A Method to Combine Linguistic Ontology-Mapping Techniques

2We use the term ontologies to include light-weight ontologies such as vocabularies and thesauri 3https://www.wendangku.net/doc/7212651730.html,/fnic/foodcomp/Data/SR16/sr16.html

4https://www.wendangku.net/doc/7212651730.html,/agrovoc

In1992Hearst devised a set of lexico-syntactic patterns for domain aspeci?c hy-ponym extraction[4].His patterns found entrance in many applications such as Cimiano and Staab’s PANKOW system.[3]The?rst method we discuss in this paper is similar to their work.

In their2004paper Cimiano and Staab try to accomplish two things.The?rst is a instance classi?cation task:to classify geographical entities such as Amsterdam(City), Atlantic(Ocean),etc.The second is a subclass learning task:to reconstruct a subclass hierarchy of travel destinations mentioned in the LonelyPlanet website5.The method they use is the same for both tasks.They send Hearst patterns describing the relation they want to test to the Google API and depending on the number of hits Google returns they accept of reject the relation.For instance,the query“cities such as Amsterdam”yields992hits.Depending on which threshold they put on the number of hits they achieved Precision between.20and.35and Recall somewhere between.15and.08. The higher the threshold,the higher the Precision and the lower Recall.

What we want to accomplish is a bit more complicated than either of Cimiano and Staab’s tasks for two reasons.The food domain is less well-de?ned than the geographi-cal domain,in which there are exhaustive thesauri such as TGN.The relations between the concepts are clearly de?ned.Countries have exactly one capital.Countries can bor-der each other,etc.In the food domain such consensus does not exist.This means the evidence for relations that can be found in Google can be expected to be more ambigu-ous in the food domain than in the geographical domain.

3Experimental Set-up

Our set-up consists of the two thesauri we want to connect,the auxiliary sources of knowledge we use to learn the mappings from,and a gold-standard mapping to assess the quality of the learnt relations.In Section3.3we discuss the gold standard and the evaluation measures we use.

3.1Thesauri

AGROVOC This is a multi-lingual thesaurus made by the Food and Agriculture Orga-nization of the United Nations(FAO).It consists of roughly17,000concepts and three types of relations derived from the ISO thesaurus standard:use(preferred term),rt(re-lated term)and bt(broader term).We use a RDFS version of this thesaurus where the broader term relation is represented with the RDFS subClassOf relation.The max-imum depth of AGROVOC’s subclass hierarchy is eight.Figure1shows an excerpt from AGROVOC.The text boxes are classes with their names and the arrows stand for subclass relations.

SR-16This is the Nutrient Database for Standard Reference version16(SR-16)made by the United States Department of Agriculture(USDA),converted to RDFS and OWL by the AID group.It consists of roughly6500concepts and one relation,RDFS subClassOf.The maximum depth of the subclass hierarchy of SR-16is four.Figure1 shows an excerpt from SR-16.

5https://www.wendangku.net/doc/7212651730.html,/destinations

Fig.1.excerpts from AGROVOC(left)and SR-16(right)

3.2Auxiliary Knowledge Sources

We used one general and one domain-speci?c source.The general source is Google and the domain-speci?c source is the https://www.wendangku.net/doc/7212651730.html,’s Cooking Dictionary.

Google Google6is an open domain search engine.At the moment(mid2005)Google indexes more than8billion pages.The large size of Google allows makes it possible to use it for statistical comparison of words.Google has a programming interface called the Google API,that at the moment allows researchers to pose1000queries per day. https://www.wendangku.net/doc/7212651730.html,’s Cooking Dictionary The https://www.wendangku.net/doc/7212651730.html, Cooking Dictionary provides de?nitions for ingredients,culinary terms and cooking techniques.It contains 1076de?nitions.An example entry is:“Basmati an aged,aromatic long-grain rice grown in the Himalayan foothills;has a creamy yellow color,distinctive sweet,nutty aroma and delicate?avor...”

3.3Evaluation Method

In order to do full evaluation of the quality of a mapping between AGROVOC and SR-16one would have to assess all possible subclass relations between a thesaurus of roughly17,000and one of around6500classes.This sums up to something of the order of hundreds of millions of possible mapping relations.With smart pruning of the possible mapping this still would have left us with more work than time allowed. Therefor we took samples from both thesauri on a common topic.From SR-16we took one set of concepts about meats,containing the parts about beef,pork and poultry (chicken,turkey bacon,ham,etc.).From AGROVOC we took two sets of concepts,one containing the part about animal products(minced meat,cheese,leather,etc.),and one containing the part about food categories(processed foods,instant foods,snack foods, etc.).

