉得没有上一所好的大学而自卑；在朋友同学面前觉得没能跟他们一样上本科院校而自卑；在陌生人面前觉得没有潜质跟他们交流而自卑。就这样自卑了一年。默默的伪装了自己一年。 二十岁的时候，刚上大二，我想全新的开始我的大学生活，重新选取我的生活状态。厄运突如其来，给了我一次全新而又残酷的选取。再次生病，再次住院，再次被架上手术台进行“骨髓炎手术”。 我二十年的人生当中，大部分的记忆都在医院，以病人的身份看透世间纷扰，尝尽世间百味。到之后觉得人这一生还是平平安安，健健康康才是最好。 我无数次的住院，经历了两次大手术。因此，我很怕疼。害怕手术过后麻药过了那种不能喊出来，也不能叫出来的疼。但是，即便是那样的疼，我也不想放下自己的生命。也许我的生命已经面临垂死挣扎，我也不放下。 一九九三年，我出生在一个普通的家庭，原本圆满的家庭正因“重男轻女”四个字，我就成了单亲家庭的孩子，跟着智障的父亲和年迈的外公外婆生活着，因此，从小我便知道“嘲笑”背后的好处。 我是一个安静的女孩儿，我把所有的想法都藏在心底，应对同学的嘲笑，我总是能够一笑了之，可谁又能知道，笑的背后是多大的勇气。因此，我从不轻易哭泣，哪怕受到再大的委屈，我都能冷静的应对。 九岁的时候，第一次生病，转了一次又一次的院，才确定我的病是“化脓性关节炎”。 十五岁的时候，第二次生病，转了两次院，在县里的骨科医院经历了我人生当中第一次比较大的手术—“骨髓炎开窗引流手术”。 二十岁的时候，第三次生病，转了两次院，确切的说是正因在大城市的医院里没有熟人，没有床位，被医院委婉的“赶”了出来。我还记得当时某位教授级别的医生说了一句话，对着我的母亲说：“就算你这天跪下来求我，你的女儿也不能住进来??????”，之后就转到了别
Deems Taylor: The Monster 怪才他身材矮小，头却很大，与他的身材很不相称——是个满脸病容的矮子。他神经兮兮，有皮肤病，贴身穿比丝绸粗糙一点的任何衣服都会使他痛苦不堪。而且他还是个夸大妄想狂。他是个极其自负的怪人。除非事情与自己有关，否则他从来不屑对世界或世人瞧上一眼。对他来说，他不仅是世界上最重要的人物，而且在他眼里，他是惟一活在世界上的人。他认为自己是世界上最伟大的戏剧家之一、最伟大的思想家之一、最伟大的作曲家之一。听听他的谈话，仿佛他就是集莎士比亚、贝多芬、柏拉图三人于一身。想要听到他的高论十分容易，他是世上最能使人筋疲力竭的健谈者之一。同他度过一个夜晚，就是听他一个人滔滔不绝地说上一晚。有时，他才华横溢；有时，他又令人极其厌烦。但无论是妙趣横生还是枯燥无味，他的谈话只有一个主题：他自己，他自己的所思所为。他狂妄地认为自己总是正确的。任何人在最无足轻重的问题上露出丝毫的异议，都会激得他的强烈谴责。他可能会一连好几个小时滔滔不绝，千方百计地证明自己如何如何正确。有了这种使人耗尽心力的雄辩本事，听者最后都被他弄得头昏脑涨，耳朵发聋，为了图个清静，只好同意他的说法。他从来不会觉得，对于跟他接触的人来说，他和他的所作所为并不是使人产生强烈兴趣而为之倾倒的事情。他几乎对世间的任何领域都有自己的理
论，包括素食主义、戏剧、政治以及音乐。为了证实这些理论，他写小册子、写信、写书……文字成千上万，连篇累牍。他不仅写了，还出版了这些东西——所需费用通常由别人支付——而他会坐下来大声读给朋友和家人听，一读就是好几个小时。他写歌剧，但往往是刚有个故事梗概，他就邀请——或者更确切说是召集——一群朋友到家里，高声念给大家听。不是为了获得批评，而是为了获得称赞。整部剧的歌词写好后，朋友们还得再去听他高声朗读全剧。然后他就拿去发表，有时几年后才为歌词谱曲。他也像作曲家一样弹钢琴，但要多糟有多糟。然而，他却要坐在钢琴前，面对包括他那个时代最杰出的钢琴家在内的聚会人群，一小时接一小时地给他们演奏，不用说，都是他自己的作品。他有一副作曲家的嗓子，但他会把著名的歌唱家请到自己家里，为他们演唱自己的作品，还要扮演剧中所有的角色。他的情绪犹如六岁儿童，极易波动。心情不好时，他要么用力跺脚，口出狂言，要么陷入极度的忧郁，阴沉地说要去东方当和尚，了此残生。十分钟后，假如有什么事情使他高兴了，他就会冲出门去，绕着花园跑个不停，或者在沙发上跳上跳下或拿大顶。他会因爱犬死了而极度悲痛，也会残忍无情到使罗马皇帝也不寒而栗。他几乎没有丝毫责任感。他似乎不仅没有养活自己的能力，也从没想到过有这个义务。他深信这个世界应该给他一条活路。为了支持这一信念，他
必修一阅读鉴赏第一单元1.*沁园春?长沙……………………………………毛泽东3 2.诗两首雨巷…………………………………………戴望舒6 再别康桥………………………………………徐志摩8 3.大堰河--我的保姆………………………………艾青10 第二单元4.烛之武退秦师………………………………….《左传》16 5.荆轲刺秦王………………………………….《战国策》18 6.*鸿门宴……………………………………..司马迁22 第三单元7.记念刘和珍君……………………………………鲁迅27 8.小狗包弟……………………………………….巴金32 9.*记梁任公先生的一次演讲…………………………梁实秋36 第四单元10.短新闻两篇别了，“不列颠尼亚”…………………………周婷杨兴39 奥斯维辛没有什么新闻…………………………罗森塔尔41 11.