2 Basic principles of legal information retrieval

Some of the justifications for computerised legal information retrieval systems are best seen by considering some of the problems of our existing print-based methods of producing and retrieving legal materials. There are problems of quantity, and problems of manual retrieval techniques.

Problems of quantity

The expanding volume of law is the cause of most of the problems. In 1974 it was estimated that the volume of Australian caselaw and statute law was 3423 million characters (about 7,000 million words), with a growth rate of 2.5% per annum (about 16 million words) (Committee on Computerisation of Legal Data Report , Australian Government, Attorney-General's Department,1974).

A 1982 study for the New South Wales government estimated that the volume of NSW Supreme Court cases reported during the 1970s was double that reported during the 1950s (Cooley Douglas Associates Pty Ltd Feasibility Study -- Legal Information Retrieval System for New South Wales, 1982, Box 411 Sydney 2001, p 63).

In the United Kingdom there were an estimated 300,000 reported cases up to 1963 (C Tapper Computers and the Law, 1973, Weidenfeld and Nicholson, pp111-14). Taking United Kingdom and Australian cases together, there must be at least half a million reported cases by now. So, whenever you attempt to retrieve all the relevant caselaw on a particular problem, you are in fact `going fishing' in a sea of caselaw containing over half a million fish.

Lawyers are faced with both a vast and growing accumulation of old information and an accelerating rate of production of new information. Old and new information each raise different problems of retrieval.

Old information

The importance of old information to lawyers, both in the form of reported cases and statutes which have not been repealed, is an unusual feature of law in comparison with other professions. Most information used by professions which are based on the natural sciences (medicine, engineering etc) have little need to retrieve information more than a decade or two old, and in some cases, considerably less. Information has a relatively short `half-life' (the period after which it is only half as useful) in such professions. The `half life' of legal information is more difficult to determine, even if the idea does have some meaning for legal information. Even statutes which have been repealed must be available so the law in force at the time of a case can be understood. Reported cases which have been accepted as precedents do not lose their authority by virtue of age. Even cases which have been overruled or repeatedly distinguished may be consistently referred to, and some stage 'comebacks' after many years. A case may be ignored for years, but then be `discovered' as embodying some now-important principle.

However, the importance of old cases should not be exaggerated. Recent research on the citation of pre-1960 NSW Supreme Court cases in the 1983-85 NSW Law Reports shows that there are, on average, only about eight decisions from each year in the 1940-60 period which are now cited at all, and about 3 decisions from each year in the 1900-1930 period which are now cited (DP Lewis `Caselaw databases: the marginal utility of additional historical materials' Vol 3 No 5 (1987) Computer Law & Practice ). So each year's law reports throw up a few decisions which remain demonstrably relevant many years later, but they are the exception rather than the rule.

The continuing relevance of old information gives rise to storage and distribution problems which are also peculiar to lawyers. The costs of each lawyer obtaining a printed copy of a comprehensive library of cases, statutes, and secondary materials, and continuing to do so every year are prohibitively high. Only the largest firms or Government departments can afford even a good library. Most lawyers must use public law libraries or, more likely, ignore some sources they should consult.

New information

Keeping up to date with new legal information presents different problems. Cases are not reported for at least some months after they are decided. Amendments to statutes and regulations are often not available until after they are proclaimed, and consolidations only appear infrequently. Loose-leaf services are the legal publishing industry's most effective attempt to deal with this. However, the costs involved in continually printing and distributing replacement pages, and the recipient's costs in filing make them more expensive than conventional books, and the extent to which publishers keep them up to date varies considerably.

Problems of existing retrieval techniques

Faced with such a vast amount of legal materials, how do we retrieve the documents (cases, sections of statutes, or commentaries) which are relevant to a particular problem? Our existing retrieval tools are all forms of indexing. The most important are subject indexing and citation indexing, although there are other types such as key word indexing (J Bing (Ed) Handbook of Legal Information Retrieval, North Holland, 1984 pp74-9).

