Evaluation of lexical methods for detecting relationships between concepts from multiple on

Evaluation of Lexical Methods for Detecting Relationships Between Concepts from

Multiple Ontologies

Helen L. Johnson, K. Bretonnel Cohen, William A. Baumgartner Jr., Zhiyong Lu, Michael Bada, Todd Kester, Hyunmin Kim, and Lawrence Hunter

Pacific Symposium on Biocomputing 11:28-39(2006)

EV ALUATION OF LEXICAL METHODS FOR DETECTING RELATIONSHIPS BETWEEN CONCEPTS FROM

MULTIPLE ONTOLOGIES?

HELEN L.JOHNSON?,K.BRETONNEL COHEN,WILLIAM A. BAUMGARTNER JR.,ZHIYONG LU,MICHAEL BADA,TODD KESTER, HYUNMIN KIM,AND LAWRENCE HUNTER

Center for Computational Pharmacology

University of Colorado School of Medicine

We used exact term matching,stemming,and inclusion of synonyms,implemented via the Lucene information retrieval library,to discover relationships between the Gene Ontology and three other OBO ontologies:ChEBI,Cell Type,and BRENDA Tissue.Proposed relationships were evaluated by domain experts.We discovered 91,385relationships between the ontologies.Various methods had a wide range of correctness.Based on these results,we recommend careful evaluation of all matching strategies before use,including exact string matching.The full set of relationships is available at https://www.wendangku.net/doc/5e17067433.html,/dependencies.

1.Introduction

Lexical analysis of an ontology is a powerful tool for suggesting relationships between concepts within the ontology[25,28,29,30,32]or among multiple ontologies[7,6].However,there are many possible text types to match between(e.g.term names,synonyms,and de?nitions)and variations on matching techniques(e.g.stemming,case normalization,etc.),and there is no reason to expect similar,and equally valid,results for all of them. Most importantly,the mere existence of a match does not prove a valid relationship between concepts.In this paper,we systematically evaluate three text matching techniques in two text types,and use domain experts to evaluate the correctness of the resulting matches.

Recently,[7]demonstrated the utility of an external,publicly-available resource for?nding within-ontology relationships.They hypothesized that ?This work is supported by NLM grant R01-LM00811to Lawrence Hunter.

?Authors Johnson and Cohen contributed equally to the work reported here.

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two GO terms that share a relationship to a single ChEBI term are related to each other.They detected771,302within-GO relationships by?nding sets of GO terms and synonyms that lexically matched a ChEBI term or its synonyms.They noted that55%of all GO terms contained26%of all ChEBI terms,totalling20,497GO-ChEBI relationships.Implicit in their work is the assumption that relationships found between GO and ChEBI terms are valid and meaningful.More recently[6],they have proposed ex-tending this technique to detect relationships between all OBO ontologies.

Various ontology working groups have become interested in integrating external ontologies into their own,and have pointed out some of the ob-stacles to doing this[15].In this paper,we show that a variety of publicly available resources can be exploited for large-scale,automated suggestion of between-ontology relationships.We de?ne a relationship as any direct or indirect association between two ontological concepts.

In this paper,we evaluate the following hypotheses:

(1)Valid relationships exist between concepts from GO and from other

OBO ontologies.

(2)Gene Ontology de?nitions are a fruitful resource for discovering re-

lationships between concepts in ontologies.

(3)Language processing techniques for discovering relationships have

quanti?able and variable rates of correctness.

A novel aspect of this paper as compared to[6]and[7]is that we make use of the text of GO de?nitions.There is some history for using de?nitions in language processing applications,particularly in the word sense disambiguation task[21].More recently,[23]shows the value of Gene Ontology de?nitions for predicting GeneRIFs.We also evaluate simple linguistic processing techniques for term detection and normalization.The ?ndings may be useful for semi-automatically linking ontologies,whether to support reasoning tasks or annotation,and also for detecting terms from ontologies in natural language texts.

1.1.Context and motivation

This research falls into the general category of semantic integration(SI). Semantic integration is a currently active topic of research in the general computer science,arti?cial intelligence,Internet,and data mining commu-nities[26].It has crucial roles to play in areas as diverse as interoperability in Semantic Web Services[8],coreference resolution in free text[22],schema

3 and data matching in databases[12],and communication between intelli-gent agents and resources[13].There is much related work in the ontology community,e.g.[27]and[24]among many others.Within the biomedi-cal ontology literature,closely related work includes the description-logic-based GONG project[33],in which GO metabolism terms were linked to biological-substance terms from MeSH using lexical tools and term syn-onyms of UMLS.[2],[19],[5]used various non-lexical techniques to?nd relations within GO.

Mapping vs.alignment of ontologies:Integration of multiple,in-dependently produced ontologies is an important task in molecular biology. One well-studied aspect of this task is mapping,the identi?cation of equiva-lent concepts in multiple ontologies[15,20,30].This work has shown some of the di?culties of textual analysis of biological ontologies.For example, [30]points out that biological terminologies pose di?culties for standard normalization procedures,since they often contain alphanumeric modi?ers. Other problems include synonymy and morphological variation[20].

Ontology alignment is the task of making overlapping concepts among multiple ontologies compatible.Although mapping may be a part of align-ment,the alignment task requires?nding meaningful relationships between non-identical concepts.The identi?cation of such relationships may also be valuable within an ontology,e.g.in order to improve compositionality [25,32,28,29]or in de?ning and populating novel relationships.The work reported here is relevant both to the mapping and to the alignment task.

Natural language processing:The relevance of locating concepts from an ontology in free text is clear from the inclusion of this task in recent“bake-o?”competitions in the NLP community.The overall low performance on these tasks[4,16,9]demonstrates their di?culty.The work in this paper can be thought of as a step towards recognizing OBO concepts in free text:GO terms and de?nitions are themselves a type of semi-structured natural language,?tting the sublanguage model but having enough complexity to be a challenge,while not being as unstructured as the language of scienti?c abstracts.

2.Methods

Materials:We retrieved the current versions of the GO[1,15],ChEBI [11],Cell Type[3],and BRENDA Tissue[31]ontologies from SourceForge. (In the remainder of this paper,when we say“(other)OBO ontologies,”we mean the ontologies other than GO.)We chose these three other ontologies

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because we expected high degrees of subject-matter overlap between them

and GO,and because they are in relatively advanced stages of development.

Table1.Materials:ontologies,data?les,and revisions.

