These authors contributed equally to this work

Preparation of name and address data for record linkage using hidden Markov models

Tim Churches 1 § *, Peter Christen 2 *, Kim Lim 1, Justin Xi Zhu 2

1Centre for Epidemiology and Research, Public Health Division, New South Wales Department of Health, Locked Mail Bag 961, North Sydney 2059, Australia

2Department of Computer Science, Australian National University, Canberra, Australia

§Corresponding author

* These authors contributed equally to this work

Email addresses:

Tim Churches (tchur@https://www.wendangku.net/doc/f117233491.html,.au)

Peter Christen (peter.christen@https://www.wendangku.net/doc/f117233491.html,.au)

Kim Lim (klim@https://www.wendangku.net/doc/f117233491.html,.au)

Justin Xi Zhu (u3167614@https://www.wendangku.net/doc/f117233491.html,.au)

Abstract

Background

Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as “standardisation”. This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs).

Methods

HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems.

Results

Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed.

Conclusion

Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve.

Background

Introduction

Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections [1]. The entity is often a person, in which case record linkage may be used for tasks such as building a longitudinal health record [2], or relating genotypic information to phenotypic information [3][4]. In other settings, the aim may be to link several sources of information about the same event, such as police, accident investigation, ambulance, emergency department and hospital admitted patient records which all relate to the same motor vehicle accident [5]. Record linkage (originally known as “medical record linkage”) is now widely used in research - in October 2002, a search of the biomedical literature via PubMed for “medical record linkage” as a Medical Subject Heading returned over 1,300 references [6].

The process of record linkage is trivial where the records that relate to the same entity or event all share a common, unique key or identifier - an SQL “equi-join” operation, or its equivalent in other data management environments, can be used to link records. However, often there is no unique key which is shared by all the data collections which need to be linked, particularly when these data collections are administered by separate organisations, possibly operated for quite different purposes in disparate subject domains.

In these settings, more specialised record linkage techniques need to be used. These techniques can be broadly divided into two groups: deterministic, or rule-based techniques, and probabilistic techniques. A full description of these techniques is beyond the scope of this paper. A number of recent reviews of this topic are available [7][8]. However, all of these techniques rely on an element-wise comparison between pairs of records each comprising an ensemble of non-unique, partially identifying personal (or event) attributes. These attribute commonly include name, residential address, date of birth (or age at a particular date), sex (or gender), marital status, and country of birth.

For example, consider the fictitious personally-identified records in Table 1.

The evident variability in the formatting and encoding of these records is quite typical of data collections which have been assembled from multiple sources. This variability tends to frustrate naive attempts at automated linkage of these records. To a human, it is obvious that records 0 and 2 represent the same person. It is quite likely, but not certain, that records 1 and 3 also represent the same person. The status of record 4 with respect to records 0 and 2 is far less clear – could this be Gwendolynne's spouse, Evelyn, or is this Gwendolynne with her sex and age wrongly recorded?

Regardless of the method used to automate such decisions, it is clear that transformation of the source data into a normalised form is required before valid and reliable comparisons between pairs of records can be made. Such transformation and normalisation is usually called “data standardisation” in the medical record literature, and “data cleaning” or “data scrubbing” in the computer science literature. We will

refer to the process as “standardisation” henceforth, which should not be confused with the epidemiological technique of “age-sex standardisation” of incidence or prevalence rates.

Standardisation of scalar attributes such as height or weight involves transformation of all quantities into a common set of units, such as from British imperial to SI units. Categorical attributes such as sex are usually transformed to a common set of representations through simple look-up tables or mapping of various encodings – for example, both “Female” and “2” might be mapped to “F” and “male and “1” to “M” in order to provide a consistent encoding of the sex attribute for each record. Such transformations do not present a major challenge. However, standardisation of attributes which are recorded in highly variable formats, such as names or residential addresses, is far less straightforward, and it is with this task that this paper is concerned.

This standardisation task can itself be decomposed into two steps: segmentation of the data into specific, atomic data elements; and the transformation of these atomic elements into their canonical forms. In some cases, a third step, the imputation of missing or blank data items, and a fourth step, the enhancement of the original data with known alternatives, may also be required.

Some examples of the first two steps will make this clearer. Table 2 shows the segmented and transformed forms of the name, address and sex attributes of the illustrative records introduced in Table 1.

Once the original data have been segmented and standardised in this way, further enhancement of the data is possible. For example, missing postal codes and territories can be automatically filled in from reference tables, and alternate, canonical forms of names can be added where informal, anglicised or other known variations are found, such as “Angie” (Angela, Angelique) or “Lyn” (Evelyn, Lyndon).

Related work

The terms data cleaning (or data cleansing), data standardisation, data scrubbing, data pre-processing and ETL (extraction, transformation and loading) are used synonymously to refer to the general tasks of transforming source data into clean and consistent sets of records suitable for loading into a data warehouse, or for linking with other data sets. A number of commercial software products are available which address this task, and a complete review is beyond the scope of this paper - a summary can be found in [9]. Name and address standardisation is also closely related to the more general problem of extracting structured data, such as bibliographic references, from unstructured or variably structured texts, such as scientific papers.

The most common approach for name and address standardisation is the manual specification of parsing and transformation rules. A well-known example of this approach in biomedical research is AutoStan, which was the companion product to the widely-used AutoMatch probabilistic record linkage software [10].

AutoStan first parses the input string into individual words, and each word is then mapped to a token of a particular class. The choice of class is determined by the presence of that word in user-supplied, class-specific lexicons (look-up tables), or by

the type of characters found in the word (such as all numeric, alphanumeric or alphabetical). An ordered set of regular expression-like patterns is then evaluated against this sequence of class tokens. If a class token sequence matches a pattern, a corresponding set of actions for that pattern is performed. These actions might include dynamically changing the class of one or more tokens, removing particular tokens from the class token sequence, or modifying the value of the word associated with that token. The remaining patterns are then evaluated against the now modified class token sequence – in other words, the pattern matcher is re-entrant, and the actions associated with more than one pattern may act on any given token sequence. When the evolving token sequence for a particular record has been tested against all the available patterns, the words in the input string are output into specific fields corresponding to the final class of the tokens associated with each word.

Such approaches necessarily require both an initial and an ongoing investment in rule programming by skilled staff. In order to mitigate this requirement for skilled programming, some investigators have recently described systems which automatically induce rules for information extraction from unstructured text. These include Whisk [11], Nodose [12] and Rapier [13].