6https://www.wendangku.net/doc/7212651730.html,

For the experiments with Google we created a gold standard mapping by hand from the set of SR-16concepts to both sets of AGROVOC concepts.The size of the mapping from meats to animal products is31relations out of3696possible relations.The size of the mapping from meats to food categories is32relations out of792possible relations.

The experiments with the https://www.wendangku.net/doc/7212651730.html, Dictionary yielded few results,dis-tributed evenly over the thesauri,which made it hard to choose a subset of the thesaurus that contained a reasonable number of mapping relations.Therefor,we evaluated only the returned results.This means we are unable to say anything about Recall of the tech-niques using the https://www.wendangku.net/doc/7212651730.html, Dictionary.

The measures we used are Precision,Recall and F-Measure as used throughout the literature.7The F-Measure we use gives Precision and Recall an equal weight. Protocol The protocol we used can be summarized as follows:All concepts are to be interpreted in their original context.For instance,in AGROVOC chicken is a subclass of product,which means none of the individuals of the chicken class are live chick-ens.Taking this into account chicken is not a subclass of frozen foods,because some chicken products are never frozen,but chicken is a subclass of poultry,because all chicken products qualify as poulty.

4Experiments

4.1Hearst patterns and Google hits

The mapping technique described in this section is approximately the same as Cimiano and Staab’s“Learning by Googling”method.It derives relations from Google hit counts on certain queries.

Method

1.Create hypothetical relations between pairs of concepts from both thesauri.

For this experiment we chose to investigate all possible relations from any of the concepts in the prede?ned set of SR-16concepts to any of the concepts in both of the prede?ned sets of AGROVOC concepts(see Section3.3).

2.Construct Google queries containing Hearst patterns for each pair of concepts.

We chose to use the same Hearst patterns as Cimiano and Staab[3]except the apposition and copula patters,to reduce the number of Google queries,because these patterns did not yield enough results to be useful.The patterns are listed in the Table1.Since we are only interested in the combined result of all the patterns we can further reduce the number of queries by putting the patterns in a disjunction.

We chose the disjunction to be as long as possible given the limit Google imposes on the number of terms in a query(which was32at the time).

3.Send the queries to the Google API.

4.Collect the hit counts for all Heart patterns that give evidence for the existence

of a relation.For instance,add the hits on the queries“milk products such as cheese”,“milk products including cheese”,etc.Since all these hits give a bit of evidence that cheese is a subclass of milk products.

7https://www.wendangku.net/doc/7212651730.html,/wiki/Information_Retrieval

concept1such as concept2

such concept1as concept2

concept1including concept2

concept1especially concept2

concept1and other concept2

concept1or other concept2

Table1.Hearst patterns used in this paper

5.Accept all hypothetical relations that get more hits than a certain threshold

value.Reject all others.

Results The average number of hits for the mapping to food categories is about2.5 and to animal products it is about1.3.Only about2.5%of the patterns had one or more hits.The maximum number of hits we found was in the order of1000,while Cimiano and Staab?nd hit counts in the order of100,000.We suspect that this is the case because people rarely discuss the ontological aspects of food,because it is assumed

to be common knowledge–everybody knows beef is a kind of meat–and hence can be

left out.Since the total number of hits is so low we chose not to use a threshold,but to accept all relations that had one or more hits instead.Precision and Recall are shown in Table2.

Precision Recall F-Measure

to animal products.17(10/58).32(10/31).22

to food categories.30(17/56).53(17/32).38

Table2.Results of the Google hits experiment

Discussion The performance of the PANKOW system of Cimiano and Staab on geo-graphical data is a Precision of.40with a Recall of around.20for instance classi?cation and a Precision of.22and a Recall of.16for subclass extraction.

Overall Recall seems to be less of a problem in the food domain than in the geo-graphical domain.The decent Recall values can be explained by the large size of the current Google corpus.On simple matters it is quite exhaustive.Even though the to-

tal hit counts in the food domain are lower than in the geographical domain it seems that a greater percentage of the relations is mentioned in Google.Apparently not all LonelyPlanet destinations have been discovered by the general web public.If you are interested in really high Recall in the?eld of geography you can simply look up your relations in the Getty Thesaurus of Geographic Names(TGN)8.

Precision of the mapping to animal products seems to be comparable to the sub-class learning task Cimiano and Staab set for themselves.The overall low Precision can

be explained by the fact that when you use Google as a source of mappings between two thesauri you turn it from one into two mapping problems:from the thesaurus to Google;and then from Google to the other thesaurus.That means you have to bridge a vocabulary gap twice and hence introduce errors twice.

8https://www.wendangku.net/doc/7212651730.html,/research/conducting_research/vocabularies/tgn

Precision of mapping to food categories using Google hits seems to be comparable to that of instance classi?cation.Mapping to animal products,i.e.mapping between concepts of similar speci?city,appears to be more dif?cult.