Ontology terms synonyms avg.syn./term data?le revision date

Gene Ontology19,5088,202.42gene ontology.obo09:06:200517:10 ChEBI11,54919,295 1.67chebi.obo25:05:200510:54 Cell Type748215.29cell.obo24:05:200517:10 BRENDA2,2221,208.54BrendaTissue.txt10:5:200513:49:02

Finding relationships:We used Lucene[14]to search for the OBO concepts in GO.Lucene is a Java information retrieval library a.We mod-

eled the GO concepts as documents to be retrieved,and the other OBO

concepts as search engine queries.We indexed the GO concepts,placing

the terms and de?nitions in distinct?elds,which allowed us to search them

separately.We constructed Lucene phrasal queries from the other OBO

concepts.This meant that for searches on multi-word OBO concepts,word

order could not vary and no words could intrude.Synonym queries were

done by constructing phrasal queries for each synonym,and then grouping

the phrasal queries with Boolean OR.Both indexing and searching require

a Lucene class called an analyzer.We used the WhiteSpace and Porter-

Stemmer analyzers.Lucene gave us an e?cient and robust framework for

carrying out searches and for manipulating their results.

Evaluation:We drew a random sample from the relationships pro-posed by each technique for each ontology,for a total of2,389relationships.

The sample is unevenly distributed across various categories of ontologies,

linguistic manipulations,and GO terms vs.GO de?nitions,but covers all

combinations of those categories.These2,389relationships were manually

examined by domain experts.One domain expert(DE1)has considerable

experience in ontologies,biology,and structural chemistry.The other do-

main expert(DE2)is a bioinformatics doctoral candidate with experience

with GO and with protein function and subcellular localization.The ex-

perts were presented with(1)the ID and name of a concept from an OBO

ontology,and(2)the ID,name,and de?nition of some concept from the GO.

In addition,the experts had access to the de?nitions of the OBO concept,

as well as any other helpful information found in the ontologies themselves.

a Previous applications of Lucene to text processing in the biomedical domain are re-

ported in[10]and[18].

5 They were instructed to evaluate the output with the following question in mind:Is this OBO term the concept that is being referred to in this GO term/de?nition?They were permitted to classify all relationships as either true positive or false positive.We calculated correctness as the number of true positive relationships divided by the number of proposed relationships (similar to precision or speci?city).All relationships are available for public inspection at https://www.wendangku.net/doc/5e17067433.html,/dependencies.

Inter-annotator agreement(IAA):DE1evaluated the majority of the output.A sample of400proposed relationships was also evaluated by DE2.Initial IAA between the two was93.5%(374/400).After dispute resolution,the consensus IAA was98.2%(393/400).For the remaining seven cases,DE1had the deciding vote.

Linguistic manipulations:We queried by exact match to the OBO concept name.We also queried using synonyms of OBO concepts.Since all work in this area has observed moderate di?erences in concept name real-ization,such as pluralization,we also implemented the standard linguistic manipulations of stemming and stop word removal[17].We evaluated the correctness of the resulting searches individually.

What we counted:For each ontology,we give data on the following:

?Relationships found by matches between the OBO ontology and GO

terms(T)

?Relationships found by matches between the OBO ontology and GO

de?nitions(D)

?The union of T and D(T∪D)b

?The intersection of T and D(T∩D)c

?The relative complement of T and D(T-D)d

?The relative complement of D and T(D-T)e

Gain,the magnitude of the increase in the number of relationships de-tected by examining de?nitions,rather than just terms,is the relative com-plement of D and T divided by the union of T and D((D-T)/(T∪D)).

In addition,for each ontology,we calculated the analogous set relations b T∪D gives the number of relationships that are found in terms or de?nitions.Some of its relationships are revealed by both.It equals(T∩D)+D-T+T-D.

c T∩D is the number of relationships that are foun

d in both terms and de?nitions.

d T-D is th

e number o

f relationships that can be found in terms,but cannot be found by examinin

g de?nitions.It equals T-(T∩D).

e D-T is the number o

f relationships that can be found in de?nitions,but cannot be found by examinin

g terms.It equals D-(T∩D).

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for the various language processing techniques.This allows us to quantify the yield and the correctness of the various techniques with respect to the three ontologies.

For the Cell Type ontology,we?ltered out all matches to the terms cell (CL:0000000,3215matches),cell by organism(CL:0000004,96matches), and cell by function(CL:0000144,10matches),since we realized early on that they were either content-free or incorrect.

3.Results

Finding relationships between ontologies:Our initial hypothesis was that there are relationships between GO and the various OBO ontologies. Table2summarizes the number of matches between GO and the three other ontologies and the average correctness calculated by manually examining a subset of the matches.Searching GO terms and de?nitions for terms from the other ontologies resulted in a total of91,385proposed relationships. The majority of these links(73,002)are between GO and ChEBI.The average correctness across the three ontologies is80.62%.This is generally consistent with the precision reported for the mapping task by[30](range from.36for BLAST to.94for exact match)and[20](range from.25for Chimaera to1.0for PROMPT).These data are consistent with the initial hypothesis,validating the goal expressed in[6],and gives an idea of the size of the set of potential relationships.

Table2.Counts and correctness of proposed relationships

between ontologies.Numbers in parentheses are the correct

and total manually evaluated pairs.

Ontology Relationships to GO Avg.Correctness

ChEBI7300284.2%(977/1161)

Cell Type196192.99%(584/628)

BRENDA1646960.83%(365/600)

TOTAL9138580.62%(1926/2389) Correctness and Error Analysis:To assess the correctness of the matches,a random set of2,389was manually examined by domain experts. All results are given in Table3.Note that although correctness is generally high,some combinations of ontology and linguistic technique had quite low correctness.This has important consequences for more ambitious e?orts to

7 detect relationships across all OBO ontologies,such as proposed by[6]:we cannot use any technique,including exact matching,without assessing its correctness for a particular pair of data sources.

Exact matching was the most accurate type of search,ranging from 76%to100%correct.This is consistent with results reported for the mapping task.One source of false positives for exact matching was pol-ysemy,or words with multiple meanings.For example,the word group (CHEBI:24433)also has a General English meaning,and often appeared with that sense in GO de?nitions.Similarly,the BRENDA term joint (BTO:0001686),which refers to an anatomical joint,appears as an adjective meaning combined in GO concepts.We found examples of false positives related to non-General-English,domain-speci?c terms as well,e.g.retic-ulum(BTO:0000347)incorrectly matching the de?nition of GO:0006614. Incorporating OBO term synonymy resulted in slightly lower correctness, ranging from42%to94%,with an average of67.4%(397/589).Finally,the stemming/stop-word-removal searches show the lowest correctness,ranging from7%to92%.