包身工………………………………………..夏衍44 12.*飞向太空的航程……………………….贾永曹智白瑞雪52 必修二阅读鉴赏第一单元1.荷塘月色…………………………………..朱自清2.故都的秋…………………………………..郁达夫3.*囚绿记…………………………………..陆蠡第二单元4.《诗经》两首氓采薇5.离骚………………………………………屈原6.*《孔雀东南飞》(并序) 7.*诗三首涉江采芙蓉《古诗十九首》短歌行……………………………………曹操归园田居(其一)…………………………..陶渊明第三单元8.兰亭集序……………………………………王羲之9.赤壁赋……………………………………..苏轼10.*游褒禅山记………………………………王安石第四单元11.就任北京大学校长之演说……………………..蔡元培12.我有一个梦想………………………………马丁?路德?金1 3.*在马克思墓前的讲话…………………………恩格斯第三册阅读鉴赏第一单元1.林黛玉进贾府………………………………….曹雪芹2.祝福………………………………………..鲁迅3. *老人与海…………………………………….海明威第二单元4.蜀道难……………………………………….李白5.杜甫诗三首秋兴八首(其一) 咏怀古迹(其三) 登高6.琵琶行(并序)………………………………..白居易7.*李商隐诗两首锦瑟马嵬(其二) 第三单元8.寡人之于国也…………………………………《孟子》9.劝学……………………………………….《荀子》10.*过秦论…………………………………….贾谊11.*师说………………………………………韩愈第四单元12.动物游戏之谜………………………………..周立明13.宇宙的边疆………………………………….卡尔?萨根14.*凤蝶外传……………………………………董纯才15.*一名物理学家的教育历程……………………….加来道雄第四册阅读鉴赏第一单元1.窦娥冤………………………………………..关汉卿2.雷雨………………………………………….曹禹3.*哈姆莱特……………………………………莎士比亚第二单元4.柳永词两首望海潮(东南形胜) 雨霖铃(寒蝉凄切) 5.苏轼词两首念奴娇?赤壁怀古定风波(莫听穿林打叶声) 6.辛弃疾词两首水龙吟?登建康赏心亭永遇乐?京口北固亭怀古7.*李清照词两首醉花阴(薄雾浓云愁永昼) 声声慢(寻寻觅觅) 第三单元8.拿来主义……………………………………….鲁迅9.父母与孩子之间的爱……………………………..弗罗姆10.*短文三篇热爱生
教师，我无悔的选择 各位领导，各位老师： 大家好！今天我演讲的题目是：《教师，我无悔的选择》。 有一首诗里这么写道：我是一个老师，我把手中的红烛高高举起，只要我的鸽群能翱翔蓝天，只要我的雏鹰能鹏程万里，只要我的信念能坚如磐石，只要我的理想能永远年轻，我情愿燃烧我的每一寸躯体，让我的青春乃至整个的生命，在跳动的烛火中升华，在血液的鲜红中奔腾…… 我无悔的选择来自对理想的追求。我来自一个教师家庭，我的父母都是教师，在他们的熏陶下，我打小便确立了自己的理想——做一名光荣的人民教师！七年前，怀揣着美丽的梦想，我走上了教师岗位。刚参加工作的时候，我满腔热情地投入到教学当中，融入到学生当中，恨不得一下子把全部的知识都交给学生。清晨，当黎明的曙光洒进教室，我在三尺讲台旁聆听孩子们琅琅的读书声，那是天底下最动听的乐章，我仿佛在享受人间最美的旋律；黄昏，踏着最后一抹夕阳，我目送他们离校的背影；夜晚灯光下，看着孩子们那稚嫩的笔迹，在我严格的要求下逐渐成长，我深深感到：这就是我人生最高的追求，最大的安慰！ 我无悔的选择来自对师爱的坚守。工作的道路并非一帆风顺，美丽的梦想也会偶尔搁浅，当我看到学生对再三讲过的知识还不明白、对再三纠正过的错误一犯再犯时，我着急了；当我看到一些学生淘气、调皮、不求上进、学习成绩一落千丈、自暴自弃的学生使人头疼万分，不知所措时，我连放弃的念头都有了。慢慢地，我发现，我的父母，还有身边的老教师，他们总能不急不躁地面对一切，用老师的温和引发出学生最善良的心，用表扬和鼓励培养班级积极向上的精神。他们的言行感动了我。望着他们呕心沥血几十年依旧孜孜不倦，看着他们青丝变白发依旧兢兢业业，我深刻地理解人们为什么总是这样描写教师：“捧着一颗心来，不带半根草去”，“春蚕到死丝方尽，蜡炬成灰泪始干”。于是，我下定决心：绝不放弃！每一颗草都有泛绿的时候，每一朵花都有盛开的理由，用爱去呵护他们，宽容他们，鼓励他们，为他们的每一次小小的进步而高兴。孩子是天使，我们就是为天使装点翅膀的人！至诚的师爱又唤回了我工作的激情，重新点燃了我美丽的梦想。