4.2Hearst patterns and Google Snippets

The second mapping technique is a modi?cation of the previous technique.Instead of deriving relations from Google hit counts we analyze the snippets presented by Google that summarize the returned documents.We try to improve performance by shallow parsing the context of the occurrence of the Hearst pattern and remove false hits. Method

1.Follow step1through3from the“Hearst patterns and Google hits”method.

2.Collect all the snippets Google returns.Snippets are the short exerpts from the

web pages that show a bit of the context of the query terms.

3.Extract the patterns.To accomplish this we part-of-speech tag the snippets with

TreeTagger and recognize sequences of adjectives and nouns as concept names.

Then we try?nd all Hearst patterns over the concept names in the snippets.

4.Discard all patterns that contain concept names that do not exactly match the

original concept names.For instance,if the original pattern looked like“soup such as chicken”,discard the matches on“soup such as chicken soup”,because these give false evidence for the relation chicken subClassOf soup.We ignore pre?xes to the concept names from the following list:“other”,“various”,“varied”,“quality”,“high quality”,“?ne”,“some”,and“many”.This uni?es concept names such as“meat products”and“high quality meat products”.

5.Count every remaining occurrence of the pattern as evidence that the relation

holds.

6.Follow step4and5from the“Hearst patterns and Google hits”method. Results Analysis of the snippets improves Precision while sacri?cing Recall.Overall performance indicated by the F-Measure does not chance much.Shallow parsing the snippets removed many false hits.For instance,“salads such as chicken salad”does not lead to chicken subClassOf salad anymore.The exact Precision and Recall are shown in Table3.

Precision Recall F-Measure

to animal products.38(7/18).22(7/31).27

to food categories.50(12/24).37(12/32).42

Table3.Results of the Google snippets experiment

Discussion Even the Precision achieved with mapping to concepts of similar speci?city (to animal products)is comparable to the level PANKOW achieves for instance classi-?cation.The mapping to food categories,which is closer to the instance classi?cation task,now achieves a higher Precision and Recall than PANKOW.

As Cimiano and Staab noted downloading the whole documents for analysis could further improve the results.This might even improve Recall a bit if these documents contain more good Hearst patterns than those that caused them to appear in Google’s result set.

4.3Extraction from a Dictionary

With the third mapping technique we try to exploit the implicit editor’s guidelines of a dictionary to achieve an even higher grade of Precision than the Google Snippets technique described in the previous section.As an example we took a dictionary that includes terms from both thesauri,the https://www.wendangku.net/doc/7212651730.html, Cooking Dictionary.This dictionary is relatively small compared to the thesauri,but it covers about the same ?eld as SR-16.

Method

Find regularities in the dictionary that highly correlate with subclass relations.

We found that the editor of the dictionary often starts a de?nition with the super-class of the described concept.The following steps are tailored to exploit this reg-ularity.

1.Select all entries describing a concept that literally matches a concept from

AGROVOC or SR-16.

2.Parse the entry with MINIPAR.

3.Extract the?rst head from the parse tree.For instance,the entry of the concept

basmati starts with“an aged,aromatic long-grain rice grown in...”The?rst head in this sentence is“rice”.

4.Check if the?rst head corresponds to a concept in the other thesaurus If

basmati is a concept from AGROVOC,try to?nd the concept rice in SR-16and vice versa.

5.Construct a subclass relation between the concept matching the entry name

and the one matching the?rst head.

Results More than half of all the returned relations,even those failing the check in step4,are correct subclass relations according to our strict evaluation protocol.As expected,given the relatively wide scope of the dictionary,step4eliminates most of the results.However the mapping relations that are left are of high quality.The exact results are shown in Table4.

Precision

relations not forming a mapping.53(477/905)

mapping entire AGROVOC–SR-16.75(16/21)

Table4.Results of the dictionary extraction experiment

Discussion We exploited a regularity in the syntax of the data.This yields high Pre-cision results.Clearly,Recall of this method is dependent on the size of the dictionary and the overlap between the dictionary and the thesauri.

We noticed that most of the errors could have been?ltered out by looking for ev-idence on Google.For instance,the entry:“leek a member of the lily family(Allium porrum);...”would cause our technique to suggest the relation leek subClassOf member.One query could have removed this false relation from the result list,because “member such as leek”gives no hits on Google.

4.4Combination of Google hits&Dictionary extraction

The fourth technique is an improvement to the dictionary extraction technique.We use the Google hits technique to?lter false relations out of the list of results provided by extraction.

Method

1.Follow all the steps of the Dictionary Extraction method.This yields a list of

relations.