Table3.Correctness Rates(correct/evaluated)

Ontology Exact Synonyms Stemming

ChEBI

GO Term99.5%(199/200)42.0%(42/100)73.0%(73/100)

GO Def97.8%(451/461)69.0%(138/200)74.0%(74/100)

Cell Type

GO Term100%(200/200)94%(44/47)76%(41/54)

GO Def98.7%(231/234)50%(21/42)92%(47/51)

BRENDA

GO Term76.0%(76/100)83.0%(83/100)7.0%(7/100)

GO Def93.0%(93/100)69.0%(69/100)15.0%(15/100) GO terms versus GO de?nitions:A novel hypothesis of this paper is that GO de?nitions are a fruitful resource for discovering relationships between GO and other ontologies.Table4addresses this hypothesis for BRENDA,and the corresponding data for the other ontologies are given on the website(https://www.wendangku.net/doc/5e17067433.html,/dependencies).Searching for relationships in the GO de?nitions in addition to the GO terms had a large impact on the quantity of relationships found between ontologies.The table presents

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the number of links found in GO terms and in GO de?nitions,as well as the union,intersection and relative complements of these sets.The number of links found only in GO terms is given by the relative complement of terms and de?nitions(T-D),listed in the?fth column of the table.The number of links found only in GO de?nitions is given by the relative complement of de?nitions and terms(D-T),the sixth column.The?nal column in Table 4describes the gain from searching in GO de?nitions for relationships.It is calculated by dividing the D-T by the union of D and T.For instance, in the?rst row of Table4,which displays the number of links found in the GO using an exact BRENDA term search,a gain of49.59%means that just under half of these between-ontology links could be found only by searching the GO de?nition.Note that for all ontologies and for all search strategies,the number of relationships is higher when de?nitions are considered.The gain is never lower than24.3%(270additional matches for exact matching of Cell Type concepts),and it is generally higher than50% (43,146additional matches just for the case of allowing stemming matches for ChEBI concepts).The correctness(see Table3)of relationships detected by matches to de?nitions is comparable to the correctness of relationships detected by matches to terms.

Table4.Relationships in GO terms vs.GO defs for BRENDA Ontology T D T∪D T∩D T-D D-T Gain

Exact1465244729061006459144149.59%

Exact+synonyms1875309336861282593181149.13%

Stemmed3892154091572235793131183075.24%

3.1.Linguistic techniques in relationship searches

Using synonyms:Results for including the synonyms associated with BRENDA terms in the search string are given in Table5;corresponding data for the other ontologies is on the website.E is exact match,Syn adds synonyms for the OBO concept,and the other columns are the union, intersection,and relative complements.Adding synonyms increased the yield of relationships by an average of36%(23,300/64,987)over using only the exact OBO term query.The set E should be a proper subset of Syn,and the relative complement of E and Syn is the empty set.The yield of using OBO synonyms ranged from9%(85/925)to40.69%(8669/21300),and was

9 generally quite similar for GO terms and for GO de?nitions.Synonyms allowed us to detect some relationships that could not have been found by any other technique,e.g.relating adipose(BTO:0000441)to larval fat body development(GO:0007504).Correctness for synonymy-based matches was sometimes low,ranging from42to94%(see Table3)—not surprising in the face of the history of query expansion attempts in information retrieval.

https://www.wendangku.net/doc/5e17067433.html,ing BRENDA synonyms

GO E Syn E∪Syn E∩Syn E-Syn Syn-E Gain

T1465187518751465041021.9%

D2447309330932447064620.9%

T∪D2906368636862906078021.2% Stemming GO concepts and OBO terms:Results for stemming and stop word removal(labelled Stem)for BRENDA are given in Table6; data for the other ontologies is on the website.Stemming garnered the greatest increase of proposed relationships,with an average gain of78% (146,777/188,464).However,this increase comes at a price,with a lower average correctness rate of51%(257/505).Note that the correctness of matching BRENDA to GO terms or de?nitions by stemming is extremely low(7-15%).Again,searching for stemmed OBO terms also returned the subset of relationships that the exact term searches returned,and E-Stem is the empty set.

Stemming allowed pluralized forms of the same term to be matched.It also picked up other morphological variation in terms,e.g.matching neuron (CL:0000540)to neuronal in the de?nition of syntrophin(GO:0016013).In a random sample of97relationships matched by stemming across the three ontologies,57%was due to pluralization,11%to adjectival derivation,and 32%to other morphological variation.

Table6.Stemming and stop word removal:BRENDA GO E Stem E∪Stem E∩Stem E-Stem Stem-E Gain

T14653892389214650242762.36%

D24471540915409244701296284.12%

T∪D29061572215722290601281681.52%

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4.Discussion and conclusions

Our results are consistent with the hypotheses that there are many valid relationships between GO and other OBO ontologies,and that in addition to GO terms,GO de?nitions are an important source for detecting them.

Implications for ontology mapping:One implication of this study comes from the observation that correctness is almost never100%:even exact string matches do not guarantee a valid match.Ontologists attempt-ing to carry out the goal stated in[6]should not ignore these?ndings.The results on BRENDA are especially cautionary.

In contrast to work on the mapping task done by the ontology commu-nity,the evaluation of work such as ours and Burgun and Bodenreider’s has been hampered by the lack of a curated gold standard.One impor-tant product of the work reported in this paper is a data set of2,389 GO/other-OBO concept pairs that has been examined by at least one do-main expert.This data set includes1,926true positive relationships and 463known unrelated(i.e.,the false positive)pairs.It is publicly available at https://www.wendangku.net/doc/5e17067433.html,/dependencies,and will allow future researchers in this area to do principled automatic evaluations.

Furthermore,the set of known unrelated pairs can be used in future e?orts to?lter out terms that are known to produce high numbers of irrele-vant,incorrect,or simply unrevealing matches.We suspect that a relatively small set of OBO terms contributed many of the errors,and that correctness can be improved by?ltering them.This analysis continues.

Implications for ontology enrichment:One limitation for the ap-plication of these relationships to ontology enrichment(the addition of re-lationships among existing terms)is the fact that most of the relation-ships that we detect are indirect.For example,our techniques relate T cell (CL:0000084)to both T cell proliferation(GO:0042098)and regulation of T cell proliferation(GO:0042129),but an ontologist would likely prefer to ?nd only the direct relationship from T cell to T cell proliferation.Another limitation for ontology enrichment is that our methods do not automati-cally di?erentiate between relation types(see e.g.[28]).Future work should attempt to di?erentiate between direct and indirect relationships,and to characterize the nature of the relations between concepts.