Subject indexing

Subject indexing includes indices to books or statutes, and the catchwords of cases. Whatever form it takes, it effectively places an intermediary between the legal materials and their eventual user. That intermediary, the indexer, categorises the legal materials in terms of concepts selected for inclusion in the index, whether or not the terms used to express those concepts appear in the original document or not. The process of selecting those concepts is subjective, a matter of human judgment. The concepts chosen may be too broad or too narrow to be useful, or simply miss some important aspect of the material. What the indexer considered important about, say, a case, might not be why the case could be considered important in the future -- what are important issues varies over time. Furthermore, the concepts used vary between indexers.

Tapper comments on some of the limitations of indexing:

Despite the small number of legal publishers ... there is no uniformity of terms or categories... This is partly because authors often do their own indexing, and partly because in other cases it is usually done by part-time or inexperienced staff with little training or supervision.

...
Even if all indexes were completely standard and uniform there would still be difficulties. These stem from the fact that indexing normally depends upon the use of the human brain to select the correct terms to characterise whatever is being indexed. as every indexer knows this involves making choices, and there is no guarantee whatever that all indexers will make them in the same way. (Tapper op cit pp120-1)

Experiments by Tapper showed very little correlation between the terms selected by a number of indexers to index the same material (ibid).

Citations

The use of citation indices (citators) does eliminate human judgment in selection, by allowing all subsequent references to a case or section of an Act to be retrieved. In theory, any relevant case may be used as a starting point, and by searching interconnected citations both forwards and backwards, all relevant cases should be retrieved. Citators are costly and labour-intensive to produce and update in print form, but they are available automatically as a by-product of a computerised retrieval system.

There are also some inherent limitations on citations as a means of research. As Tapper points out:

Such a technique depends for finding a case upon its either being cited, or its citing other cases. This is not always true even of reported cases. Thus of the 120 cases reported in [1961] 1 All England Reports, sixteen cite no case, and of these no fewer than thirteen seem not so far to have been cited in subsequent cases.

...
On the other hand, the citation approach can also lead to the retrieval of large numbers of irrelevant cases. The reason for this is that a case may raise a very large number of issues quite distinct from each other. It may thus be cited quite reasonably as authority in a very large number of cases which have nothing whatsoever to do with a particular point in which the searcher is interested. (Tapper ibid p124)

Basic principles of computerised retrieval

Hardware

The host computer

The organisation which operates a commercial information retrieval service (for example, CLIRS) requires a powerful computer capable of storing very large amounts of information (the databases), retrieving it quickly for users (by the retrieval software), and communicating with a large number of different users simultaneously (the communications system). The user of an information retrieval system needs to know very little about the operator's computer, which is sometimes called the `host' computer, beyond these simple facts.

The lawyer or other user who wishes to obtain access to this host computer and its information retrieval system may be located in another part of the same city, or hundreds of miles away. In order to do so, the user will require a computer terminal and other equipment (including a device known as a modem) to connect that terminal, via the telephone system, to the host computer. The user, sometimes called a `remote user', is then said to be `on line' to the host computer. The equipment required by the user is discussed in Chapter 5, but does not concern us now because it is not fundamental to the nature of information retrieval.

So the overall picture is that of a central `host' computer communicating with a the terminals of a variety of `remote' users via the telephone system, as in the illustration below.

Information retrieval systems

Two components

The expression `information retrieval system' is a vague one, and is used with a variety of meanings. It is sometimes used to include the host computer and the communications arrangements discussed above, but we exclude these. In our usage, an information retrieval system has two components:

(i) the databases (information or data), the computerised store of information which is to be searched (such as cases and statutes); and

(ii) the retrieval program (software), the computer program which is used to instruct the computer how to search the databases.

These two components have two things in common: they are both stored on the host computer; and they are both stored in the only way in which computers can store information, as binary representations (code or notation) of that information.

Binary representation

A binary representation of information is one in which all information is reduced to combinations of two states, which can be thought of as ON or OFF, as one (1) or zero (0), or, say, as presence or absence of a magnetic charge. So, for instance, the word `dog' may be represented in binary notation by the string of zeros and ones `01100100 01101111 01100111', whereas the word `cat' may be represented as `01100011 01100001 01110100', and a blank space by `00100000'. In computer terminology, each 0 or 1 is called a `bit', and letters or other characters are represented by groups of 8 bits, called `bytes'. You can think of a byte as equivalent to a letter, so words are made up of bytes.