2.For each extracted relation follow step2–5from the Google hits method.This

?lters out all relations for which no evidence can be found on Google using Hearst patterns.

Results Applying the Google hits technique as a sanity check on the extraction results greatly reduces the number of relations.Precision of this smaller result set is higher than with both the Google hits and dictionary extraction technique.Around63%of the correct results were removed versus92%of the incorrect results.The results are shown in Table5.

Precision

relations not forming a mapping.53(477/905)

after Google hits sanity check.84(178/210)

mapping entire AGROVOC to SR-16.75(16/21)

after Google hits sanity check.94(15/16)

Table5.Results of combining dictionary extraction and Google hits Discussion The combination of Google hits and a dictionary gave the best Precision of the four techniques.Most of the mismatches caused by de?nitions that did not exactly?t the regularity that we exploited with the dictionary extraction technique were removed by applying the Google hits technique.On the other hand,a substantial portion of the correct results was also removed.

We noticed that most of the incorrect relations that were not removed are eas-ily recognizable by hand.If the superclass is not directly food related the relation is usually false.For instance,mayonnaise subClassOf cold.Most relations to latin

names of plants were inverted.For instance,rosmarinus of?cinalis subClassOf rosemary.There is another member of the rosemary family,“Rosmarinus eriocalix”, so rosmarinus of?cinalis should be a subclass.

5Method Proposal

As we discussed in Section3.3simply checking all possible relations between two ontologies is task of quadratic complexity.In theoretical computer science this might qualify as a polynomial with a low degree,but for a mapping technique that uses the Google API(which only allows1000queries per account per day)this means it does not scale well.Furthermore,assessing a quadratic number of relations by hand is often not feasible.Therefor we propose to combine high Precision techniques and techniques that achieve a high Recall per human assessment.The method we propose is as follows: 1.Find a small set of high Precision mapping relation as starting points,prefer-

ably distributed evenly over the ontologies.This could be done with the last two techniques we described or with tools such as PROMPT9.Which technique works best depends largely on the naming conventions used in the ontologies.

2.Manually remove all the incorrect relations.Assessing the results of the dictio-

nary extraction technique took about one man hour.

3.For each correct relation select the concepts surrounding the subject and ob-

ject concepts.For instance,if the SR-16concept cheese(see Figure1)was cor-rectly mapped as a subclass of the AGROVOC concept Milk products,one would select a subclass tree from SR-16that contains cheese and a subclass tree from AGROVOC that contains Milk products.This can be accomplished in the follow-ing two steps:

(a)Travel up the subclass hierarchy from the starting point.Go as far as pos-

sible as long as it is still clear what is subsumed by the examined concept, without having to examine the subtrees of the sibling concepts.A suitable top concept from SR-16could be Dairy and egg products because it is imme-diate clear to us what is subsumed by this concept without having to look at the Pork products concepts.A suitable top concept from AGROVOC could be Processed animal products.

(b)Select all subclasses of the two top concepts.Collect the concepts as two sets.

This could be done using tools such as Triple2010or Sesame11.

4.Find relations between the two sets of concepts returned in the previous step.

This could be done with the Google snippets technique.

5.Manually remove all incorrect relations.The evaluation of the mapping between

the AGROVOC animal product concepts and the SR-16meat concepts took us four man hours.Assessing all the mappings returned by the previous steps could take days.The higher the applied mapping techniques’Precision,the less time this step takes.

9https://www.wendangku.net/doc/7212651730.html,/plugins/prompt/prompt.html

10https://www.wendangku.net/doc/7212651730.html,/packages/Triple20

11https://www.wendangku.net/doc/7212651730.html,

6.Manually add all omissions.Creating a list of omissions during the assessments

of the previous step reduces the amount of work in this step.The higher the applied mapping techniques’Recall,the less time this step takes.

This method reduces the search space by eliminating cross-references between concepts in unrelated parts of the ontologies.For instance,possible relations between concepts in the part of AGROVOC about legumes and in the part of SR-16about poultry would be ignored if step1did not yield any relations between those parts.Hence the number of queries we have to send to Google is reduced along with the number of necessary manual assessments low.

6Discussion

We discussed four ontology mapping techniques and evaluated their performance. There is a clear trade-off between Precision and Recall.The more assumptions we make the higher Precision gets and the lower Recall.We showed that exploiting syntactic in-formation by using a part-of-speech tagger can improve Precision of ontology-mapping methods based on Google hits such as our Google hits method and possibly PANKOW.