Implications for language processing:This study provides cau-tionary data on the limits of various techniques,even exact string matches. The data provides a list of terms that are likely to produce false-positive matches under conditions of exact match and speci?c linguistic manipula-

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tions;these lists can be used to?lter results from any language processing system that seeks to recognize concepts from the ChEBI,Cell Type,and BRENDA ontologies.It also points us towards techniques that might al-low us to predict which terms are likely to produce high rates of false positive matches,such as ones at high positions in an ontology(e.g.cell (CL:0000000))and ones that are isomorphic with General English words (e.g.groups(CHEBI:24433)).Additionally,it highlights the importance of building biomedical-domain-speci?c preprocessing tools,such as stemmers.

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ology to migrate the Gene Ontology to a description logic environment using DAML+OIL.PSB2003pp.624-635.

的、地、得的用法和区别

“的、地、得”的用法和区别 导入（进入美妙的世界啦~） “的、地、得”口诀儿歌 的地得，不一样，用法分别记心上， 左边白，右边勺，名词跟在后面跑。 美丽的花儿绽笑脸，青青的草儿弯下腰， 清清的河水向东流，蓝蓝的天上白云飘， 暖暖的风儿轻轻吹，绿绿的树叶把头摇， 小小的鱼儿水中游，红红的太阳当空照， 左边土，右边也，地字站在动词前， 认真地做操不马虎，专心地上课不大意， 大声地朗读不害羞，从容地走路不着急， 痛快地玩耍来放松，用心地思考解难题， 勤奋地学习要积极，辛勤地劳动花力气， 左边两人双人得，形容词前要用得， 兔子兔子跑得快，乌龟乌龟爬得慢， 青青竹子长得快，参天大树长得慢， 清晨锻炼起得早，加班加点睡得晚， 欢乐时光过得快，考试题目出得难。 知识典例（注意咯，下面可是黄金部分！） 的、地、得 “的”、“地”、“得”的用法区别本是中小学语文教学中最基本的常识，但在使用中也最容易发生混淆，再加上一段时间里，中学课本中曾将这三个词的用法统一为“的”，因此造成了很多人对它们的用法含混不清进而乱用一通的现象。

一、“的、地、得”的基本概念 1、“的、地、得”的相同之处。 “的、地、得”是现代汉语中高频度使用的三个结构助词，都起着连接作用；它们在普通话中都读轻声“de”，没有语音上的区别。 2、“的、地、得”的不同之处。 吕叔湘、朱德熙所著《语法修辞讲话》认为“的”兼职过多，负担过重，而力主“的、地、得”严格分工。50 年代以来的诸多现代汉语论著和教材，一般也持这一主张。从书面语中的使用情况看，“的”与“地”、“得”的分工日趋明确，特别是在逻辑性很强的论述性、说明性语言中，如法律条款、学术论著、外文译著、教科书等，更是将“的”与“地”、“得”分用。 “的、地、得”在普通话里都读轻声“de”，但在书面语中有必要写成三个不同的字：在定语后面写作“的”，在状语后面写作“地”，在补语前写作“得”。这样做的好处，就是可使书面语言精确化。 二、“的、地、得”的用法 1、的——定语的标记，一般用在主语和宾语的前面。“的”前面的词语一般用来修饰、限制“的”后面的事物，说明“的”后面的事物怎么样。结构形式一般为：形容词、名词（代词）+的+名词。如： ①颐和园(名词)的湖光山色（主语）美不胜收。 ②她是一位性格开朗的女子(名词，宾语)。 2、地——状语的标记，一般用在谓语（动词、形容词）前面。“地”前面的词语一般用来形容“地”后面的动作，说明“地”后面的动作怎么样。结构方式一般为：形容词（副词）+地+动词（形容词）。如： ③她愉快(形容词)地接受(动词，谓语)了这件礼物。 ④天渐渐(时间副词)地冷(形容词，谓语)起来。 3、得——补语的标记，一般用在谓语后面。“得”后面的词语一般用来补充说明“得”前面的动作怎么样，结构形式一般为：动词（形容词）+得+副词。如： ⑤他们玩(动词，谓语)得真痛快(补语)。

英语介词用法大全

英语介词用法大全 TTA standardization office【TTA 5AB- TTAK 08- TTA 2C】

介词（The Preposition）又叫做前置词，通常置于名词之前。它是一种虚词，不需要重读，在句中不单独作任何句子成分，只表示其后的名词或相当于名词的词语与其他句子成分的关系。中国学生在使用英语进行书面或口头表达时，往往会出现遗漏介词或误用介词的错误，因此各类考试语法的结构部分均有这方面的测试内容。 1. 介词的种类 英语中最常用的介词，按照不同的分类标准可分为以下几类： (1). 简单介词、复合介词和短语介词 ①.简单介词是指单一介词。如： at , in ,of ,by , about , for, from , except , since, near, with 等。②. 复合介词是指由两个简单介词组成的介词。如： Inside, outside , onto, into , throughout, without , as to as for , unpon, except for 等。 ③. 短语介词是指由短语构成的介词。如： In front of , by means o f, on behalf of, in spite of , by way of , in favor of , in regard to 等。 (2). 按词义分类 {1} 表地点（包括动向）的介词。如： About ,above, across, after, along , among, around , at, before, behind, below, beneath, beside, between , beyond ,by, down, from, in, into , near, off, on, over, through, throught, to, towards,, under, up, unpon, with, within , without 等。 {2} 表时间的介词。如： About, after, around , as , at, before , behind , between , by, during, for, from, in, into, of, on, over, past, since, through, throughout, till(until) , to, towards , within 等。 {3} 表除去的介词。如： beside , but, except等。 {4} 表比较的介词。如： As, like, above, over等。 {5} 表反对的介词。如： againt ,with 等。 {6} 表原因、目的的介词。如： for, with, from 等。 {7} 表结果的介词。如： to, with , without 等。 {8} 表手段、方式的介词。如： by, in ,with 等。 {9} 表所属的介词。如： of , with 等。 {10} 表条件的介词。如：

of与for的用法以及区别

of与for的用法以及区别 for 表原因、目的 of 表从属关系 介词of的用法 （1）所有关系 this is a picture of a classroom （2）部分关系 a piece of paper a cup of tea a glass of water a bottle of milk what kind of football，American of soccer？ （3）描写关系 a man of thirty 三十岁的人 a man of shanghai 上海人 （4）承受动作 the exploitation of man by man．人对人的剥削。 （5）同位关系 It was a cold spring morning in the city of London in England． （6）关于，对于 What do you think of Chinese food？ 你觉得中国食品怎么样？ 介词 for 的用法小结 1. 表示“当作、作为”。如: I like some bread and milk for breakfast. 我喜欢把面包和牛奶作为早餐。What will we have for supper? 我们晚餐吃什么?