Probabilistic methods are an alternative to these deterministic approaches. Statistical models, particularly hidden Markov models, have been used extensively in the computer science fields of speech recognition and natural language processing to help solve problems such as word-sense disambiguation and part-of-speech tagging[14]. More recently, hidden Markov and related models have been applied to the problem of extracting structured information from unstructured text [15][16][17][18][19][20].

This paper describes the use of lexicon-based tokenisation with hidden Markov models for name and address standardisation, implemented as part of a free, open source [21] record linkage package known as Febrl (Freely extensible biomedical record linkage) [22]. Febrl is written in the free, open source, object-oriented programming language Python [23]. Other aspects of the Febrl project will be described in subsequent papers.

Cleaning and tokenisation

The following steps are used to clean and tokenise the raw name or address input string. Firstly, all letters are converted to lower case. Various sub-strings in the input string, such as “ c/- ” or “ c.of ” are then converted to their canonical form, such as “care_of ”, based on a user-specified and domain-specific substitution table. Similarly, punctuation marks are regularised – for example, all forms of quotation marks are converted to single character (a vertical bar). The cleaned string is then split into a vector of words, using white space and punctuation marks as delimiters.

Using look-up tables and some hard-coded rules, the words in this input vector are assigned one or more tokens, to which we will refer as “observation symbols” henceforth. The hard-coded rules include, for example, the assignment of the AN (alphanumeric) observation symbol to all words which are a mixture of alphabetic and numeric characters. However, the majority of observation symbols are assigned by searching for words, or sub-sequences of words, in various look-up tables. A list of observation symbols currently supported by the Febrl package is given in Table 3.

For example, one of the look-up tables may be a list of locality names. If a word (or contiguous group of words) is found in the locality table, then the LN (locality name) observation symbol is assigned to that word (or group). This look-up uses a “greedy” matching algorithm. For example, the wayfare name look-up table might contain a record for “macquarie”, the locality qualifier look-up table might contain a record for “fields” and the locality name look-up table might contain a record for “macquarie fields”. If the first word in the input vector is “macquarie” and the second word is “fields”, these first two words will be coalesced (into “macquarie_fields”) and will be assigned an LN (locality name) observation symbol, rather than the first word being assigned a WN (wayfare name) symbol and the second field an LQ (locality qualifier) symbol.

Such lexicon-based tokenisation allows readily-available lists of postal codes, locality names, states and territories, as typically published by postal authorities or government gazetteers, to be leveraged to provide the probabilistic model used in the next stage with the maximum number of “hints” about the semantic content of the input string. Note that theses probabilistic models are able to cope with situations in which incorrect observation symbols are assigned to particular words in the input string – the only requirement is that the symbols are assigned in a consistent fashion. For example, the input string “17 macquarie fields road, northmead nsw 2345” might be tokenised as “NU-LN-WT-LN-TR-PC” (number-locality name-wayfare type-locality name-territory-postal code). The first LN symbol is wrong in this context because “macquarie fields” is a wayfare name , not a locality name.

Hidden Markov models

A hidden Markov model (HMM) is a probabilistic finite state machine comprising a set of observable facts or observation symbols (also known as output symbols), a finite set of discrete, unobserved (hidden) states, a matrix of transition probabilities between those hidden states, and a matrix of the probabilities with which each hidden state emits an observation symbol [24].

In the case of residential addresses, we posit that hidden states exist for each segment of an address, such as the wayfare (street) number, the wayfare name, the wayfare type, the locality and so on. We treat the tokenised input address as an ordered sequence of observation symbols, and we assume that each observation symbol has been emitted by one of the hidden address states. Training data are representative samples of the input records which have been tokenised into sequences of observation symbols as described above, and then tagged with the hidden state which the trainer thought was most likely to have been responsible for emitting each observation symbol. Maximum likelihood estimates (MLEs) are derived for the HMM transition and observation probability matrices by accumulating frequency counts for each type of state transition and observation symbol from the training records. Because of the use of frequency-based MLEs, it is important that the records in the training data set are reasonably representative of the data sets to be standardised. However, as reported below, the HMMs appear to be quite robust with respect to the training set used and quite general with respect to the data sources with which they can be used. As a result, it is quite feasible to add training records which are archetypes of unusual name or address patterns, without compromising the performance of the HMMs on more typical source records.

The trained HMM can then be used to determine which sequence of hidden states was most likely to have emitted the observed sequence of symbols. In an ergodic (fully connected) HMM, in which each state can be reached from any other state, if there are N states and T observations symbols in a given sequence, then there are N T different paths through the model. Even with quite simple models and input sequences, it is computationally infeasible to evaluate the probability of every path to find the most likely one. Fortunately, the Viterbi algorithm [25] provides an efficient method for pruning the number of paths which need to be evaluated in order to find the most likely path through the model.

Once found, the most likely path through the HMM can then be used to associate each word in the original input string with a hidden state, and this information used to segment the input string into atomic data elements like those illustrated in Table 2. This approach can also be used with names or other variably-formatted text, using different sets of hidden states, observation symbols, transition and output matrices.

Figure 1 shows a simplified HMM for addresses with eight states. The start and end states are both virtual states as they do not emit any observation symbols. The probabilities of transition from one state to another are shown by the arrows (transitions with zero probabilities are omitted for the sake of clarity). The illustrative transition and emission probability matrices for this model are shown in Tables 4 and 5.

Notice that the probabilities in each row of the transition matrix and in each column of the emission matrix add up to one. Also notice that none of the probabilities in the emission matrix are zero. In practice, it is common for some combinations of state and observations symbol not to appear in the training data, resulting in a maximum likelihood estimate of zero for that element of the emission matrix. Such zero probabilities can cause problems when the model is presented with new data, so smoothing techniques are used to assign small probabilities (in this case 0.01) to all unencountered observation symbols for all states. Traditionally Laplace smoothing is used [26], but Borkar et al. have also described the use of absolute discounting as an alternative when there are a large number of distinct observation symbols [20]. The Febrl package offers both types of smoothing.

Now consider an example address: “17 Epping St Smithfield New South Wales 2987”. This would first be cleaned and tokenised as follows.

'2987'

]

'nsw',

'epping', 'street', 'smithfield',

[ '17',

'TR',

]

'LN',

'PC'

[ 'NU',

'LN',

'WT',

Note that Epping is a suburb of the city of Sydney in the state of New South Wales, Australia, hence the word “epping” in the input string is assigned an LN (locality name) observation symbol even though to a human observer it is clearly a wayfare name in this context. This does not matter because we are ultimately not interested in the types of the observed symbols but rather in the underlying hidden states which were most likely to have generated them.