We showed that in our experiments?nding subclass relations to generic concepts such as food categories is easier than mapping concepts that are roughly equal in speci-?city.We hypothesize that this is because the former discriminate more clearly between different interpretations of concepts and are therefor used more often.For instance,the phrase“chickens such as roosters”is less discriminating about the meaning of the word “rooster”than“poultry such as roosters”or“birds such as roosters”.

Furthermore,we introduced a method that extends the PANKOW two-step method by Cimiano and Staab to decrease the number of necessary Google queries and the amount of manual work.

7Acknowledgements

This paper has bene?tted from input from the AID group’s participants:Pieter Adriaans, Jan van Eijck,Leonie IJzereef,Machiel Jansen,Sophia Katrenko,Hap Kolb,Maarten de Rijke and Guus Schreiber.Especially Sophia Katrenko,who provided the RDFS and OWL version of SR-16.Furthermore we want to thank Marco Roos and Scott Marshall from the Micro Array Department of the University of Amsterdam,Michel Klein at the Computer Science department of the Free University Amsterdam for valuable discus-sions,Victor de Boer who organized the Ontology Learning and Population Workshop at the Human-Computer Studies Laboratory of the University of Amsterdam and and everybody who https://www.wendangku.net/doc/7212651730.html,st we want to thank Thijs de Graaf,Wessel Kraaij and Dolf Trieschnigg at the Signal Processing group at TNO Science&Industry. References

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English Linguistic

Chapter 1 Introduction 1. Linguistic and English Linguistic Linguistic 的目的 aims at developing a theory general linguistic 一般语言学 descriptive linguistic 描述性语言学 general linguistic (一般语言学 ) 为 descriptive linguistic (描述性语言学) 提供了 framework(框架), 这也就是为什么 general linguistic 能够被分析和被描述. General linguistic and descriptive linguistic are complementary to each other (相互补充). English linguistic is a kind of descriptive linguistics. 2.The nature of languages (语言的本质) 1. language is a system 2. language is symbolic 3. language is a system of vocal symbols The system of language is called langue 1. language is a system the speaker ’s speech is called parole Competence is the speaker-hearer’s knowledge of his language Performance is the actual use of language in concrete situation 2. language is symbolic ( 语言是象征意义的) 3. language is a system of vocal symbols ( 语言是一种声音符号) 语言学侧重研究的是 speech 不是 written form Reason 1. Biologically (生物上来讲) 儿童学习说比学习读写早得多. 2. Functionally (功能上来讲) 日常生活中口语使用比书面语频繁的多. 3. Historically ( 历史而言) 口头语使用在书面语之前,当今世界有许多语言并没有 留 下 文 字 记录 Language is arbitrary Language is creative Language is double-structured Language is changeable de Saussure 的理论 Chomsky 的理论