2. 表示理由或原因,意为“因为、由于”。如: Thank you for helping me with my English. 谢谢你帮我学习英语。 Thank you for your last letter. 谢谢你上次的来信。 Thank you for teaching us so well. 感谢你如此尽心地教我们。 3. 表示动作的对象或接受者,意为“给……”、“对…… (而言)”。如: Let me pick it up for you. 让我为你捡起来。 Watching TV too much is bad for your health. 看电视太多有害于你的健康。 4. 表示时间、距离,意为“计、达”。如: I usually do the running for an hour in the morning. 我早晨通常跑步一小时。We will stay there for two days. 我们将在那里逗留两天。 5. 表示去向、目的,意为“向、往、取、买”等。如: let’s go for a walk. 我们出去散步吧。 I came here for my schoolbag.我来这儿取书包。 I paid twenty yuan for the dictionary. 我花了20元买这本词典。 6. 表示所属关系或用途,意为“为、适于……的”。如: It’s time for school. 到上学的时间了。 Here is a letter for you. 这儿有你的一封信。 7. 表示“支持、赞成”。如: Are you for this plan or against it? 你是支持还是反对这个计划? 8. 用于一些固定搭配中。如: Who are you waiting for? 你在等谁? For example, Mr Green is a kind teacher. 比如,格林先生是一位心地善良的老师。

to与for的用法和区别

to与for的用法和区别 一般情况下, to后面常接对象; for后面表示原因与目的为多。 Thank you for helping me. Thanks to all of you. to sb.表示对某人有直接影响比如，食物对某人好或者不好就用to; for表示从意义、价值等间接角度来说，例如对某人而言是重要的，就用for. for和to这两个介词，意义丰富，用法复杂。这里仅就它们主要用法进行比较。 1. 表示各种“目的” 1. What do you study English for? 你为什么要学英语？ 2. She went to france for holiday. 她到法国度假去了。 3. These books are written for pupils. 这些书是为学生些的。 4. hope for the best, prepare for the worst. 作最好的打算，作最坏的准备。 2．对于 1．She has a liking for painting. 她爱好绘画。 2．She had a natural gift for teaching. 她对教学有天赋/ 3．表示赞成同情，用for不用to. 1. Are you for the idea or against it? 你是支持还是反对这个想法？ 2. He expresses sympathy for the common people.. 他表现了对普通老百姓的同情。 3. I felt deeply sorry for my friend who was very ill. 4 for表示因为，由于（常有较活译法） 1 Thank you for coming. 谢谢你来。 2. France is famous for its wines. 法国因酒而出名。 5．当事人对某事的主观看法，对于（某人），对…来说（多和形容词连用）用介词to，不用for.. He said that money was not important to him. 他说钱对他并不重要。 To her it was rather unusual. 对她来说这是相当不寻常的。 They are cruel to animals. 他们对动物很残忍。 6．for和fit, good, bad, useful, suitable 等形容词连用，表示适宜，适合。 Some training will make them fit for the job. 经过一段训练，他们会胜任这项工作的。 Exercises are good for health. 锻炼有益于健康。 Smoking and drinking are bad for health. 抽烟喝酒对健康有害。 You are not suited for the kind of work you are doing. 7. for表示不定式逻辑上的主语，可以用在主语、表语、状语、定语中。 1．It would be best for you to write to him. 2．The simple thing is for him to resign at once. 3．There was nowhere else for me to go. 4．He opened a door and stood aside for her to pass.

“的、地、得”的用法和区别

的、地、得的用法和区别 的、地、得的用法和区别老班教育 一、的、地、得的基本概念 1、的、地、得的相同之处。 的、地、得是现代汉语中高频度使用的三个结构助词，都起着连接作用；它们在普通话中都读轻声de，没有语音上的区别。 2、的、地、得的不同之处。 吕叔湘、朱德熙所著《语法修辞讲话》认为的兼职过多，负担过重，而力主的、地、得严格分工。50 年代以来的诸多现代汉语论著和教材，一般也持这一主张。从书面语中的使用情况看，的与地、得的分工日趋明确，特别是在逻辑性很强的论述性、说明性语言中，如法律条款、学术论著、外文译著、教科书等，更是将的与地、得分用。 的、地、得在普通话里都读轻声de，但在书面语中有必要写成三个不同的字：在定语后面写作的，在状语后面写作地，在补语前写作得。这样做的好处，就是可使书面语言精确化。 二、的、地、得的用法 （一）、用法 1、的——定语的标记，一般用在主语和宾语的前面。的前面的词语一般用来修饰、限制的后面的事物，说明的后面的事物怎么样。 结构形式一般为：形容词、名词（代词）+的+名词。如： 颐和园(名词)的湖光山色（主语）美不胜收。 她是一位性格开朗的女子(名词，宾语)。 2、地——状语的标记，一般用在谓语（动词、形容词）前面。地前面的词语一般用来形容地后面的动作，说明地后面的动作怎么样。 结构方式一般为：形容词（副词）+地+动词（形容词）。如： 她愉快(形容词)地接受(动词，谓语)了这件礼物。 天渐渐(时间副词)地冷(形容词，谓语)起来。 3、得——补语的标记，一般用在谓语后面。得后面的词语一般用来补充说明得前面的动作怎么样。 结构形式一般为：动词（形容词）+得+副词。如： 他们玩(动词，谓语)得真痛快(补语)。 她红(形容词，谓语)得发紫(补语)。 （二）、例说 的，一般用在名词和形容词的后面，用在描述或限制人物、事物时，形容的词语与被形容的词语之间，表示一种描述的结果。如：漂亮的衣服、辽阔的土地、高大的山脉。结构一般为名词（代词或形容词）+的+名词。如，我的书、你的衣服、他的孩子，美丽的景色、动听的歌曲、灿烂的笑容。 地，用法简单些，用在描述或限制一种运动性质、状态时，形容的词语与被形容的词语之间。结构通常是形容词+地+动词。前面的词语一般用来形容后面的动作。一般地的后面只跟动词。比如高兴地跳、兴奋地叫喊、温和地说、飞快地跑；匆匆地离开；慢慢地移动......... 得，用在说明动作的情况或结果的程度时，说明的词语与被说明的词语之间，后面的词语一般用来补充和说明前面的情况。比如。跑得飞快、跳得很高、显得高雅、显得很壮、馋得直流口水、跑得快、飞得高、走得慢、红得很……得通常用在动词和形容词（动词之间）。

with的用法大全

with的用法大全----四级专项训练with结构是许多英语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述，以帮助同学们掌握这一重要的语法知识。 一、 with结构的构成 它是由介词with或without+复合结构构成，复合结构作介词with或without的复合宾语，复合宾语中第一部分宾语由名词或代词充当，第二部分补足语由形容词、副词、介词短语、动词不定式或分词充当，分词可以是现在分词，也可以是过去分词。With结构构成方式如下： 1. with或without-名词/代词+形容词; 2. with或without-名词/代词+副词; 3. with或without-名词/代词+介词短语; 4. with或without-名词/代词+动词不定式; 5. with或without-名词/代词+分词。 下面分别举例：