Even in this very simple model there are 86 = 262,144 possible combinations of hidden states which could have generated this observed sequence of symbols - such as the following sequence of states (with the corresponding observation symbols in brackets):

Start -> Wayfare Name (NU) -> Locality Name (LN) -> Postal Code (WT) -> Territory (LN) -> Postal Code (TR) -> Territory (PC) -> End

Common sense tells us that this sequence of hidden states is a very unlikely explanation for the observed symbols. From our HMM, the probability of this sequence is indeed rather small (emission probabilities are underlined):

0.8 x 0.01 x 0.02 x 0.8 x 0.4 x 0.01 x 0.1 x 0.01 x 0.8 x 0.01 x 0.1 x 0.01 x 0.2 =

8.19 x 10-17

The following sequence of hidden states is a more plausible explanation for the observed symbols:

Start -> Wayfare Number (NU) -> Wayfare Name (LN) -> Wayfare Type (WT) -> Locality (LN) -> Territory (TR) -> Postal Code (PC) -> End

In fact, according to our simple HMM, this sequence has the greatest probability of all 262,144 possible combinations of hidden states and observation symbols and is therefore the most likely explanation for the input sequence of observation symbols:

0.9x 0.9 x 0.95 x 0.1 x 0.95 x 0.92 x 0.95 x 0.8 x 0.4 x 0.94 x 0.8 x 0.85 x 0.9 =

1.18 x 10-2

It is then a simple matter to use this information to segment the cleaned version of the input string into address elements and output them, as shown in Table 6.

Further details of the way in which HMMs are implemented in the Febrl package are available in the associated documentation [22].

Methods

We evaluated the performance of the approach described above with typical Australian residential address data using two data sources.

The first source was a set of approximately 1 million addresses taken from uncorrected electronic copies of death certificates as completed by medical practitioners and coroners in the state of New South Wales (NSW) in the years 1988 to 2002. The majority of these data were entered from hand-written death certificate forms. The information systems into which the data were entered underwent a number of changes during this period.

The second data set was a random sample of 1,000 records of residential addresses drawn from the NSW Inpatient Statistics Collection for the years 1993 to 2001 [27]. This collection contains abstracts for every admission to a public- or private-sector acute care hospital in NSW. Most of the data were extracted from a variety of computerised hospital information systems, with a small proportion entered from paper forms.

Accuracy measurements for name standardisation were conducted using a subset of the NSW Midwives Data Collection (MDC) [28]. This subset contained 962,776 records for women who had given birth in New South Wales, Australia, over a ten year period (1990-2000). Most of these data was entered from hand-written forms, although some of the data for the latter years were extracted directly from computerised obstetric information systems.

Access to these data sets for the purpose of this project was approved by the Australian National University Human Research Ethics Committee and by the relevant data custodians within the NSW Department of Health. The data sets used in this project were held on secure computing facilities at the Australian National University and the NSW Department of Health head offices. In order to minimise the invasion of privacy which is necessarily associated with almost all research use of identified data, the medical and health status details were removed from the files used in this project. Thus, for this project the investigators had access to files of names and addresses, but not to any of the medical or other details for the individuals identified in those files, other than the fact that they had died or had given birth.

Address Standardisation

Training of HMMs for residential address standardisation was undertaken in the following manner.

An initial “bootstrap” hidden Markov model (HMM) was trained using 100 randomly selected death certificate (DC) records. Annotating these records with state and observation symbol information took less than one person-hour. The resulting model was used to process 1,100 randomly chosen DC records. These records then became a second-stage training set, with each record already annotated with states and observation symbols derived from the initial “bootstrap” model. This annotation was manually checked and corrected where necessary, which took about 5 person-hours. An HMM derived from this second training set was then used to standardise 50,000 randomly chosen DC records, and records with unusual patterns of observation

symbols (with a frequency of six or less) were examined, corrected and added to the training set if the results produced by the second-stage HMM were incorrect. A new HMM was then derived from this augmented training set and the process repeated a further three times, resulting in the addition of approximately 250 “atypical” training records (bringing the total number of training records to 1,450). The HMM which emerged from this process, designated HMM1, was used to standardise 1,000 randomly chosen DC test records and the accuracy of the standardisation was assessed. Laplace smoothing used in this and all subsequent address standardisation evaluations. Approximately ten hour person-hours of training time was required to reach this point.

HMM1 was then used to standardise 1,000 randomly chosen Inpatient Statistics Collection (ISC) test records, and the accuracy assessed. In other words, an HMM trained using one data source (DC) was used to standardise addresses from a different data source (ISC) without any retraining of the HMM.

An additional 1,000 randomly chosen address training records derived from the Midwives Data Collection (MDC) were then added to the 1,450 training records described above, and this larger training set was used to derive HMM2. HMM2 was then used to re-standardise the same sets of randomly chosen test records described in the first and second steps above, and the results were assessed.

A further 60 training records, based on archetypes of those records which were incorrectly standardised in all of the preceding tests, were then added to the training set to produce HMM3. HMM3 was then used to re-standardise the same DC and ISC

test sets. Thus, HMM3 could be considered as an “overfitted” model for the particular records in the two test sets, although in practice researchers are likely to use such overfitting to maximise standardisation accuracy for the particular data sets used in their studies. The total training time for all address standardisation models was not more than 20 person hours.

Finally, by way of comparison, the same two 1,000 record test data sets were standardised using AutoStan in conjunction with a rule set which had been developed and refined by two of the investigators (TC and KL) over several years for use with ISC (but not DC) address data, representing a cumulative investment of at least several person-weeks of programming time.

Name Standardisation

To assess the accuracy of name standardisation, a subset of 10,000 records with non-empty name components was selected from the MDC data set (approximately a one per cent sample). This sample was split into ten test sets each containing 1,000 records. A ten-fold cross validation study was performed, with each of the folds having a training set of 9,000 records and the remaining 1,000 records being the test set. The training records were marked up with state and observation symbol information in about 10 person-hours using the bootstrapping method described above. HMMs were then trained without smoothing, and with Laplace and absolute discount smoothing, resulting in 30 different HMMs. We found that smoothing had a negligible effect on performance, and only the results from the unsmoothed HMMs are reported here.

The performance of HMMs for name standardisation was compared with a deterministic rule-based standardisation algorithm which is also implemented in the Febrl package - details of this algorithm can be found in the associated documentation [22].