电大公共行政学小抄名词解释

1.公共行政学:公共行政学是研究公共组织依法处理政务的有效性、公平性、民主性的规律的交叉性与综合性学科。（在这里公共组织主要是指政府，公共行政就是政府行政。） 2.公共行政环境:公共行政环境是指直接或间接地作用或影响公共组织、行政心理、行政行为和管理方法与技术的行政系统内部和外部的各种要素的总和。 3.组织文化:组织文化是指组织在一定的环境中，逐步形成的全体公共组织成员所共同信奉和遵守的价值观，并支配他们的思维方式和行为准则。（组织文化在政府也可以称之为公共行政组织文化，在企业则称之为企业文化。组织文化包括组织观念、法律意识、道德感情和价值观等。） 4.政府职能:政府职能是指政府在国家和社会中所扮演的角色以及所应起的作用。（换句话说，就是指政府在国家和社会中行使行政权力的范围、程度和方式。） 5.市场失效:市场失效是指因为市场局限性和缺陷所导致资源配置的低效率或无效率,并且不能解决外部经济与外部不经济的问题以及社会公平问题。 6.行政体制:行政体制指政府系统内部行政权力的划分、政府机构的设置以及运行等各种关系和制度的总和。 7.地方政府体制:地方政府体制是指地方政府按照一定的法律或标准划分的政府组织形式. 8.行政区划体制:行政区划体制是指根据一定的原则将全国领土划分为若干部分和若干层次的管理区域，并设置相应的行政机关的组织体制。 9.完整制:完整制又叫一元统属制，是指公共组织的同一层级或同一组织内部的各个部门，完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。 10.分离制:分离制又称多元领导制，是指一个公共组织的同一层级的各个组织部门或同一组织部门，隶属于两个或两个以上公共组织或行政首长领导、指挥和监督的组织类型。 11.首长制:首长制又称独立制、一长制或首长负责制。它是指行政首长独自掌握决策权和指挥权，对其管辖的公共事务进行统一领导、统一指挥并完全负责的公共组织类型。 12.层级制:层级制又分级制，是指公共组织在纵向上按照等级划分为不同的上下节制的层级组织结构，不同等级的职能目标和工作性质相同，但管理范围和管理权限却随着等级降低而逐渐变小的组织类型。 13.机能制:机能制又称职能制，是指公共组织在横向上按照不同职能目标划分为不同职能部门的组织类型。14.行政领导者:行政领导者是指在行政系统中有正式权威和正式职位的集体或个人。 15.委任制:亦称任命制，是指由立法机关或其他任免机关经过考察而直接任命产生行政领导者的制度。 16.考任制:考任制是指由专门的机构根据统一的、客观的标准，按照公开考试、择优录取的程序产生行政领导者的制度。 17.行政领导权力:行政领导权力是指行政领导者在行政管理活动中,利用其合法地位以不同的激励方式和制约方式,引导下属同心协力达成行政目标的影响力。18..行政领导责任:行政领导责任是指行政领导者违反其法定的义务所引起的必须承担的法律后果。 19.人事行政:人事行政是指国家的人事机构为实现行政目标和社会目标，通过各种人事管理手段对公共行政人员所进行的制度化和法治化管理。20.人力资源:人力资源是指在一定范围内能够作为生产性要素投入社会经济活动的全部劳动人口的总和。它可分为现实的人力资源和潜在的人力资源两部分。 21.程序性决策:也叫常规性决策，是指决策者对所要决策的问题有法可依，有章可循，有先例可参考的结构性较强，重复性的日常事务所进行的决策。 22.非程序性决策:也叫非常规性决策，是指决策者对所要决策的问题无法可依，无章可循，无先例可供参考的决策，是非重复性的、非结构性的决策。 23.危机决策:是指领导者在自然或人为的突发性事件发生后，迅速启动各种突发事件应急机制，大胆预测，做出决定的过程。 24.行政决策参与:是指行政领导者个人或集体在行政决策时，专家学者、社会团体、公民等对决策提出意见或建议的活动。 25.行政执行:行政执行是行政机关及行政人员依法实施行政决策，以实现预期行政目标和社会目标的活动的总和。 26.行政控制:行政控制指行政领导者运用一定的控制手段，按照目标规范衡量行政决策的执行情况，及时纠正和调节执行中的偏差，以确保实现行政目标的活动。27.行政协调:行政协调是指调整行政系统内各机构之间、人员之间、行政运行各环节之间的关系，以及行政系统与行政环境之间的关系，以提高行政效能，实现行政目标的行为。 28.法制监督:法制监督，又称对行政的监督，是指有权国家机关对行政机关及其工作人员是否合法正确地行使职权所进行的监督与控制。 29.舆论监督:舆论监督是指通过在公共论坛的言论空 间中所抒发的舆论力量对政府机构和政府官员滥用权力等不当行为的监督与制约。 30.行政立法:行政立法一般是指立法机关通过法定形 式将某些立法权授予行政机关，行政机关得依据授权法(含宪法)创制行政法规和规章的行为。 31.行政法规:行政法规是指国务院根据宪法和法律，按照法定程序制定的有关行使行政权力，履行行政职责的规范性文件的总称。 32.标杆管理: 标杆管理是指公共组织通过瞄准竞争的 高目标,不断超越自己,超越标杆,追求卓越,成为强中之 强组织创新和流程再造的过程. 33.政府全面质量管理:政府全面质量管理是一种全员 参与的、以各种科学方法改进公共组织的管理与服务的，对公共组织提供的公共物品和公共服务进行全面管理，以获得顾客满意为目标的管理方法、管理理念和制度。 34.行政效率:行政效率是指公共组织和行政工作人员 从事公共行政管理工作所投入的各种资源与所取得的成果和效益之间的比例关系。 35.行政改革:行政改革是指政府为了适应社会环境，或者高效公平地处理社会公共事务，调整内部体制和组织结构，重新进行权力配置，并调整政府与社会之间关系的过程。 36.政府再造:政府再造是指对公共体制和公共组织绩 效根本性的转型，大幅度提高组织效能、效率、适应性以及创新的能力，并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型过程。

电大专科《公共行政学》名词解释简答题题库及答案(试卷号：2202)