A case or a statute, as stored in a computer, can usefully be thought of a an enormously long string of bits, of zeros and ones.

Databases (information)

A database is nothing more than a collection of information stored in computerised form. The word `database' is also ambiguous, in that sometimes it is used to refer to all the information held on one computer, and sometimes it is used to refer to only part of that information, so that one computer is said to hold multiple databases.

We will use `database' to refer to the largest part of the information stored on a computer which can be searched together in the one search. So the CLIRS computer contains many different databases, not just one, because you cannot search all of the information on CLIRS at the same time.

The contents of databases may be as various as the information which may comprise them. However, it is useful to distinguish some major categories of information contained in databases:

Full text (or `authentic text') of documents - for example the complete text of the judgments in a case as reported, or of a statute or a legal textbook;
Abstracts - summaries of the full text, such as a headnote of a case;
Bibliographic entries - the identification of the documents, such as the name and author of a book or article;
Subject indices - the concepts under which the document may be indexed, such as catchwords to cases;
Cross references - other documents referred to in the document, such as citations of cases or sections.

Many legal databases contain the full texts of cases or statutes (and therefore also contain abstracts or subject indices), but there are just as many which contain only headnotes, citations or bibliographic entries. The CLIRS system contains databases in all these categories.

`Free-text' or `full-text' retrieval systems

The type of databases and retrieval systems used by legal information retrieval systems are usually called `full-text` or `free-text' systems. They are different from conventional database programs (a well-known examples of which is dBaseIII) in two main ways:

(i) Conventional databases often require the information stored in them to be divided into separate fields, each of which may have a strict size limit (say 64 words), whereas free text systems can store data of variable and unlimited length;

(ii) Conventional databases rely heavily on this strict field structure in order to retrieve information quickly, whereas free text systems use a special technique called a concordance, a word-occurrence index of the location of every word in the database. The concordance is explained in detail below.

In this Handbook, `retrieval system' is used synonymously with `free-text retrieval systems', unless stated otherwise. All of the types of information mentioned above are stored in free-text systems, although bibliographies and abstracts can be stored in conventional databases.

The retrieval program (software)

The other part of an information retrieval system is the retrieval software, the computer program which is used by the user to instruct the computer how to retrieve the documents which the user wishes to locate. The retrieval program used by the CLIRS system is called STATUS, discussed in Chapter 4 and (at length) in Part B.

There are a number of different retrieval programs which may be used, most of which operate in a fashion similar to that which is outlined in the rest of this chapter. Bing's Handbook provides details of the main retrieval programs in use around the world. Other important retrieval programs from an Australasian point of view are STAIRS (used by AUSINET), BRS/SEARCH (used by KiwiNet) and the LEXIS retrieval program, discussed in Part C.

Retrieval techniques

Word occurrence searching vs concept searching

A typical problem in legal research is of the form 'I would like to find all cases on the subject of ....', and the subject may be something like `trespass by children' or `the liability of schools for accidents to children'. In thinking of problems in this way, we characterise what we wish to retrieve in terms of concepts. We don't particularly care whether the specific words `trespass`, `children' or `liability' appear in the cases (though we won't be surprised if they do), provided the cases deal with those concepts.

How do you instruct a computer to retrieve all such cases? Neither a computer nor a legal information retrieval system has any understanding of the legal system or legal concepts. Unless the retrieval system contained a list of all documents where these concepts were dealt with, it literally would not know where to look. Such a list would simply be a subject index of the database, and while that would no doubt be useful, it would bring with it many of the deficiencies of manual searching and would be a very minor advance.

Existing information retrieval systems commence from a completely different starting point. The basic assumption of most computerised retrieval systems is that the concepts we are searching for can be represented adequately by the words most commonly used to express those concepts . What an information retrieval system does is find all occurrences of specified words, or all occurrences of particular combinations of words, very fast. If a search request can be put in terms of words or combinations of words, the system can find all documents containing those words very quickly.

The search language

Just as a computer cannot understand concepts, nor can most present day computers be communicated with by ordinary language, or `natural language' as it is called. In order to give instructions to the computer, using a retrieval program, you must phrase the instructions precisely in the syntax or language required by that retrieval program. However, these instructions sometimes come close to instructions in ordinary English. For example, in STATUS, where `Q' refers to the `question' command, the instruction Q negligence means "find all articles containing the word `negligence'", and Q trespass + children means "find all cases containing the word `trespass' and the word `children'". It is really just a matter of issuing instructions in a very simplified, but very grammatically strict, form of English.