Evaluation Criteria

For all tests, records were judged to be accurately standardised when all of the elements present in the input address string, with the exception of punctuation, were allocated to the correct output field, and the values in each output field were correctly transformed to their canonical form where required. Thus, a record was judged to have been incorrectly standardised if any element of the input string was not allocated to an output field, or if any element was allocated to the wrong output field. Due to resource constraints, the investigators were not blind to the nature of the standardisation process (HMM versus AutoStan) used. Exact binomial 95 per cent confidence limits for the proportion of correctly standardised records were calculated using the method given in [29].

In the records which were standardised incorrectly, not every data element was assigned to the wrong output field. For each of these address records, the proportions (and corresponding 95 per cent confidence limits) of data elements which were assigned to the wrong output field, or which were not assigned to an output field at all, were calculated. These quantities were not calculated for names due to the much simpler form of the name data.

Results

Addresses standardisation

Results are shown in Table 7.

The mean proportions of data items in each address which were assigned to the incorrect output field, or which were not assigned to any output field, are shown in Table 8.

Name standardisation

Results of the ten-fold cross-validation of name standardisation on 1,000 names of mothers are shown in Table 9.

The way常见用法

The way 的用法 Ⅰ常见用法： 1)the way+ that 2)the way + in which（最为正式的用法） 3)the way + 省略（最为自然的用法） 举例：I like the way in which he talks. I like the way that he talks. I like the way he talks. Ⅱ习惯用法： 在当代美国英语中，the way用作为副词的对格，“the way+ 从句”实际上相当于一个状语从句来修饰整个句子。 1)The way =as I am talking to you just the way I’d talk to my own child. He did not do it the way his friends did. Most fruits are naturally sweet and we can eat them just the way they are—all we have to do is to clean and peel them. 2）The way= according to the way/ judging from the way The way you answer the question, you are an excellent student. The way most people look at you, you’d think trash man is a monster. 3）The way =how/ how much No one can imagine the way he missed her. 4）The way =because

The way的用法及其含义(二)

The way的用法及其含义（二） 二、the way在句中的语法作用 the way在句中可以作主语、宾语或表语： 1.作主语 The way you are doing it is completely crazy.你这个干法简直发疯。 The way she puts on that accent really irritates me. 她故意操那种口音的样子实在令我恼火。The way she behaved towards him was utterly ruthless. 她对待他真是无情至极。 Words are important, but the way a person stands, folds his or her arms or moves his or her hands can also give us information about his or her feelings. 言语固然重要,但人的站姿,抱臂的方式和手势也回告诉我们他(她)的情感。 2.作宾语 I hate the way she stared at me.我讨厌她盯我看的样子。 We like the way that her hair hangs down.我们喜欢她的头发笔直地垂下来。 You could tell she was foreign by the way she was dressed. 从她的穿著就可以看出她是外国人。 She could not hide her amusement at the way he was dancing. 她见他跳舞的姿势，忍俊不禁。 3.作表语 This is the way the accident happened.这就是事故如何发生的。 Believe it or not, that's the way it is. 信不信由你, 反正事情就是这样。 That's the way I look at it, too. 我也是这么想。 That was the way minority nationalities were treated in old China. 那就是少数民族在旧中

this-that-these-those的用法及习题

语法强化与扩展： 指示代词：this, that, these, those 1. this和these指说话人较近的人或物，that和those指说话人较远的人或物. this和that指单数，be动词用单数形式is,同时后面的名词用单数形式。these和those表示复数，be 动词用复数形式are,同时后面的名词用复数形式。 2. 在回答主语this或that的一般疑问句或特殊疑问句时，在答语中用it代替句中this 或that; What i s t hi s/that这是/那是什么It' s a/an...它是???： Is this/that...这是/那是???吗Yes, it is.是的，它是。No, it isn' t?不，它不是。 3. 在回答主语these或those的一般疑问句或特殊疑问句时，在答语中用they代替句中these 或those; What are these/those它们是什么They are...它们是???； Are these/those...这些/那些是?吗Yes, they are.是的，它们是。No, they aren* t. 不，它们不是。4. 在介绍某人时，用this或that,而不用he或she:例如This is my mum. 5. 打电话时，说自己是谄用This is...，问别人是谄用Who* s that )7. --------- 1s that Mary* s school bag 一：填空 1. I like pan ts. pan ts are red.（这些） 2. I don' t Ii ke shoes?shoes are too sma I I ?(那些) 3. I v/ant （这个）sweater. I don t want 个 sweater. （那个）is too big. ） 二：选择 ( )1. pen i s red?oenci I i s green? A. this, that B. These, Those C. That, Those D?Thi s, That ( )2. I s a panda over there A. this B. that C ■those D. these ( )3. two girls are Mary and Linda? A. This B. They C ■That D. Those ( )4. Look, what is It' s an eraser? A. this ( )5. Are your watches over there A. this ( )6. those his dietionaries A. Yes, it isn S?isn' t.

(完整版)the的用法

定冠词the的用法： 定冠词the与指示代词this ,that同源,有“那(这)个”的意思,但较弱,可以和一个名词连用,来表示某个或某些特定的人或东西. (1)特指双方都明白的人或物 Take the medicine.把药吃了. (2)上文提到过的人或事 He bought a house.他买了幢房子. I've been to the house.我去过那幢房子. (3)指世界上独一无二的事物 the sun ,the sky ,the moon, the earth (4)单数名词连用表示一类事物 the dollar 美元 the fox 狐狸 或与形容词或分词连用,表示一类人 the rich 富人 the living 生者 (5)用在序数词和形容词最高级,及形容词等前面 Where do you live?你住在哪? I live on the second floor.我住在二楼. That's the very thing I've been looking for.那正是我要找的东西. (6)与复数名词连用,指整个群体 They are the teachers of this school.(指全体教师) They are teachers of this school.(指部分教师) (7)表示所有,相当于物主代词,用在表示身体部位的名词前 She caught me by the arm.她抓住了我的手臂. (8)用在某些有普通名词构成的国家名称,机关团体,阶级等专有名词前 the People's Republic of China 中华人民共和国 the United States 美国 (9)用在表示乐器的名词前 She plays the piano.她会弹钢琴. (10)用在姓氏的复数名词之前,表示一家人 the Greens 格林一家人(或格林夫妇) (11)用在惯用语中 in the day, in the morning... the day before yesterday, the next morning... in the sky... in the dark... in the end... on the whole, by the way...