电大专科《公共行政学》名词解释简答题题库及答案（试卷号：2202） 盗传必究 一、名词解释 1．政府职能：是指政府在国家和社会中所扮演的角色以及所应起的作用。 2．行政区划体制：是指根据一定的原则将全国领土划分为若干部分和若干层次的管理区域，并设置相应的行政机关的组织体制。 3．完整制：又叫一元统属制，是指公共组织的同一层级或同一组织内部的各个部门，完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。 4．行政效率：是指公共组织和行政工作人员从事公共行政管理工作所投入的各种资源与所取得的成果和效益之间的比例关系。 5．市场失效：是指因为市场局限性和缺陷所导致资源配置的低效率或无效率，并且不能解决外部性问题以及社会公平问题。 6．行政体制：指政府系统内部行政权力的划分、政府机构的设置以及运行等各种关系和制度的总和。 7．程序性决策：也叫常规性决策，是指决策者对所要决策的问题有法可依，有章可循，有先例可参考的结构性较强，重复性的日常事务所进行的决策。 8．行政法规：是指国务院根据宪法和法律，按照法定程序制定的有关行使行政权力，履行行政职责的规范性文件的总称。 9. 管理幅度：是指领导机关或领导者直接领导下属的部门或人员的数额。 10．行政决策参与：是指行政领导者个人或集体在行政决策时，专家学者、社会团体、公民等对决策提出意见或建议的活动。 11．电子政府：是指在政府内部采用电子化和自动化技术的基础上，利用现代信息技术和网络技术，建立起网络化的政府信息系统，并利用这个系统为政府机构、社会组织和公民提供方便、高效的政府服务和政务信息。 12. 公共行政学：是研究公共组织依法处理政务的有效性、公平性、民主性的规律的交叉性与综合性学科。 13. 行政领导责任：是指行政领导者违反其法定的义务所引起的必须承担的法律后果。 14. 风险型决策：是指决策者对决策对象的自然状态和客观条件比较清楚，也有比较明确的决策目标，但是实现决策目标结果必须冒一定风险。 15. 事前监督：是指在某种公共行政管理活动开展之前，监督部门围绕公共行政管理主体的行政行为进行的监督检查。 16. 政府再造：是指对公共体制和公共组织绩效根本性的转型，大幅度提高组织效能、效率、适应性以及创新的能力，并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型

What is linguistic

What is linguistics? I think that what should we realize is the fundamental things of linguistics about is that language is more than just a big bound of words. It’s easy to think that what are we doing, what are we speaking, is producing words that we learn when we were kids and now we know them, and now we string them together and found some sort of ways. Maybe there were some wrong ways to string them together, you learn at school. But basically what we doing is using big bound of words. Now if you really think about it. Words alone can’t be even most of what using a language in terms of what we are interested about using languages. And that’s because you think about it. Imagine if you in heeled a Russian dictionary, that’s say that you for all the attendance purpose knew every single word in the language. If you think about it really, even then you would be unable to have any kind of meaning for conversation of language. And 3 and 4 years olds would like be the years hard of you and that’s because obviously, there is also an issue how you put the words together in order to convey meaning. There’s also a said say, if a language is more than just a big bucket of words, then as Saussure’s grammar’s concern, it’s the matter of setting whether the words fit into one of a part of speech. So the idea is that when you got these parts of speech, then

公共行政学名词解释

1.委员会制 委员会制是指在公共组织中，由两个人以上掌握决策权和指挥权，按照多数原则进行决策的公共组织类型。 2.层级制 层级制又分级制，是指公共组织在纵向上按照等级划分为不同的上下节制的层级组织结构，不同等级的职能目标和工作性质相同，但管理范围和管理权限却随着等级降低而逐渐变小的组织类型。 3.机能制 机能制又称职能制，是指公共组织在横向上按照不同职能目标划分为不同职能部门的组织类型。 4.战略管理 公共组织的战略管理是指对公共组织在一定时期的全局的、长远的发展方向、目标、任务和政策，以及资源调配做出的决策和管理艺术。 5.政府再造的含义 政府再造是指对公共体制和公共组织绩效根本性的转型，大幅度提高组织效能、效率、适应性以及创新的能力，并通过改革组织目标、组织激励、责任机制、权力结构以及组织文化等来完成这种转型过程。 政府再造就是用企业化体制取代官僚体制，即创造具有创新习惯和持续改进质量能力的公共组织和公共体制，而不必靠外力驱使。 企业家政府是政府再造的重要内容。企业家政府是指具有企业家精神的行政管理者，用企业的管理方式，以低成本高产出为目标，敢于冒风险、敢于创新、敢于打破僵化官僚体制，取得高绩效的政府。