There are two methods by which most existing information retrieval systems retrieve documents containing words or combinations of words: concordance searching, and scanning. Understanding the difference between them is fundamental to understanding how information retrieval works.

Scanning

Scanning uses the most obvious method. One of the things a computer does best is make comparisons at very great speed. Since it recognises words like `trespass' and `children' as strings of 0s and 1s, and it stores whole cases as exceedingly long strings of 0s and 1s, the obvious way in which it could search for all cases containing both of these words would simply be to start at the start of the first case in the database, and scan through it from start to finish to find if any part of the string of 0s and 1s for that case matched the strings for `trespass' and for `children'. Then it could move on to the second case, then the third, and so on, in what is called a sequential search.

While computers can make such comparisons very quickly, the types of computers used for information retrieval systems are generally not fast enough to carry out this type of sequential search over large databases. The most powerful computer in the world would have severe difficulties. It must be remembered that a database of caselaw may contain many thousands of cases, some of which will run for hundreds of pages of text. The computer may be able to carry out such a search in half an hour, which is, of course, very fast compared with how long it would take a person. However, such a `response time' - the time between when the user issues a command and when the computer provides a response - would be regarded by most users of information retrieval systems as unacceptably slow. No one is going to wait at a terminal for more than a minute or so without becoming impatient.

As a result, scanning is generally only used once a small number of potentially relevant documents have already been isolated. With a small number of documents, scanning commands may have a response time of seconds, or, at worst, a few minutes.

The concordance and the text files

The principal retrieval method in existing retrieval systems is much more ingenious. It involves the creation in the database of an `index file' or `concordance', also sometimes called an `inverted file'. This concordance file is in addition to the actual documents themselves as they appear when you read them. The concordance is best understood as a word occurrence dictionary . A dictionary lists all words in alphabetic order followed by their meanings. A concordance lists every different word which occurs in the whole database in alphabetic order, followed by a list of the locations of every occurrence of that word in the documents in the database. A few common words are not indexed.

The location of an occurrence of a word in a database is recorded in the concordance as a set of numbers. For example, in such a system each database could be divided into numbered Chapters (eg a year of Law Reports), each Chapter into numbered Articles (eg one case), each Article into numbered and named Sections (specified parts of a document, such as the Title, the Headnote of a case, each Judgment, or the Longtitle of an Act), each Section into numbered Paragraphs, and each Paragraph into numbered Words. Therefore, each occurrence of each word in the database can be recorded as a unique five number set. The set (87, 23, 3, 15, 1) would mean the 1st Word of the 15th Paragraph of the 3rd Section of the 23rd Article in the 87th Chapter of a Database). The concordance can be thought of as an alphabetical list of words, with each word followed by as many of these four number sets as there are occurrences of that word in the database.

When a new document is added to a database, a retrieval program (STATUS, STAIRS, AIRS etc) is used by the system operator to `concord' the new document. In other words, all occurrences of words in the new document are added to the existing concordance.

An extract from the concordance of an AIRS database is included at the end of this chapter. It uses a 5 place concordance, as described above. The numbered Sections in the extract in the document are 1 - TITLE (the start of every article, although the word TITLE does not appear), 2 - SECT, 3 - NOTES, and 5 - LONGTITLE. In the example, you can see from the document that the word `animals' appears 5 times, and so there are 5 sets of 5 numbers listed under `animals' in the concordance. In Article 2 you will see that the expression `liability for damage' occurs. In the concordance this occurrence of `liability' is recorded as (2,2,5,1,5), `for' is (2,2,5,1,6) and `damage' is (2,2,5,1,7).

There are other ways of dividing up information which could be used to create a concordance. For example, paragraphs could be divided into numbered sentences, and the sentences then divided into words. However, each additional number in the concordance creates additional information which has to be stored in the computer. In most information retrieval systems, the concordance takes up at least as much storage space as the documents themselves, and additional levels in the concordance may make this ratio considerably worse than 1:1.