演讲的手势语言 一：表情 用手势表达思想感情，使情感表达得真切、具体、形象，渲染作用很大。如高兴时拍 拍手，悲痛时捶胸，愤怒时挥舞拳头，悔恨时敲前额。翘起拇指或鼓掌表示钦佩、赞扬; 双手摊开表示真诚、无可奈何;手摸后脑表示尴尬或不好意思;挥手用力下砍表示坚决果断 的决心或态度;见面时伸手，手掌向前伸，手心向上，胳膊微曲，与别人紧握表示友好和 欢迎等。表情性手势是演说人内在情感和态度的自然流露，鲜明突出、生动形象地表明自 己的观点、情感，能给听众留下深刻的印象。如西方政治家在一些盛大的群众集会上演讲 之前，面对热烈鼓掌的广大听众，他们往往会用双手举过双肩，手心向外，向听众摇摆， 表示对听众的欢迎致以礼貌性的谢意，以及恳请听众可以停止鼓掌，以便他开始演讲。表 情性手势语在演讲中运用得最多，其表现方式和涵义也极为丰富。 1917 年5月14日，列宁在演讲台上，时而来回走动，时而有力地挥动双臂，时而俯身，那激昂的声调，适当的动作，给人以无尽的感染力。对无产阶级革命必胜的信心，对人民 的爱，对敌人的蔑视，是他演讲时激情和力量的源泉。因而在演讲中，他总是那么热情洋溢、精神振奋。列宁的演讲，列宁的激情，没有丝毫的矫揉造作的成份。他不以美丽的词 藻来哗众取宠，不以无病呻吟来博取同情，而是以自己坚强的信念和执着的追求以及对是 非功过的正确认识来激励、鼓舞群众，号召他们起来斗争。 二：达意 用手势表明具体内容，直接指示了演讲者要说的事物，或者表达特定含义。如招手表 示过来;连连摆手表示反对或不要;挥手表示再见或叫人走开;用手指自己表示谈论自己及 其有关的事等。又如，1942年延安整风运动中，毛泽东曾多次为党政军干部作演讲，他把内容归纳为一、二、三、四或甲、乙、丙、丁，并且边讲边用右手扳着左手指，一个一个 地数，其手势语含义直截了当。当说到“你”、“我”、 “他”，或者“这边”、“那边”、“上头”、“下头”等等，都可以用手朝相应 的方向指一下，给听众以实感，让人明白其中的意思。 再如，列宁在演讲时善于运用富有感染力的手势。当起义的工人、士兵攻下冬宫之后，列宁快步登上讲台。他面向台下群众，就像大乐队的指挥，身势稍向前倾，右手掌向前果 断有力地推出。沸腾的冬宫顿时鸦雀无声，列宁震荡环宇的声音开始传向世界……列宁的 这一手势，使人民群众看到了前进的方向，感受到了巨大的力量。多少年来，人们一看到 或想到这一形象，就会对列宁油然而生敬意。
我想好好的活着 本文从网络收集而来，上传到平台为了帮到更多的人，如果您需要使用本文档，请点击下载按钮下载本文档（有偿下载），另外祝您生活愉快，工作顺利，万事如意！ 我想好好的活着，为了自己，也为了您，为了你们。 曾经在一本书上看到这样一段话—“每个人都是一座孤岛，可以决定自己的流放。遭遇风雨，心路不开，愁思就会绵长。直到有一天，撞见阳光??????” 所以，我觉得自己就是一座孤岛，当初决定了自己的流放，一路走来，遭遇了风雨，心路不开，愁思绵长，想到了您，想到了你们，就撞见了属于自己的阳光。从此，就有了信仰；从此，就有了希望；从此，就有了坚持。 ?????? 我出生在一个平凡的家庭，却生活在一个特殊的家庭。 我原本有个圆满的家庭，后来却是守住了一个单亲的家庭。 我本就是一个平凡的人，一路走来却总是有一段不平凡的经历。 我从不认命，可在有时候，我又不得不低头，因
为我没有能力去抵抗，所以，每一次低头，我都暗暗告诉自己，我要变强，变强，好好地活着，活出自己来。 二十一年的生活平淡着，也体会到了所谓的艰辛； 二十一年的人生平凡着，也领悟到了所谓的无奈； 二十一年的生命平庸着，也明白到了所谓的命运。 我就像一只渺小而又无力的蚂蚁，在生态平衡中，没有自己的领域，奋力去追寻属于自己的领土，却一路坎坷，一路艰险。没有尽头，却也固执的不肯认命，不肯低头。 其实，有时候，自己都不知道为了什么还在这样坚持，为了什么还在继续的走下去。 以前刚上大学的时候，有些迷茫，有些害怕，甚至有些自卑。为何自卑？在亲戚面前觉得没有上一所好的大学而自卑；在朋友同学面前觉得没能跟他们一样上本科院校而自卑；在陌生人面前觉得没有能力跟他们交流而自卑。就这样自卑了一年。默默的伪装了自己一年。 二十岁的时候，刚上大二，我想全新的开始我的大学生活，重新选择我的生活状态。厄运突如其来，给了我一次全新而又残酷的选择。再次生病，再次住院，再次被架上手术台进行“骨髓炎手术”。
小学教师演讲稿--无悔的选择无尽的追 求 我曾听过这样的感叹，我生不逢时，没赶上英雄时代，要不我也会扬名天下！我也听过类似的抱怨，我时运不佳，没摊上个好岗位，否则咱也不想当孩子王。但我却要说，选择教师，我今生无悔，也是我毕生的追求。 96年的盛夏，带着对明天的憧憬，走上了三尽讲台，实现了我的梦想。虽是偏远，落后的杨溪村小，我却异常满足。一天到晚和孩子们打交道，我觉得很有意思，很有乐趣。天真活泼，心地善良的孩子们，在我的教育下，一天比一天懂事，一天比一天成熟。我看到了教育的力量，也深深地爱上了自己的事业。 爱岗敬业，诲人不倦，是对每位教育工作者最基本的要求，初涉教坛时，我深知在学校所学的知识远远不够。提高自身业务素质，异常重要。于是我的书桌上就多了些教育专著，教育教学杂志，读读、摘摘，博采他山石，琢为自家玉。平时，常向有经验的老师请教，听课，听讲座，学写论文，取他人之长，补已之短。 爱学生是教师的天职，没有爱就没有教育。我把爱镶在举手投足间，嵌在我的一颦一笑中，让学生时刻感受到了信任与鼓舞。我总是把学生看成自己的弟妹，不失时机地为贫