1、 She came into the room，with her nose red because of cold.(with+名词+形容词，作伴随状语) 2、 With the meal over ， we all went home.(with+名词+副词，作时间状语) 3、The master was walking up and down with the ruler under his arm。(with+名词+介词短语，作伴随状语。) The teacher entered the classroom with a book in his hand. 4、He lay in the dark empty house，with not a man ，woman or child to say he was kind to me.(with+名词+不定式，作伴随状语) He could not finish it without me to help him.(without+代词 +不定式，作条件状语) 5、She fell asleep with the light burning.(with+名词+现在分词，作伴随状语) 6、Without anything left in the cupboard， she went out to get something to eat.(without+代词+过去分词，作为原因状语) 二、with结构的用法 在句子中with结构多数充当状语，表示行为方式，伴随情况、时间、原因或条件(详见上述例句)。

常用介词用法(for to with of)

For的用法 1. 表示“当作、作为”。如: I like some bread and milk for breakfast. 我喜欢把面包和牛奶作为早餐。 What will we have for supper? 我们晚餐吃什么? 2. 表示理由或原因,意为“因为、由于”。如: Thank you for helping me with my English. 谢谢你帮我学习英语。 3. 表示动作的对象或接受者,意为“给……”、“对…… (而言)”。如: Let me pick it up for you. 让我为你捡起来。 Watching TV too much is bad for your health. 看电视太多有害于你的健康。 4. 表示时间、距离,意为“计、达”。如: I usually do the running for an hour in the morning. 我早晨通常跑步一小时。 We will stay there for two days. 我们将在那里逗留两天。 5. 表示去向、目的,意为“向、往、取、买”等。如: Let’s go for a walk. 我们出去散步吧。 I came here for my schoolbag.我来这儿取书包。 I paid twenty yuan for the dictionary. 我花了20元买这本词典。 6. 表示所属关系或用途,意为“为、适于……的”。如: It’s time for school. 到上学的时间了。 Here is a letter for you. 这儿有你的一封信。 7. 表示“支持、赞成”。如: Are you for this plan or against it? 你是支持还是反对这个计划? 8. 用于一些固定搭配中。如: Who are you waiting for? 你在等谁? For example, Mr Green is a kind teacher. 比如,格林先生是一位心地善良的老师。 尽管for 的用法较多，但记住常用的几个就可以了。 to的用法: 一：表示相对，针对 be strange (common, new, familiar, peculiar) to This injection will make you immune to infection. 二：表示对比，比较 1：以-ior结尾的形容词，后接介词to表示比较，如：superior ,inferior,prior,senior,junior 2: 一些本身就含有比较或比拟意思的形容词，如equal，similar，equivalent，analogous A is similar to B in many ways.

(完整版)介词for用法归纳

介词for用法归纳 用法1：(表目的)为了。如： They went out for a walk. 他们出去散步了。 What did you do that for? 你干吗这样做？ That’s what we’re here for. 这正是我们来的目的。 What’s she gone for this time? 她这次去干什么去了？ He was waiting for the bus. 他在等公共汽车。 【用法说明】在通常情况下，英语不用for doing sth 来表示目的。如： 他去那儿看他叔叔。 误：He went there for seeing his uncle. 正：He went there to see his uncle. 但是，若一个动名词已名词化，则可与for 连用表目的。如： He went there for swimming. 他去那儿游泳。(swimming 已名词化) 注意：若不是表目的，而是表原因、用途等，则其后可接动名词。(见下面的有关用法) 用法2：(表利益)为，为了。如： What can I do for you? 你想要我什么？ We study hard for our motherland. 我们为祖国努力学习。 Would you please carry this for me? 请你替我提这个东西好吗？ Do more exercise for the good of your health. 为了健康你要多运动。 【用法说明】(1) 有些后接双宾语的动词(如buy, choose, cook, fetch, find, get, order, prepare, sing, spare 等)，当双宾语易位时，通常用for 来引出间接宾语，表示间接宾语为受益者。如： She made her daughter a dress. / She made a dress for her daughter. 她为她女儿做了件连衣裙。 He cooked us some potatoes. / He cooked some potatoes for us. 他为我们煮了些土豆。 注意，类似下面这样的句子必须用for： He bought a new chair for the office. 他为办公室买了张新办公椅。 (2) 注意不要按汉语字面意思，在一些及物动词后误加介词for： 他们决定在电视上为他们的新产品打广告。 误：They decided to advertise for their new product on TV. 正：They decided to advertise their new product on TV. 注：advertise 可用作及物或不及物动词，但含义不同：advertise sth＝为卖出某物而打广告；advertise for sth＝为寻找某物而打广告。如：advertise for a job=登广告求职。由于受汉语“为”的影响，而此处误加了介词for。类似地，汉语中的“为人民服务”，说成英语是serve the people，而不是serve for the people，“为某人的死报仇”，说成英语是avenge sb’s death，而不是avenge for sb’s death，等等。用法3：(表用途)用于，用来。如： Knives are used for cutting things. 小刀是用来切东西的。 This knife is for cutting bread. 这把小刀是用于切面包的。 It’s a machine for slicing bread. 这是切面包的机器。 The doctor gave her some medicine for her cold. 医生给了她一些感冒药。 用法4：为得到，为拿到，为取得。如： He went home for his book. 他回家拿书。 He went to his friend for advice. 他去向朋友请教。 She often asked her parents for money. 她经常向父母要钱。