STATUS only uses a four level concordance, Section locations being recorded by the database creator allocating a maximum number of paragraphs for each Section. LEXIS and STAIRS do not record grammatical paragraph numbers. STAIRS records sentence numbers. These differences between retrieval systems need not concern you at this stage: the basic principles of the different systems are the same.

Searches using the concordance

When a user of an information retrieval system instructs the computer to carry out a search, the computer goes first to the concordance and finds which documents satisfy the search request. Only then does the computer go to the actual documents, retrieve those documents which the concordance has identified as satisfying the request, and present them to the user as the result of the search.

As an example, to find which documents satisfy the request Q trespass + children, the computer would first find in the concordance all locations of `trespass', and then compare this list with the list of locations for `children'. Since the request is for the words to appear in the same Article, this means that both the first and second numbers of the five number set must be the same for occurrences of each word.

As you might expect, there are even some special tricks by which the computer finds the correct entries in the concordance. It doesn't start at `aardvark' and proceed sequentially through the index until the desired words are found. Just as you have tabs in a dictionary indicating where the `T`s start and stop (ie where the`U's start), and you therefore go straight to the `T's to find `trespass', so there can be a similar `sparse index' to the concordance itself.

The search process using the concordance is illustrated in the diagram below.

Concordance-based retrieval

An illustration of concordance-based retrieval

Concepts as relationships between words

Most concepts for which we may wish to search cannot be expressed by simply one word. Any search language must contain rules for how concepts which can be expressed as combinations of words may be searched for.

Logical connectors

The most common method is by the use of the logical connectors AND, OR and NOT. These are based on Boolean algebra, named after the British mathematician, George Boole.

In the STATUS search language, a plus sign (+) means AND, a comma (,) means OR and a minus (-) means NOT.

One way of thinking of these connectors is as relationships between sets of documents. For example, in the diagram below, A could represent the set of documents in a database containing the word `trespass', and B the set of documents containing the word `children'. The relationships between the sets of documents, and how they are expressed in the STATUS search language, then follow.

Some search languages (eg STAIRS) also use another connector called NOT AND or XOR (exclusive or), which means `A or B but not both', but this connector is used rarely.

Positional connectors

Additional connectors which depend on how close two words are to each other are provided in STATUS and in other search languages. For example, in STATUS,

Q trespass // children is the instruction given to retrieve all documents where `trespass' and `children' occur in the same paragraph of the same article, and

Q trespass /3/ children means that the two words must occur not only in the same paragraph, but that `children' must follow three words after `trespass'.

In the example following, a search for

Q liability /2/ damage

would find the phrase `liability for damage', because this occurrence of `liability' is recorded as (2,2,5,1,5) and `damage' is (2,2,5,1,7), and so the distance between the words is 2 words as specified.

Connectors and the concordance

There is a close relationship between the connectors which may be used in a search language and the structure of the concordance created by the retrieval program. This is illustrated for the AIRS retrieval program (the program used at a number of Australian law schools) in the following diagram:

Relationship between connectors and levels of the concordance

Operator Levels of concordance which must be compared

`and'	+	chapter	article
`or'	,	chapter	article
`not'	-	chapter	article
`within a Section'	@			section
`same paragraph'	//	chapter	article	section	paragraph
`n words after'	/n/	chapter	article	section	paragraph	word

When the retrieval program is carrying out a search which uses one of the logical connectors (+ , - ) it need only compare the concordance numbers for the connected words at the first two levels of the concordance, Chapter and Article. When the @ connector is used, the numbers at the third level, section, must also be compared. When the // connector is used, the numbers at the fourth level, Paragraph, must also be compared. When the /n/ connector is used, the numbers at the fifth level, Word, must also be compared.

Impossible operators

If you have a concordance which identifies occurrences of words by their Chapter, Article, Paragraph and Word numbers, it is not possible to have a search language which allows you to retrieve, for example, all documents where two words appear in the same sentence. The retrieval system simply has no way of looking up the concordance to determine whether or not they are in the same sentence, because it does not record the existence of sentences. However, it can retrieve all documents where the two words are only two words apart, because it can look up the concordance to see if any occurrences of the two words have the same Chapter, Article, Section and Paragraph numbers, but have Word numbers differing by two.