this, these, that, those 的用法

this, these, that, those 的用法 我们把this, these, that, those 这四个词称为“指示代词”，用来指示或标识人或事物。其中，this 和that 为单数指示代词，these 和those 为复数指示代词。例如： ?This is my mother. 这是我的妈妈。 ?That's my dad. 那是我的爸爸。 ?These are my parents. 这是我的父母。 ?Those are Paul's son and daughter. 那是保罗的儿子和女儿。 通常，我们谈论离自己近的人或物时用 this / these，离自己远的人或物时用that / those 。例如： ?I like these books, but I don't like those books. 我喜欢这些书，但是我不喜欢那些书。 ?This girl is Mary. 这个女孩是玛丽。 ?That boy is in Class 5. 那个男孩在五班。 下面我们来看一下这些指示代词作主语构成一般疑问句时，答句所使用的主语会有什么变化。 ?Is this your bike? ?Yes, it is ?. 这是你的自行车吗？ ?是的，是我的自行车。 ?Are these your grandparents? ?Yes, they are.

?这是你的祖父母吗？ ?是的，他们是。 由此可以看出，当指示代词所指的事物已确定时，后面的指示代词指人时用he、she 和they 来代替，指物时用it 和they 来代替。再如： ?Is that a bird or a plane? ?It's a plane. ?那是鸟还是飞机？ ?是飞机。 ?Is this your friend Tony ?Yes, he is. ?这是你的朋友托尼吗？ ?是的，他是。 上一个：Module1 动词be 说明身份、年龄、状态等 下一个：Module 3 there be 句型的用法

“the way+从句”结构的意义及用法

“tｈｅwａｙ+从句”结构的意义及用法 首先让我们来看下面这个句子: Ｒead the foｌloｗｉnｇpassagｅａnd talkａbout ｉt wi ｔh your classmaｔes．Try tｏｔeｌl whaｔyｏu thiｎk ｏf Tom aｎd ｏｆｔhe ｗay the chiｌｄrenｔrｅated ｈim. 在这个句子中，ｔhe waｙ是先行词,后面是省略了关系副词that或in whｉch的定语从句。 下面我们将叙述“the ｗaｙ+从句”结构的用法。 1.tｈe wａy之后，引导定语从句的关系词是tｈat而不是hoｗ,因此，<<现代英语惯用法词典＞＞中所给出的下面两个句子是错误的：Ｔhｉs is ｔｈeｗayｈowｉtｈａppened. This is the way how he ａlwａｙs treats me. 2．在正式语体中,thaｔ可被in which所代替;在非正式语体中，ｔhat则往往省略。由此我们得到theｗay后接定语从句时的三种模式：1) tｈe ｗaｙ＋ｔhａt-从句２）the way +ｉn whiｃh－从句3) the ｗay +从句 例如：Ｔｈe way(in whiｃh ，thａt) ｔhｅｓｅｃomｒade ｓloｏｋａｔｐrobｌems is wrong．这些同志看问题的方法

不对。 Ｔｈｅｗａy(that ,ｉn ｗhiｃh)yoｕ’ｒe doinｇit is comple ｔeｌy crａzy.你这么个干法,简直发疯。 Wｅａdmiｒｅd him for thｅｗay ｉｎｗhｉch he fａｃeｓdiｆficultieｓ． Waｌlａｃe ａｎd Ｄarwiｎｇreed ｏn the way ｉｎwhi ｃh ｄｉfｆｅrｅnt forms ｏf life had ｂegｕn.华莱士和达尔文对不同类型的生物是如何起源的持相同的观点。 Thｉs is ｔｈe ｗaｙ（tｈａｔ) hｅdｉd it. I lｉkeｄｔhe waｙ(ｔhat) ｓｈeｏｒganized ｔhe ｍeetｉng． 3.theｗay(tｈat)有时可以与hｏw(作“如何”解）通用。例如： That’s tｈe ｗaｙ(tｈaｔ) shｅspoke. = Tｈat’s how shｅspoke.

小学this-that-these-those用法+练习(带答案)

指示代词this,that.these.those 注意 is/are的使用： this/that+可数名词单数或不可数名词+ is these/those＋可数名词复数（不可以加不可数名词）+are 例：离你较近的时候：this duck这个鸭子these ducks这些鸭子 离你较远的时候(经常与over there连用)：that duck 那个鸭子those ducks那些鸭子 常见句型： 1.What is this/that?这是/那是什么？ It’s a/an...它是... 2.Is this/that...?这是/那是...吗？ Yes,it is.是的，它是。No,it isn’t.不，它不是。 3.What are these/those?它们是什么？ They are...它们是... 4.Are these/those...?这些/那些是...吗？ Yes,they are.是的，它们是。No,they aren’t.不，它们不是。 【补充部分：名词规则变化】 1、一般情况（包括以e结尾的名词）,加-s 例：cup(杯子)-cups, cat-cats, cake－cakes, flag(旗子)-flags, face(脸)－faces 2、以s,x,ch,sh结尾,加-es 例：class(班级)-classes, box(盒子)-boxes, watch(手表)-watches, brush(刷子)-brushes 3、以辅音+y结尾变y为ies 例：city(城市)-cities, country(国家)-countries, study(书房)-studies 注意：以元音+y结尾,加-s boy-boys, ray(光线)-rays, day-days 4、以o 结尾 有生命加-es 例：mango(芒果)-mangoes, hero(英雄)-heroes, tomato(西红柿)-tomatoes, potato(土豆)-potatoes 没生命加-s 例：zoo(动物园)-zoos, photo(照片)-photos, piano(钢琴)-pianos 5、以f,fe结尾变f,fe为ves 例：leaf(叶子)-leaves, knife(小刀)-knives, wife(妻子)-wives