困的学生送一句安慰，为自卑的孩子送一份自信，为偏激的学生送一份冷静，让学生时刻生活在温暖中。 寒来暑往，风雨八年，我不曾为我的早出晚归而后悔，也不曾为我的挑灯夜战而遗憾。因为在我的学生身上，我看到了我的付出所开的花，所结的果。我从教虽短短八年，但在这八年里，凭着对事业的真诚迷恋，对学生的无比热爱，所教的每届学生都在进步，都在成才。我想，这与我的不懈追求是分不开的。 “衣带渐宽终不悔，为伊消得人憔悴”，在事业默默的耕耘中，我体验到了人生最大的幸福，每个教师节一封封热情洋溢的信，一张张饱含谢意的精致卡片，雪片似的从四面八方飞到我的身边。我的心里总是缀满了骄傲与自豪。我在心底里默默发誓，不为别的就为这些天真无邪的学生，我也要把工作干好，不求轰轰烈烈，但求踏踏实实；不求涓滴相报，但求今生无悔。 扩展阅读：怎样做一个受人敬仰的优秀教师 一、要有强烈的责任感。 衡量一个教师是否合格，最重要的一点就是看其有没有强烈的社会责任感。因为教育工作的根本意义在于通过培养合格的社会公民去优化和推动社会的发展。如果一个教师不
好好活着的经典短句子 1、如果你恨我，那么你就是失败者，而不是我。——艾薇儿 2、作为一个词语，“活着”在我们中国的语言里充满了力量，它的力量不是来自于喊叫，也不是来自于进攻，而是忍受，去忍受生命赋予我们的责任，去忍受现实给予我们的幸福和苦难、无聊和平庸。——余华《活着》 3、有时，我们对未来的恐惧是来自于对未来的不确定性。因而不想也懒得改变现状，然而，生活本该就是丰富多彩的。去学习，去改变自身所存在的不足，努力成为自己心目中的样子，用心体验生命里的每一天，这才叫成长，才叫真真切切的活着。 4、不管怎样，总要活得对得起自己的眼泪吧！ 5、有的人为自己而活，有的人为别人而活。 6、老人黝黑的脸在阳光里笑的十分生动，脸上的皱纹欢乐地游动着，里面镶满了泥土，就如布满田间的小道——余华《活着》 7、不知道还能活多久，不知道还要遭遇多少不幸，不知道还有多少痛苦。可我，还要活下去…… 8、就算被抱怨，不曾被理解，蝼蚁依旧忙碌，大海继续高歌，什么也挡不住它们前进的脚步。活着，就应该自私些。 9、我想起了七哥的话，七哥说生命如同树叶，所有的生长都是为了死亡。殊途却是同归。
七哥说谁是好人谁是坏人直到死都是无法判断清的。七哥说你把这个世界连同它本身看透了之后你才会弄清楚你该有个什么活法。我将七哥的话品味了很久很久，但我仍然没有悟出他到底看透了什么到底作怎样的判断是选择生长还是死亡。我想七哥毕竟还是幼稚且浅薄得像每一个活着的人。——方方《风景》10、尸体上的伤口想要痊愈就只能靠油灰去填补了···活着的身体上的伤口则会流血，然后结疤。这是你的身体哭泣的证据···绝望地、斗争着想要活下去。——间宫心十郎《死化妆师》11、还没有理由值得我死去，我却有着理由要活下去。12、如果思考是生存的证明我真难判断，你是不是一具尸体。——默苍离《金光布袋戏》13、“我喜欢活着，呼吸，甚于喜欢工作。我不觉得我做的东西可以在将来对社会有什么重要意义。因此，如果你愿意这么看，我的艺术就可以是活着，每一秒，每一次呼吸就是一个作品，那是不留痕迹的，不可见不可思的，那是一种其乐融融的感觉。”——马塞尔·杜尚14、这一生，她只希望他能好好活下去，她愿意为此付出任何代价。——玄默《此生不渝》15、我就是这么自私，我就是这么爱自己，我就是这么任性，怎么也是为了自己活着！16、怎么样能以最少的物质最自由地活着，准确说，我怎么样才能以对物质最少的占有心最自由
Frankenstein's Monster Part 1 The story of Frankenstein Frankenstein is a young scientist/ from Geneva, in Switzerland. While studying at university, he discovers the secret of how to give life/ to lifeless matter. Using bones from dead bodies, he creates a creature/ that resembles a human being/ and gives it life. The creature, which is unusually large/ and strong, is extremely ugly, and terrifies all those/ who see it. However, the monster, who has learnt to speak, is intelligent/ and has human emotions. Lonely and unhappy, he begins to hate his creator, Frankenstein. When Frankenstein refuses to create a wife/ for him, the monster murders Frankenstein's brother, his best friend Clerval, and finally, Frankenstein's wife Elizabeth. The scientist chases the creature/ to the Arctic/ in order to destroy him, but he dies there. At the end of the story, the monster disappears into the ice/ and snow/ to end his own life. Part 2 Extract from Frankenstein It was on a cold November