的地得的用法和区分

《“的、地、得”的用法》语文微课教案 一、教学背景 在语言文字规范化大背景下，帮助学生解决应用“的地得”的疑惑与困难。 二、设计思路 针对学生对于“的地得”的误用与忽视展开教学，规范结构助词“的地得”的使用。按照“问题的提出、问题的分析、问题的解决”的思路展开教学，总结归纳优化的方式方法。 三、教学目标 1、知道“怎么样的什么、怎么样地干什么、干得怎么样”三种固定搭配。 2、掌握“的、地、得”的区别与联系。 3、运用小儿歌“动前土、名前白、行动后面双人来”的口诀帮助正确使用“的、地、得”。 四、教学重难点 1、知道“的、地、得”的区别。 2、在实际情境中正确运用“的、地、得”。 五、教学时间 8分钟微课堂 六、教学适用对象 义务教育九年制内的学生 七、教学准备

多媒体课件、录屏软件 八、教学设计与过程 开场白： 同学们好！今天我们一起来学习“的、地、得”的正确用法。首先我们来了解一下它们的区别。 1、相同之处：原来它们都是念轻声“de”，都是结构助词，起连接作用。 2、不同之处：在书面语中要写成三个不同的字，而且它们的搭配及用法也各不相同。 （1）怎么样的什么 （2）怎样样地干什么 （3）干得怎么样 下面我们就来学习一下它们的正确用法。 白勺“的”的结构是用“形容词或名词或代词+的+名词”来表示，而我们最常见，用得最多的还是“形容词+的+名词”的结构。 而土也“地”的用法可以用“形容词+地+动词”的结构来表示。 双人“得”是用“动词+得+形容词”的结构来表示 3、练习巩固 （1）形近区分 静静（的）河面静静（地）写字欢乐（的）山谷

欢乐（地）歌唱满意（地）点头满意（的）作品 （2）类别区分 1）跑（得）飞快飞快（地）跑 2）愉快（的）旅行旅行（得）愉快 3）强烈（的）渴望强烈（地）渴望 （3）综合杂糅 小雏鹰飞到大树的上方，高兴地喊起来：“我真的会飞啦！而且飞（得）很高呢！” 小结：能填对这个句子的你肯定就已经学会它们的用法了！ 4、特殊情况 质疑：假如遇到特殊情况怎么办呢？ 我从书包里拿出书交给她们，她们高兴得．围着我跳起舞来。（出自二年级上册《日记两则》） （1）质疑：为什么这里要使用“得”呢？ （2）释疑：原来这里强调的是心情，动词在后，形容词在前，相当于后置，“得”修饰“跳舞”而非“围”。现在你明白了吧？ 5、小结归纳： 怎么样，你们学会了吗？为了让同学们能够更快的记住它们的用法，老师送给大家一首口诀来帮助你们熟记三个“的”的正确使用方法：动前土、名前白、行动后面双人来。

with用法归纳

with用法归纳 （1）“用……”表示使用工具，手段等。例如： ①We can walk with our legs and feet. 我们用腿脚行走。 ②He writes with a pencil. 他用铅笔写。 （2）“和……在一起”，表示伴随。例如： ①Can you go to a movie with me? 你能和我一起去看电影'>电影吗？ ②He often goes to the library with Jenny. 他常和詹妮一起去图书馆。 （3）“与……”。例如： I’d like to have a talk with you. 我很想和你说句话。 （4）“关于，对于”，表示一种关系或适应范围。例如： What’s wrong with your watch? 你的手表怎么了？ （5）“带有，具有”。例如： ①He’s a tall kid with short hair. 他是个长着一头短发的高个子小孩。 ②They have no money with them. 他们没带钱。 （6）“在……方面”。例如： Kate helps me with my English. 凯特帮我学英语。 （7）“随着，与……同时”。例如： With these words, he left the room. 说完这些话，他离开了房间。 [解题过程] with结构也称为with复合结构。是由with+复合宾语组成。常在句中做状语，表示谓语动作发生的伴随情况、时间、原因、方式等。其构成有下列几种情形： 1.with+名词(或代词)+现在分词 此时，现在分词和前面的名词或代词是逻辑上的主谓关系。 例如:1)With prices going up so fast, we can't afford luxuries. 由于物价上涨很快，我们买不起高档商品。（原因状语） 2)With the crowds cheering, they drove to the palace. 在人群的欢呼声中，他们驱车来到皇宫。（伴随情况） 2.with+名词(或代词)+过去分词 此时，过去分词和前面的名词或代词是逻辑上的动宾关系。

of和for的用法

of 1....的,属于 One of the legs of the table is broken. 桌子的一条腿坏了。 Mr.Brown is a friend of mine. 布朗先生是我的朋友。 2.用...做成的;由...制成 The house is of stone. 这房子是石建的。 3.含有...的;装有...的 4....之中的;...的成员 Of all the students in this class,Tom is the best. 在这个班级中,汤姆是最优秀的。 5.(表示同位) He came to New York at the age of ten. 他在十岁时来到纽约。 6.(表示宾格关系) He gave a lecture on the use of solar energy. 他就太阳能的利用作了一场讲演。 7.(表示主格关系) We waited for the arrival of the next bus. 我们等待下一班汽车的到来。

I have the complete works of Shakespeare. 我有莎士比亚全集。 8.来自...的;出自 He was a graduate of the University of Hawaii. 他是夏威夷大学的毕业生。 9.因为 Her son died of hepatitis. 她儿子因患肝炎而死。 10.在...方面 My aunt is hard of hearing. 我姑妈耳朵有点聋。 11.【美】(时间)在...之前 12.(表示具有某种性质) It is a matter of importance. 这是一件重要的事。 For 1.为,为了 They fought for national independence. 他们为民族独立而战。 This letter is for you. 这是你的信。

介词with的用法大全

介词with的用法大全 With是个介词，基本的意思是“用”，但它也可以协助构成一个极为多采多姿的句型，在句子中起两种作用；副词与形容词。 with在下列结构中起副词作用： 1.“with+宾语+现在分词或短语”，如： (1) This article deals with common social ills, with particular attention being paid to vandalism. 2.“with+宾语+过去分词或短语”，如： (2) With different techniques used, different results can be obtained. (3) The TV mechanic entered the factory with tools carried in both hands. 3.“with+宾语+形容词或短语”，如： (4) With so much water vapour present in the room, some iron-made utensils have become rusty easily. (5) Every night, Helen sleeps with all the windows open. 4.“with+宾语＋介词短语”，如： (6) With the school badge on his shirt, he looks all the more serious. (7) With the security guard near the gate no bad character could do any thing illegal. 5.“with+宾语＋副词虚词”，如： (8) You cannot leave the machine there with electric power on. (9) How can you lock the door with your guests in? 上面五种“with”结构的副词功能，相当普遍，尤其是在科技英语中。 接着谈“with”结构的形容词功能，有下列五种： 一、“with+宾语+现在分词或短语”，如： (10) The body with a constant force acting on it. moves at constant pace. (11) Can you see the huge box with a long handle attaching to it ? 二、“with+宾语+过去分词或短语” (12) Throw away the container with its cover sealed. (13) Atoms with the outer layer filled with electrons do not form compounds. 三、“with+宾语+形容词或短语”，如： (14) Put the documents in the filing container with all the drawers open.