way 用法

表示“方式”、“方法”，注意以下用法： 1.表示用某种方法或按某种方式，通常用介词in(此介词有时可省略)。如： Do it (in) your own way. 按你自己的方法做吧。 Please do not talk (in) that way. 请不要那样说。 2.表示做某事的方式或方法，其后可接不定式或of doing sth。 如： It’s the best way of studying [to study] English. 这是学习英语的最好方法。 There are different ways to do [of doing] it. 做这事有不同的办法。 3.其后通常可直接跟一个定语从句(不用任何引导词)，也可跟由that 或in which 引导的定语从句，但是其后的从句不能由how 来引导。如： 我不喜欢他说话的态度。 正：I don’t like the way he spoke. 正：I don’t like the way that he spoke. 正：I don’t like the way in which he spoke. 误：I don’t like the way how he spoke. 4.注意以下各句the way 的用法： That’s the way (=how) he spoke. 那就是他说话的方式。 Nobody else loves you the way(=as) I do. 没有人像我这样爱你。 The way (=According as) you are studying now, you won’tmake much progress. 根据你现在学习情况来看，你不会有多大的进步。 2007年陕西省高考英语中有这样一道单项填空题： ——I think he is taking an active part insocial work. ——I agree with you_____. A、in a way B、on the way C、by the way D、in the way 此题答案选A。要想弄清为什么选A，而不选其他几项，则要弄清选项中含way的四个短语的不同意义和用法，下面我们就对此作一归纳和小结。 一、in a way的用法 表示：在一定程度上，从某方面说。如： In a way he was right.在某种程度上他是对的。注：in a way也可说成in one way。 二、on the way的用法 1、表示：即将来(去)，就要来(去)。如： Spring is on the way.春天快到了。 I'd better be on my way soon.我最好还是快点儿走。 Radio forecasts said a sixth-grade wind was on the way.无线电预报说将有六级大风。 2、表示：在路上，在行进中。如： He stopped for breakfast on the way.他中途停下吃早点。 We had some good laughs on the way.我们在路上好好笑了一阵子。 3、表示：(婴儿)尚未出生。如： She has two children with another one on the way.她有两个孩子，现在还怀着一个。 She's got five children，and another one is on the way.她已经有5个孩子了，另一个又快生了。 三、by the way的用法

The way的用法及其含义(一)

The way的用法及其含义（一） 有这样一个句子：In 1770 the room was completed the way she wanted. 1770年，这间琥珀屋按照她的要求完成了。 the way在句中的语法作用是什么？其意义如何？在阅读时，学生经常会碰到一些含有the way 的句子，如：No one knows the way he invented the machine. He did not do the experiment the way his teacher told him.等等。他们对the way 的用法和含义比较模糊。在这几个句子中，the way之后的部分都是定语从句。第一句的意思是，“没人知道他是怎样发明这台机器的。”the way的意思相当于how；第二句的意思是，“他没有按照老师说的那样做实验。”the way 的意思相当于as。在In 1770 the room was completed the way she wanted.这句话中，the way也是as的含义。随着现代英语的发展，the way的用法已越来越普遍了。下面，我们从the way的语法作用和意义等方面做一考查和分析： 一、the way作先行词，后接定语从句 以下3种表达都是正确的。例如：“我喜欢她笑的样子。” 1. the way+ in which +从句 I like the way in which she smiles. 2. the way+ that +从句 I like the way that she smiles. 3. the way + 从句(省略了in which或that) I like the way she smiles. 又如：“火灾如何发生的，有好几种说法。” 1. There were several theories about the way in which the fire started. 2. There were several theories about the way that the fire started.

way 的用法

way 的用法 【语境展示】 1. Now I’ll show you how to do the experiment in a different way. 下面我来演示如何用一种不同的方法做这个实验。 2. The teacher had a strange way to make his classes lively and interesting. 这位老师有种奇怪的办法让他的课生动有趣。 3. Can you tell me the best way of working out this problem? 你能告诉我算出这道题的最好方法吗？ 4. I don’t know the way (that / in which) he helped her out. 我不知道他用什么方法帮助她摆脱困境的。 5. The way (that / which) he talked about to solve the problem was difficult to understand. 他所谈到的解决这个问题的方法难以理解。 6. I don’t like the way that / which is being widely used for saving water. 我不喜欢这种正在被广泛使用的节水方法。 7. They did not do it the way we do now. 他们以前的做法和我们现在不一样。 【归纳总结】 ●way作“方法，方式”讲时，如表示“以……方式”，前面常加介词in。如例1； ●way作“方法，方式”讲时，其后可接不定式to do sth.，也可接of doing sth. 作定语，表示做某事的方法。如例2，例3；

this,that,these,those的用法及习题

语法强化与扩展： 指示代词：this,that,these,those 1.this和these指说话人较近的人或物，that和those指说话人较远的人或物． this和that指单数，be动词用单数形式is，同时后面的名词用单数形式。 these和those表示复数，be动词用复数形式are，同时后面的名词用复数形式。 2.在回答主语this或that的一般疑问句或特殊疑问句时，在答语中用it代替句中this或that; Whatisthis/that这是/那是什么It’sa/an...它是...； Isthis/that...这是/那是...吗Yes,itis.是的，它是。No,itisn’t.不，它不是。 3.在回答主语these或those的一般疑问句或特殊疑问句时，在答语中用they代替句中these 或those; Whatarethese/those它们是什么Theyare...它们是...; Arethese/those...这些/那些是.吗Yes,theyare.是的，它们是。No,theyaren’t.不，它们不是。 4.在介绍某人时，用this或that，而不用he或she；例如Thisismymum. 5.打电话时，说自己是谁用Thisis...，问别人是谁用Who’sthat 一：填空 1.Ilike_____pants._______pantsarered.(这些) 2.Idon’tlike____shoes.____shoesaretoosmall.(那些) 3.Iwant_____(这个)sweater.Idon’twant________（那个）sweater._____（那个）istoobig. 二：选择 （）1.__________penisred.________pencilisgreen. A.this,that B.These,Those C.That,Those D.This,That （）2.Is_____apandaoverthere A.this B.that C.those D.these （）3.__________twogirlsareMaryandLinda. A.This B.They C.That D.Those （）4.Look，whatis_______It’saneraser. A.this （）5.Are________yourwatchesoverthere A.this （）6._______thosehisdictionaries （）7.——IsthatMary’sschoolbag ——_________ A.Yes,itisn’t.,itis.,it’s.,itisn’t. （）8.——_______thisyourfriend ——_______ A.is;Yes,heis.;Yes,itis.;No,itsnot.;Yes,Iam. （）9.——AretheseHelen’spencils ——_______ A.Yes,they’re.,theyare.,theyare.,itis. （）10.——Whatarethose ——______