night/ that I saw my creation/ for the first time. Feeling very anxious, I prepared the equipment/ that would give life/ to the thing/ that lay at my feet. It was already one/ in the morning/ and the rain/ fell against the window. My candle was almost burnt out when, by its tiny light，I saw the yellow eye of the creature open. It breathed hard, and moved its arms and legs. How can I describe my emotions/ when I saw this happen? How can I describe the monster who I had worked/ so hard/ to create? I had tried to make him beautiful. Beautiful! He was the ugliest thing/ I had ever seen! You could see the veins/ beneath his yellow skin. His hair was black/ and his teeth were white. But these things contrasted horribly with his yellow eyes, his wrinkled yellow skin and black lips. I had worked/ for nearly two years/ with one aim only, to give life to a lifeless body. For this/ I had not slept, I had destroyed my health. I had wanted it more than anything/ in the world. But now/ I had finished, the beauty of the dream vanished, and horror and disgust/ filled my heart. Now/ my only thoughts were, "I wish I had not created this creature, I wish I was on the other side of the world, I wish I could disappear!” When he turned to look at me, I felt unable to stay in the same room as him. I rushed out, and /for a long time/ I walked up and down my bedroom. At last/ I threw myself on the bed/ in my clothes, trying to find a few moments of sleep. But although I slept, I had terrible dreams. I dreamt I saw my fiancée/ walking in the streets of our town. She looked well/ and happy/ but as I kissed her lips，they became pale, as if she were dead. Her face changed and I thought/ I held the body of my dead mother/ in my arms. I woke, shaking with fear. At that same moment，I saw the creature/ that I had created. He was standing/by my bed/ and watching me. His
人教版高中语文必修必背课文精编W O R D版 IBM system office room 【A0816H-A0912AAAHH-GX8Q8-GNTHHJ8】
必修1 沁园春·长沙（全文）毛泽东 独立寒秋， 湘江北去， 橘子洲头。 看万山红遍， 层林尽染， 漫江碧透， 百舸争流。 鹰击长空， 鱼翔浅底， 万类霜天竞自由。 怅寥廓， 问苍茫大地， 谁主沉浮。 携来百侣曾游， 忆往昔峥嵘岁月稠。
恰同学少年， 风华正茂， 书生意气， 挥斥方遒。 指点江山， 激扬文字， 粪土当年万户侯。 曾记否， 到中流击水， 浪遏飞舟。 雨巷（全文）戴望舒撑着油纸伞，独自 彷徨在悠长、悠长 又寂寥的雨巷， 我希望逢着 一个丁香一样地