的地得的用法教案

“的、地、得”的用法教案 教学目标： 1.能通过看视频知道“的、地、得”的用法区别。 2.能在小组合作中正确掌握“的、地、得”的用法。 3.能正确熟练地运用“的、地、得”。 教学重点：通过看视频知道“的、地、得”的用法区别。 教学难点：正确熟练地运用“的、地、得”。 教学过程： 一、导入（板书课题：“的、地、得”的用法“的、地、得”） 这三个字认识吧！虽然它们都有一个相同的读音de，但用法却不一样，可不能把他们用错了。究竟他们的用法有什么不同，我们来听听他们的故事吧！ 二、看微视频，学习“的、地、得”的用法区别。 三、小结： 1.孩子们，刚才看了视频知道他们是谁吗？（白勺的，土也地，双人得。） （1）白勺的是个杂货铺老板，她的店里都有什么？（彩色的毛巾美味的汉堡结实的帐篷舒适的儿童车捕捉风的网会唱歌的小树开个没完的花朵优美动听的歌曲飘来飘去的云……）还可能有什么？ 你们一定会发现，白勺的的用法有什么特点？（后面是名词。）板书：名词 （2）土也地是个运动男孩，他喜欢？（悠闲地散步欢快地跳舞兴奋地跳跃开心地捕蝴蝶看图书踢球骑自行洗澡吃冰淇淋……）他还可能喜欢干什么呢？你发现了吗？土也地的用法特点？（后面是动词。）板书：动词 （3）双人得呢？她是个总喜欢评价别人的小妹妹。（球踢得真棒舞跳得精彩长得好高呀……） 她可能还怎么评价别人？（歌唱得动听饭吃得很饱人长得漂亮）你们会发现，双人得的前面通常都是——动词。板书：动词 2.小结：所以，他们的用法也很简单，区别就在这里。 （白勺的用在名词前面；土也地用在动词前面；双人得用在动词后面。）你明白了吗？ 四、我来考考你们，看哪一组完成得又对又快！ 1.菜鸟级练习 2.老鸟级练习 3.大虾级练习 五、总结

初中 英语 介词“with”的用法

介词“with”的用法 1、同, 与, 和, 跟 talk with a friend 与朋友谈话 learn farming with an old peasant 跟老农学习种田 fight [quarrel, argue] with sb. 跟某人打架 [争吵, 辩论] [说明表示动作的词, 表示伴随]随着, 和...同时 change with the temperature 随着温度而变化 increase with years 逐年增加 be up with the dawn 黎明即起 W-these words he left the room. 他说完这些话便离开了房间。2 2、表示使用的工具, 手段 defend the motherland with one s life 用生命保卫祖国 dig with a pick 用镐挖掘 cut meat with a knife 用刀割肉3

3、说明名词, 表示事物的附属部分或所具有的性质]具有; 带有; 加上; 包括...在内 tea with sugar 加糖的茶水 a country with a long history 历史悠久的国家4 4、表示一致]在...一边, 与...一致; 拥护, 有利于 vote with sb. 投票赞成某人 with的复合结构作独立主格,表示伴随情况时，既可用分词的独立结构，也可用with的复合结构： with +名词(代词)+现在分词/过去分词/形容词/副词/不定式/介词短语。例如： He stood there, his hand raised. = He stood there, with his hand raise.他举手着站在那儿。 典型例题 The murderer was brought in, with his hands ___ behind his back A. being tied B. having tied C. to be tied D. tied 答案D. with +名词(代词)+分词+介词短语结构。当分词表示伴随状况时，其主语常常用

for和of的用法

for的用法： 1. 表示“当作、作为”。如: I like some bread and milk for breakfast. 我喜欢把面包和牛奶作为早餐。 What will we have for supper? 我们晚餐吃什么? 2. 表示理由或原因,意为“因为、由于”。如: Thank you for helping me with my English. 谢谢你帮我学习英语。 Thank you for your last letter. 谢谢你上次的来信。 Thank you for teaching us so well. 感谢你如此尽心地教我们。 3. 表示动作的对象或接受者,意为“给……”、“对…… (而言)”。如: Let me pick it up for you. 让我为你捡起来。 Watching TV too much is bad for your health. 看电视太多有害于你的健康。 4. 表示时间、距离,意为“计、达”。如:

I usually do the running for an hour in the morning. 我早晨通常跑步一小时。 We will stay there for two days. 我们将在那里逗留两天。 5. 表示去向、目的,意为“向、往、取、买”等。如: Let’s go for a walk. 我们出去散步吧。 I came here for my schoolbag.我来这儿取书包。 I paid twenty yuan for the dictionary. 我花了20元买这本词典。 6. 表示所属关系或用途,意为“为、适于……的”。如: It’s time for school. 到上学的时间了。 Here is a letter for you. 这儿有你的一封信。 7. 表示“支持、赞成”。如: Are you for this plan or against it? 你是支持还是反对这个计划? 8. 用于一些固定搭配中。如:

双宾语 to for的用法

1.两者都可以引出间接宾语，但要根据不同的动词分别选用介词to 或for：(1) 在give, pass, hand, lend, send, tell, bring, show, pay, read, return, write, offer, teach, throw 等之后接介词to。 如： 请把那本字典递给我。 正：Please hand me that dictionary. 正：Please hand that dictionary to me. 她去年教我们的音乐。 正：She taught us music last year. 正：She taught music to us last year. (2) 在buy, make, get, order, cook, sing, fetch, play, find, paint, choose,prepare, spare 等之后用介词for 。如： 他为我们唱了首英语歌。 正：He sang us an English song. 正：He sang an English song for us. 请帮我把钥匙找到。 正：Please find me the keys. 正：Please find the keys for me. 能耽搁你几分钟吗(即你能为我抽出几分钟吗)? 正：Can you spare me a few minutes? 正：Can you spare a few minutes for me? 注：有的动词由于搭配和含义的不同，用介词to 或for 都是可能的。如：do sb a favour＝do a favour for sb 帮某人的忙 do sb harm＝do harm to sb 对某人有害