企业家政府重视政府的成本效益，重视创新与改革，强调利用市场机制和竞争，强调对执行者授权，主张顾客导向，主张放松规制。 6.目标管理的涵义和特点 目标管理是以目标为导向，以人为中心，以成果为标准，而使组织和个人取得最佳业绩的现代管理方法。目标管理的特点是以人、工作和成果为中心的现代管理方法。 7.行政规章的含义 行政规章是指特定的行政机关根据法律和法规，按照法定程序制定的具有普遍约束力的规范性文件的总称。行政规章简称规章。 8.事前监督； 事前监督是指在某种公共行政管理活动开展之前，监督部门围绕公共行政管理主体的行政行为进行的监督检查。 9.行政评估的含义 行政评估是指对行政执行活动的进展情况和效果进行评价和总结，包括行政执行过程评估和行政执行效果评估两个方面。一般意义上所说的行政评估主要是指行政执行效果评估。 10.行政领导权力的概念 行政领导权力是指行政领导者在行政管理活动中，利用其合法地位以不同的激励方式和制约方式，引导下属同心协力达成行政目标的影响力。 11.完整制 完整制又叫一元统属制，是指公共组织的同一层级或同一组织内部的各个部门，完全接受一个公共组织或同一位行政首长的领导、指挥和监督的组织类型。

Cognitive Linguistic

Linguistic Coursework ---Cognitive Linguistics

Linguistic course/work -----Cognitive Linguistics Introduction Cognitive Linguistics is a new approach to linguistics which appeared in the late 1980s and it has grown rapidly at home and abroad, and has gradually turned into the major school of linguistics. To cognitive linguists, language not only enables communication, but also reflects mankind’s conceptual world. In other words, linguistic categories not only enable us to communicate, but also impose a certain way of understanding of the world. It integrates the research ways of language typology and functional linguistics, depicting and elaborating the constitution of human language.Cognitive Linguistics presents a forum for high-quality linguistic research on topics which investigate the interaction between language and cognition. Compared with linguistic structuralism, which sparked off substitution drills, and speech act theory, which initiated a complete reorganization of teaching strategies, the impact of cognitive linguistics is much less revolutionary. Yet the influence of cognitive linguistics may prove very valuable, because it lends theoretical support to a number of accepted teaching approaches in the fields of both vocabulary and grammar. The most influential linguists focusing centrally on cognitive principles and organization were Wallace Chafe, Charles Fillmore, George Lakoff, Ronald Langacker, and Leonard Talmy etc. Masterpieces including: Handbook of Pragmatics、What Categories Reveal about the Mind、Metaphors we Live by、Foundations of Cognitive Grammar、An introduction to Cognitive Linguistics etc. At present, the study of cognitive linguistics is very active in Europe and the United States .Cognitive linguistics in the two research center in the United States has formed two different schools: Berkeley School( Lakoff、Fillmore、Kay、Sweetser) and San Diego School(Langacker、Fauconnier). The Foundations of Cognitive Grammar was written by Langacker, father of Cognitive Linguistics. This book introduces a new and fundamentally different conception of language structure and linguistic investigation.

公共行政学作业3答案

公共行政学作业3答案 一、名词解释 1、行政监察管辖：行政监察管辖，是指对某个监督对象确定由哪一级或者哪一个行政监察机关实施监督和哪一级或者哪一个行政监察机关对哪些特定监督事项有权进行管辖的法律制度。 2、招标性采购：是指通过招标的方式，邀请所有的或一定范围的潜在的供应商参加投标，采购主体通过某种事先确定并公布的标准从所有投标商中评选出中标供应商，并与之签订合同的一种采购方式。 3、标杆管理：是指一个组织瞄准一个比其绩效更高的组织进行比较，以便取得更好的绩效。 4、行政诉讼：所谓行政诉讼，就是公民或法人对行政机关或行政工作人员就违法行政行为向司法机关提起诉讼。俗称“民告官”。 二、单项选择题 1．整个行政执行过程中最具实质意义的、最为关键的阶段是( C )。 A．协调阶段 B．总结阶段 C．实施阶段 D．准备阶段 2．对具有公务员身份的中国共产党党员的案件，需要给予处分的，由( D )给予处分。

A．检察机关 B．行政监察机关 C．党的纪律检查机关 D．党的纪律检查机关和行政监察机关 3．国家预算中占主导地位的是( A )。 A．中央预算 B．县级预算 C．省级预算 D．市级预算 4．根据《立法法》，行政法规和规章应当在公布后的( B )天内报有关部门备案。 A．15 B．30 C．45 D．60 5．批准是一种约束力较强的( A )监督方式。其内容包括：要求监督对象报送审批材料、审查和批准(含不批准)三个基本步骤。 A．事先 B．事中 C．事后 D．全面 6．从20世纪( C )年代开始，西方发达国家相继开始进行行政改革，然后许多发展中国家因为实行市场化也进行不同程度的行政改革。 A．50 B．60 C．70 D．80 7．为了解决在实施决策的过程中出现的而一时又难以查清原因的问题的决策方案，称为( D )。 A．积极方案 B．追踪方案 C．应变方案 D．临时方案 8．行政决策体制的核心( D )。

语言学论文Linguistic and Language Learning

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