结着愁怨的姑娘。 她是有 丁香一样的颜色， 丁香一样的芬芳， 丁香一样的忧愁， 在雨中哀怨， 哀怨又彷徨； 她彷徨在这寂寥的雨巷，撑着油纸伞 像我一样， 像我一样地 默默彳亍着 冷漠、凄清，又惆怅。她默默地走近， 走近，又投出 太息一般的眼光
她飘过 像梦一般地， 像梦一般地凄婉迷茫。像梦中飘过 一枝丁香地， 我身旁飘过这个女郎；她默默地远了，远了，到了颓圮的篱墙， 走尽这雨巷。 在雨的哀曲里， 消了她的颜色， 散了她的芬芳， 消散了，甚至她的 太息般的眼光 丁香般的惆怅。 撑着油纸伞，独自
彷徨在悠长、悠长 又寂寥的雨巷， 我希望飘过 一个丁香一样地 结着愁怨的姑娘。 再别康桥（全文）徐志摩 轻轻的我走了，正如我轻轻的来； 我轻轻的招手，作别西天的云彩。 那河畔的金柳，是夕阳中的新娘； 波光里的艳影，在我的心头荡漾。 软泥上的青荇，油油的在水底招摇； 在康河的柔波里，我甘心做一条水草！ 那榆荫下的一潭，不是清泉， 是天上虹揉碎在浮藻间，沉淀着彩虹似的梦。寻梦？撑一支长篙，向青草更青处漫溯， 满载一船星辉，在星辉斑斓里放歌。
演讲中的手势语言 演讲中的手势语言 演讲稿演讲手势演讲中的手势语言 演讲中的手势语言 手势语言有多种复杂的含义，一般可分四类，表达演讲者的情感，使其形象化、具体化的手势为情意手势，也叫感情手势。表示抽象的意念的手势叫象征手势。模形状物，给听众一种具体、形象感觉的手势叫形象手势，也称图示式手势。指示具体对象的手势称为指示手势。有人总结，常见的演讲手势有上举、下压和平移等几类，各类中又分单手、双手两种。每种又可以作拳式、掌式、屈肘翻腕式等。手向上、向前、向内往往表达希望、成功、肯定等积极意义的内容。手向下、向后、向外，往往表达批判、蔑视、否定等消极意义的内容。如空中劈掌表示坚决果断，手指微摇表示蔑视活無所謂，双手摊开表示无可奈何等等。右手紧握拳头从上劈下表达愤慨、决心。讲话的手势大有讲究，但不是靠闭门造车设计出来的，而是在讲坛上，随着演说的内容、听众的情绪、场上的气氛，在演讲者情感的支配下，自然而然喷射出来的。至于选择单式手势还是复式手势，则要看内容的要求，会场的大小，听众的多少，表情达意的強弱而定。手势语言沒有固定的模式，沒有规定的角度，不受先人的制约，也无需导演的引发。它象体操动作一样，绝不是用一个模子套出来的，它需要自己创造。但对于初学者总会有一个模仿的过程，如听演讲、看电影时，注意揣摩，作点积累，这样在演讲时就可以顺手拈来了。演讲中的手势语言相关内容:演讲时的30种手势
演讲的手势可以说是“词汇”丰富，千变万化，没有一个固定的模式。作为一个出色的演讲者平时要认真观察生活，刻苦训练，积极付出实践。下面介绍演讲中常用的手势三十式。 演讲手势的基本规范 在演讲中使用最多、动作最大的要算手势了。它可以随着内容的需要向上、下、左、右、前、侧各个方向挥动。就是在同一个方向还可以有手心向上、向下、向内、向外之别。还可以用拳。手势可单手，可双手。这些都没有机械的规定。 三种演讲中比用的手势 演讲中，手是活动范围最广，活动幅度最大的部位，它包括从肩膀到手指的活动，还有肘、腕、指、掌各部分的协同动作。是态势语言的一个重要组成部分，在演讲起着重要作用，是演讲中演的重要手段之一。 演讲中的肢体语言是掌控他人的力量 在约会中，你更看重对方的什么？美貌、个性、幽默、谈吐，还是别的什么？外表很重要，在第一节里，我们已经看到，心理学的研究证实：外表的作用，在日常生活中具有一致性和普遍性，它是一笔财富。不管你承认与否，这是的确存在的事实。 演讲中的反驳手势 我要反驳一些荒诞的说法：伸出手掌并持平。不对。手掌对你一定要自然。当你对黑板或图表做手势时，站在一边，侧对听众，拇指向内，手持平并正对黑板。又错了。在观众能看见你的情况下尽量用最自然的方法指示。 演讲中常见的“强调”手势
Unit 7 The Monster Deems Taylor 1He was an undersized little man, with a head too big for his body ― a sickly little man. His nerves were bad. He had skin trouble. It was agony for him to wear anything next to his skin coarser than silk. And he had delusions of grandeur. 2He was a monster of conceit. Never for one minute did he look at the world or at people, except in relation to himself. He believed himself to be one of the greatest dramatists in the world, one of the greatest thinkers, and one of the greatest composers. To hear him talk, he was Shakespeare, and Beethoven, and Plato, rolled into one. He was one of the most exhausting conversationalists that ever lived. Sometimes