清华大学《控制工程基础》课件-4

则系统闭环传递函数为 假设得到的闭环传递函数三阶特征多项式可分解为 令对应项系数相等，有 二、高阶系统累试法 对于固有传递函数是高于二阶的高阶系统，PID校正不可能作到全部闭环极点的任意配置。但可以控制部分极点，以达到系统预期的性能指标。 根据相位裕量的定义，有 则有 则 由式可独立地解出比例增益，而后一式包含两个未知参数和，不是唯一解。通常由稳态误差要求，通过开环放大倍数，先确定积分增益，然后计算出微分增益。同时通过数字仿真，反复试探，最后确定、和三个参数。 设单位反馈的受控对象的传递函数为 试设计PID控制器，实现系统剪切频率 ，相角裕量。 解： 由式,得 由式,得 输入引起的系统误差象函数表达式为

令单位加速度输入的稳态误差，利用上式，可得 试探法 采用试探法，首先仅选择比例校正，使系统闭环后满足稳定性指标。然后，在此基础上根据稳态误差要求加入适当参数的积分校正。积分校正的加入往往使系统稳定裕量和快速性下降，此时再加入适当参数的微分校正，保证系统的稳定性和快速性。以上过程通常需要循环试探几次，方能使系统闭环后达到理想的性能指标。 齐格勒－尼柯尔斯法 （Ziegler and Nichols ） 对于受控对象比较复杂、数学模型难以建立的情况，在系统的设计和调试过程中，可以考虑借助实验方法，采用齐格勒－尼柯尔斯法对PID调节器进行设计。用该方法系统实现所谓“四分之一衰减”响应（”quarter-decay”），即设计的调节器使系统闭环阶跃响应相临后一个周期的超调衰减为前一个周期的25%左右。 当开环受控对象阶跃响应没有超调，其响应曲线有如下图的S形状时，采用齐格勒－尼柯尔斯第一法设定PID参数。对单位阶跃响应曲线上斜率最大的拐点作切线，得参数L 和T，则齐格勒－尼柯尔斯法参数设定如下： (a) 比例控制器： (b) 比例－积分控制器： ， (c) 比例－积分－微分控制器： ， 对于低增益时稳定而高增益时不稳定会产生振荡发散的系统，采用齐格勒－尼柯尔斯第二法(即连续振荡法)设定参数。开始只加比例校正，系统先以低增益值工作，然后慢慢增加增益，直到闭环系统输出等幅度振荡为止。这表明受控对象加该增益的比例控制已达稳定性极限，为临界稳定状态，此时测量并记录振荡周期Tu和比例增益值Ku。然后，齐格勒－尼柯尔斯法做参数设定如下： (a) 比例控制器：

the-way-的用法讲解学习

t h e-w a y-的用法

The way 的用法 "the way+从句"结构在英语教科书中出现的频率较高, the way 是先行词, 其后是定语从句.它有三种表达形式:1) the way+that 2)the way+ in which 3)the way + 从句(省略了that或in which),在通常情况下, 用in which 引导的定语从句最为正式,用that的次之,而省略了关系代词that 或 in which 的, 反而显得更自然,最为常用.如下面三句话所示,其意义相同. I like the way in which he talks. I like the way that he talks. I like the way he talks. 一.在当代美国英语中,the way用作为副词的对格,"the way+从句"实际上相当于一个状语从句来修饰全句. the way=as 1)I'm talking to you just the way I'd talk to a boy of my own. 我和你说话就象和自己孩子说话一样. 2)He did not do it the way his friend did. 他没有象他朋友那样去做此事. 3)Most fruits are naturally sweet and we can eat them just the way they are ----all we have to do is clean or peel them . 大部分水果天然甜润,可以直接食用,我们只需要把他们清洗一下或去皮.

this-that-these-those-句型的用法

功课：2017年1月16日 翻译下列句子： 1.这是一张深黄色桌子。 2.那是一盏电灯。那些是椅子。那些是窗户。 3.这些是学生们的书包 4.那是一个黑色的书柜。 5.这是我的英语词典。 6.这些淡绿色的笔记本是我的，不是你的。 7.这些是你的英语书吗？不，不是，我的英语书是褐色的。 8.那些是她的铅笔吗？不，不是，她的铅笔是红色的。 9.这支钢笔是谁的？这支钢笔是他的。 10.这台电脑是你的吗？不，不是我的，是她的。 11.你的玩具车什么颜色？我的玩具车是白色的。 二、请同学们写下教室的物品。(回忆单词）

文具类：pen, pencil, chalk, pencilcase, ruler,eraser, paper, notebook 书籍类：dictionary, book, math book, Chinese book, physics book, chemistry book, biology book, history book, geography book 物品类：door, window, floor, ceiling, roof, light, fan, blackboard, platform, desk, chair, clock, screen, prejector, loudspeaker box, monitor, switch, socket, slogan, bookcase 生活用品类：schoolbag，bottle, cup 三、用所给名词的适当形式填空。 1.These are ____(pencil). 2.Those are______(dictionary). 3.There are two______(piano) in our school. 4.These are ______(Chinese). 5.Those are ______(box). 参考答案 一、翻译 1.This is a desk. 2.That is a lamp. 3.These are students’ schoolbag. 4.That is bookcase. 5.Those are chairs. 6.This is my English dictionary. 7.These are windows. 8.These are books. 三、填空 1.Pencils 2.Dictionaries 3.Pianos 4.Chinese 5.boxes This/That is .......和These/Those are......

way的用法总结大全

way的用法总结大全 way的用法你知道多少，今天给大家带来way的用法，希望能够帮助到大家，下面就和大家分享，来欣赏一下吧。 way的用法总结大全 way的意思 n. 道路,方法,方向,某方面 adv. 远远地，大大地 way用法 way可以用作名词 way的基本意思是“路,道,街,径”,一般用来指具体的“路,道路”,也可指通向某地的“方向”“路线”或做某事所采用的手段,即“方式,方法”。way还可指“习俗,作风”“距离”“附近,周围”“某方面”等。 way作“方法,方式,手段”解时,前面常加介词in。如果way前有this, that等限定词,介词可省略,但如果放在句首,介词则不可省略。

way作“方式,方法”解时,其后可接of v -ing或to- v 作定语,也可接定语从句,引导从句的关系代词或关系副词常可省略。 way用作名词的用法例句 I am on my way to the grocery store.我正在去杂货店的路上。 We lost the way in the dark.我们在黑夜中迷路了。 He asked me the way to London.他问我去伦敦的路。 way可以用作副词 way用作副词时意思是“远远地,大大地”,通常指在程度或距离上有一定的差距。 way back表示“很久以前”。 way用作副词的用法例句 It seems like Im always way too busy with work.我工作总是太忙了。 His ideas were way ahead of his time.他的思想远远超越了他那个时代。 She finished the race way ahead of the other runners.她第一个跑到终点,远远领先于其他选手。 way用法例句