he was brilliant; sometimes he was maddeningly tiresome. But whether he was being brilliant or dull, he had one sole topic of conversation: himself. What he thought and what he did. 3He had a mania for being in the right. The slightest hint of disagreement, from anyone, on the most trivial point, was enough to set him off on a harangue that might last for hours, in which he proved himself right in so many ways, and with such exhausting volubility, that in the end his hearer, stunned and deafened, would agree with him, for the sake of peace. 4It never occurred to him that he and his doing were not of the most intense and fascinating interest to anyone with whom he came in contact. He had theories about almost any subject under the sun, including vegetarianism, the drama, politics, and music; and in support of these theories he wrote pamphlets, letters, books ... thousands upon thousands of words, hundreds and hundreds of pages. He not only wrote these things, and published them ― usually at somebody else’s expense ― but he would sit and read them aloud, for hours, to his friends, and his family. 5He had the emotional stability of a six-year-old child. When he felt out of sorts, he would rave and stamp, or sink into suicidal gloom and talk darkly of going to the East to end his days as a Buddhist monk. Ten minutes later, when something pleased him he would rush out of doors and run around the garden, or jump up and down off the sofa, or stand on his head. He could be grief-stricken over the death of a pet dog, and could be callous and heartless to a degree that would have made a Roman emperor shudder. 6He was almost innocent of any sense of responsibility. He was convinced that
高中语文必修一背诵篇目 1、《沁园春长沙》毛泽东 独立寒秋，湘江北去，橘子洲头。 看万山红遍，层林尽染；漫江碧透，百舸争流。 鹰击长空，鱼翔浅底，万类霜天竞自由。 怅寥廓，问苍茫大地，谁主沉浮？ 携来百侣曾游，忆往昔峥嵘岁月稠。 恰同学少年，风华正茂；书生意气，挥斥方遒。 指点江山，激扬文字，粪土当年万户侯。 曾记否，到中流击水，浪遏飞舟？ 2、《诗两首》 （1）、《雨巷》戴望舒 撑着油纸伞，独自 /彷徨在悠长、悠长/又寂寥的雨巷， 我希望逢着 /一个丁香一样的 /结着愁怨的姑娘。 她是有 /丁香一样的颜色，/丁香一样的芬芳， /丁香一样的忧愁， 在雨中哀怨， /哀怨又彷徨； /她彷徨在这寂寥的雨巷， 撑着油纸伞 /像我一样， /像我一样地 /默默彳亍着 冷漠、凄清，又惆怅。 /她静默地走近/走近，又投出 太息一般的眼光，/她飘过 /像梦一般地， /像梦一般地凄婉迷茫。 像梦中飘过 /一枝丁香的， /我身旁飘过这女郎； 她静默地远了，远了，/到了颓圮的篱墙， /走尽这雨巷。 在雨的哀曲里， /消了她的颜色， /散了她的芬芳， /消散了，甚至她的 太息般的眼光， /丁香般的惆怅/撑着油纸伞，独自 /彷徨在悠长，悠长 又寂寥的雨巷， /我希望飘过 /一个丁香一样的 /结着愁怨的姑娘。 （2）、《再别康桥》徐志摩 轻轻的我走了， /正如我轻轻的来； /我轻轻的招手， /作别西天的云彩。 那河畔的金柳， /是夕阳中的新娘； /波光里的艳影， /在我的心头荡漾。 软泥上的青荇， /油油的在水底招摇； /在康河的柔波里， /我甘心做一条水草！那榆阴下的一潭， /不是清泉，是天上虹 /揉碎在浮藻间， /沉淀着彩虹似的梦。寻梦？撑一支长篙， /向青草更青处漫溯， /满载一船星辉， /在星辉斑斓里放歌。但我不能放歌， /悄悄是别离的笙箫； /夏虫也为我沉默， / 沉默是今晚的康桥！悄悄的我走了， /正如我悄悄的来；/我挥一挥衣袖， /不带走一片云彩。 4、《荆轲刺秦王》 太子及宾客知其事者，皆白衣冠以送之。至易水上，既祖，取道。高渐离击筑，荆轲和而歌，为变徵之声，士皆垂泪涕泣。又前而为歌曰：“风萧萧兮易水寒，壮士一去兮不复还！”复为慷慨羽声，士皆瞋目，发尽上指冠。于是荆轲遂就车而去，终已不顾。 5、《记念刘和珍君》鲁迅 （1）、真的猛士，敢于直面惨淡的人生，敢于正视淋漓的鲜血。这是怎样的哀痛者和幸福者？然而造化又常常为庸人设计，以时间的流驶，来洗涤旧迹，仅使留下淡红的血色和微漠的悲哀。在这淡红的血色和微漠的悲哀中，又给人暂得偷生，维持着这似人非人的世界。我不知道这样的世界何时是一个尽头！
竞选校长演讲稿：无悔的选择尊敬的各位专家、各位评委： 大家好！ 我来自中国枇杷之乡＿＿xx县xx镇，我抽到的演讲题目是《无悔的选择》，下面是我简短的演讲： 茫茫宇宙，每颗星都有自己的位置；芸芸众生，每个人都有自己的追求。教师，太阳底下最光辉的职业，这就是我无悔的选择！ 小时候，家里穷，寒冬腊月仍光着脚丫，一天，我小学班主任王淑君老师把我领到她的寝室，拿出一双布鞋送给我说：“穿上它，好好读书！你会有出息的！”顿时一股暖流流遍了全身。从此，我暗下誓言：长大了，我也要像王老师一样做一位好老师。这誓言真成了我今后的精神支柱。初中毕业那年，我义无反顾地在志愿栏填了“四川省井研师范学校”，我如愿以偿了。[] 书生意气，风华正茂的师范求学时期，我喜欢读一些与教育有关的课外书籍。读《武训传》我伤心掉泪；谈孔子我肃然起敬；说陶行知我感慨万千；评中外教育史，我豪情万丈。我深深懂得：民族的希望在少年，少年智则国智，少年强则国强。而培养少年之责任，全在我们教师！教师是支撑
民族的擎天柱！ 师范毕业，我被分配到一所山村小学，面对破破烂烂的校舍，一双双渴求知识的农家子弟的眼睛，我的心都碎了。孤灯作伴，淡饭粗茶，而心中的理想之火却在熊熊燃烧。我认真钻研大纲、教材、探索教法、学法；我走村串户搞家访，挤出微薄的工资，资助贫困学生。几番风雨，风度春秋，苦心人，天不负，我教的学生在毕业会考中荣获全区前茅，我调任区少年委员，在送别的那天，我的学生浸着眼泪拉着我的手久久不松开，朴实的村民聚集在校门口给我挥手致意。此时此刻我真切地感受到了幸福的滋味。区少年委员，在老百姓心中是个“官”，的确工作环境和对自己今后的发展比做一个山村教师强百倍，但我仅仅只做了一年，坚决要求下去仍做教师，当时，真有人不理解，觉得我是不是神经出了点问题，但我自己知道，我清醒得很，我是放不下我的学生呀！领导经过一段时间的考虑终于同意了我的要求，我又回到别了一年的课堂。我回到课堂犹如把鱼放回到了大海。 从教近18年，我教过小学语文、数学，也教过地理、历史、社会、音乐；当过班主任、少先队辅导员、教研组长、教导主任、校长，无论在学校什么岗位，我都觉得很适合我，无论自己有多么不快，心中有多大的委屈，只要一站上讲台，面对一张张纯真的笑脸，心里就充满了幸福与自豪。为了教
演讲中手势的运用方法 演讲活动是演讲者与听众进行思想、意念和情感交流的过程，是人际交流的高级形式，今天小编给大家分享一些演讲中手势的运用方法，希望对大家有所帮助。 演讲中手势的运用方法一、把手放到上半身来 1.双手不要自然下垂 这样每次要做手势时，都要把手拿上来。稍微有点怯场的话，手还没抬起来，动作就结束了，而观众的视线是集中在你上半身的。 3.正确的方法是双手放在肚子前 比如下图默克尔的作法。这样需要做手势时，手会自然伸出来。具体怎么舒服怎么放，可以合十，可以互握，随便你。 变体：如果想酷一点，可以一只手放在口袋里，或者其他，但是必须保证一只手在上半身。 演讲中手势的运用方法二、不要事先计划手势
只要像之前说的那样，为手提供了足够的方便，那么在需要的时候，手势会自然跟上，这个不用学。每个人都有不同的风格，有的人手势少，有的人手势多，这些都是正常的。 需要注意的是自己的情绪，如果自己紧张或无聊，你的手势会自然把这些情绪也表现出来。如果自己本来就很High，或者极想说服听众，那么运用什么手势，你的手自己知道。 下面给大家分享一些手势的演变由来： 1、切菜 将右手掌放于胸前，大拇指与食指之间角度大约为60-75度。然后向前切出，切出幅度分为三个幅度，小、中、大(左手同理)。 将手掌置于胸前是单手势练习的标准式，待您熟悉后可以自然运用，不用每个手势都把手置于胸前。 2、拍菜 将右手掌放于胸前，大拇指与食指之间角度大约为60-75度。然后手掌从胸前向前拍出。幅度分为小、中、大三个幅度。 3、扔菜 将右手掌放于胸前，大拇指与食指之间角度大约为60-75度。然后手掌从胸前向右划弧。幅度分为小、中、大三个幅度。