this that these those 句型的用法

随堂练习 一、翻译下列句子： 1.这是一张桌子。 2.那是一盏电灯。 3.这些是学生们的书包 4.那是一个书柜。 5.那些是椅子。 6.这是我的英语词典。 7.那些是窗户。 8.这些是书。 二、请同学们写下教室的物品。(回忆单词） 文具类：pen, pencil, chalk, pencilcase, ruler,eraser, paper, notebook 书籍类：dictionary, book, math book, Chinese book, physics book, chemistry book, biology book, history book, geography book 物品类：door, window, floor, ceiling, roof, light, fan, blackboard, platform, desk, chair, clock, screen, prejector, loudspeaker box, monitor, switch, socket, slogan, bookcase 生活用品类：schoolbag，bottle, cup 三、用所给名词的适当形式填空。 1.These are ____(pencil). 2.Those are______(dictionary). 3.There are two______(piano) in our school. 4.These are ______(Chinese). 5.Those are ______(box).

参考答案 一、翻译 1.This is a desk. 2.That is a lamp. 3.These are students’ schoolbag. 4.That is bookcase. 5.Those are chairs. 6.This is my English dictionary. 7.These are windows. 8.These are books. 三、填空 1.Pencils 2.Dictionaries 3.Pianos 4.Chinese 5.boxes

《交通规划》课程教学大纲

《交通规划》课程教学大纲 课程编号：E13D3330 课程中文名称：交通规划 课程英文名称：Transportation Planning 开课学期：秋季 学分/学时：2学分/32学时 先修课程：管理运筹学，概率与数理统计，交通工程学 建议后续课程：城市规划，交通管理与控制 适用专业/开课对象：交通运输类专业/3年级本科生 团队负责人：唐铁桥责任教授：执笔人：唐铁桥核准院长： 一、课程的性质、目的和任务 本课程授课对象为交通工程专业本科生，是该专业学生的必修专业课。通过本课程的学习，应该掌握交通规划的基础知识、常用方法与模型。课程具体内容包括：交通规划问题分析的一般方法，建模理论，交通规划过程与发展历史，交通调查、出行产生、分布、方式划分与交通分配的理论与技术实践，交通网络平衡与网络设计理论等，从而在交通规划与政策方面掌握宽广的知识和实际的操作技能。 本课程是一间理论和实践意义均很强的课程，课堂讲授要尽量做到理论联系实际，模型及其求解尽量结合实例，深入浅出，使学生掌握将交通规划模型应用于实际的基本方法。此外，考虑到西方在该领域内的研究水平，讲授时要多参考国外相关研究成果，多介绍专业术语的英文表达方法以及相关外文刊物。课程主要培养学生交通规划的基本知识、能力和技能。 二、课程内容、基本要求及学时分配 各章内容、要点、学时分配。适当详细，每章有一段描述。 第一章绪论（2学时） 1. 交通规划的基本概念、分类、内容、过程、发展历史、及研究展望。 2. 交通规划的基本概念、重要性、内容、过程、发展历史以及交通规划中存在的问题等。

第二章交通调查与数据分析（4学时） 1. 交通调查的概要、目的、作用和内容等；流量、密度和速度调查；交通延误和OD调查；交通调查抽样；交通调查新技术。 2. 交通中的基本概念，交通流量、速度和密度的调查方法，调查问卷设计与实施，调查抽样，调查结果的统计处理等。 第三章交通需求预测（4学时） 1. 交通发生与吸引的概念；出行率调查；发生与吸引交通量的预测；生成交通量预测、发生与吸引交通量预测。 2. 掌握交通分布的概念；分布交通量预测；分布交通量的概念，增长系数法及其算法。 3. 交通方式划分的概念；交通方式划分过程；交通方式划分模型。 第四章道路交通网络分析（4学时） 1. 交通网络计算机表示方法、邻接矩阵等 2. 交通阻抗函数、交叉口延误等。 第五章城市综合交通规划（2学时） 1. 综合交通规划的任务、内容；城市发展战略规划的基本内容和步骤 2. 城市中长期交通体系规划的内容、目标以及城市近期治理规划的目标与内容 第六章城市道路网规划（2学时） 城市路网、交叉口、横断面规划及评价方法。 第七章城市公共交通规划（2学时） 城市公共交通规划目标任务、规划方法、原则及技术指标。 第八章停车设施规划（2学时） 停车差设施规划目标、流程、方法和原则。 第九章城市交通管理规划（2学时） 城市交通管理规划目标、管理模式和管理策略。 第十章公路网规划（2学时） 公路网交通调查与需求预测、方案设计与优化。 第十一章交通规划的综合评价方法（2学时） 1. 交通综合评价的地位、作用及评价流程和指标。 2. 几种常见的评价方法。 第十二章案例教学（2学时）

the_way的用法大全教案资料

t h e_w a y的用法大全

The way 在the way+从句中, the way 是先行词, 其后是定语从句.它有三种表达形式:1) the way+that 2)the way+ in which 3)the way + 从句(省略了that或in which),在通常情况下, 用in which 引导的定语从句最为正式,用that的次之,而省略了关系代词that 或 in which 的, 反而显得更自然,最为常用.如下面三句话所示,其意义相同. I like the way in which he talks. I like the way that he talks. I like the way he talks. 如果怕弄混淆，下面的可以不看了 另外，在当代美国英语中,the way用作为副词的对格,"the way+从句"实际上相当于一个状语从句来修饰全句. the way=as 1)I'm talking to you just the way I'd talk to a boy of my own. 我和你说话就象和自己孩子说话一样. 2)He did not do it the way his friend did. 他没有象他朋友那样去做此事. 3)Most fruits are naturally sweet and we can eat them just the way they are ----all we have to do is clean or peel them . 大部分水果天然甜润,可以直接食用,我们只需要把他们清洗一下或去皮. the way=according to the way/judging from the way 4)The way you answer the qquestions, you must be an excellent student. 从你回答就知道,你是一个优秀的学生. 5)The way most people look at you, you'd think a trashman was a monster. 从大多数人看你的目光中,你就知道垃圾工在他们眼里是怪物. the way=how/how much 6)I know where you are from by the way you pronounce my name. 从你叫我名字的音调中,我知道你哪里人. 7)No one can imaine the way he misses her. 人们很想想象他是多么想念她. the way=because 8) No wonder that girls looks down upon me, the way you encourage her. 难怪那姑娘看不起我, 原来是你怂恿的