The use of these logical and positional connectors in STATUS is explained fully in Chapters 10 and 11. As mentioned earlier, the way in which STATUS implements Sections is different from the explanation given above for AIRS.

Summary

A somewhat more complex description of an information retrieval system has now emerged, consisting of:

(i) The databases (information), consisting of two parts:

(a) the documents, or text files; and

(b) the concordance, or word occurrence index.

(ii) The retrieval program (software) performing two main functions:

(a) to allow the system operator to build the concordance from the documents; and

(b) to provide a search language used by the users of the system to retrieve documents, based on logical (Boolean) and proximity connectors.

In the next chapter, some deficiencies of existing retrieval techniques, and suggested improvements, are discussed.

The concordance -- example and exercise

Extracts from an AIRS database

The examples below show two extracts from an AIRS database. The first is from the Document file, which is also the same as it would appear on screen if retrieved in a search. Chapter 2 of AIRS database number 320 (NSW Current Acts) is the Animals Act, 1977, and this extract shows the first 3 articles from Chapter 2. The TITLE section is section number 1, SECT is section number 2, and LONGTITLE is section number 5. These are the only sections appearing in this extract.

The second extract is the Concordance file for a concordance of those 3 articles alone. In the real concordance for the whole Act, references to all the other sections of the Act would appear, and some words might have hundreds of entries.

Exercise on the concordance

To check for yourself how the concordance works, try the following exercises:

1 How many concordance entries are there for the word `1977'? What is the first one? Is is correct?

2 What is the concordance entry for the occurrence of `animals' where it occurs in the phrase `caused by animals'?

3 The concordance entries for the words `New South Wales' in the last paragraph have been omitted. What should they be?

Document

CHAPTER 2
ARTICLE 1
ANIMALS ACT, 1977, No. 25
#DATE 23:05:1979
Reprinted as at 23rd May, 1979
Current as at 31:12:1985 CLIRS
NOTES
(1) Animals Act, 1977, No. 25. Assented to, 13th April, 1977.

Note.--This Act is reprinted with the omission of all amending provisions
authorised to be omitted under s.6 of the Acts Reprinting Act, 1972.

ARTICLE 2
ANIMALS ACT, 1977 - LONG TITLE
LONGTITLE
An Act relating to liability for damage caused by animals.

ARTICLE 3
ANIMALS ACT, 1977 - PREAMBLE
SECT
BE it enacted by the Queen's Most Excellent Majesty, by and with the advice and
consent of the Legislative Council and Legislative Assembly of New South Wales
in Parliament assembled, and by the authority of the same, as follows:--

Concordance

Chapter Article Section Para Word

05 2 1 1 1 8
1 2 1 3 1 1
12 2 1 1 1 20
13TH 2 1 3 1 9
1972 2 1 3 2 25
1977 2 1 1 1 3
2 1 3 1 4
2 1 3 1 11
2 2 1 1 3
2 3 1 1 3
1979 2 1 1 1 9
2 1 1 1 15
1985 2 1 1 1 21
23 2 1 1 1 7
23RD 2 1 1 1 13
25 2 1 1 1 5
2 1 3 1 6
31 2 1 1 1 19
6 2 1 3 2 19
ACTS 2 1 3 2 22
ADVICE 2 3 2 1 15
ALL 2 1 3 2 10
AMENDING 2 1 3 2 11
ANIMALS 2 1 1 1 1
2 1 3 1 2
2 2 1 1 1
2 2 5 1 10
2 3 1 1 1
APRIL 2 1 3 1 10
ASSEMBLED 2 3 2 1 31
ASSEMBLY 2 3 2 1 24
ASSENTED 2 1 3 1 7
AUTHORISED 2 1 3 2 13
AUTHORITY 2 3 2 1 35
CAUSED 2 2 5 1 8
CLIRS 2 1 1 1 22

CONSENT 2 3 2 1 17
COUNCIL 2 3 2 1 21
CURRENT 2 1 1 1 16
DAMAGE 2 2 5 1 7
ENACTED 2 3 2 1 3
EXCELLENT 2 3 2 1 9
FOLLOWS 2 3 2 1 40
FOR 2 2 5 1 6
IT 2 3 2 1 2
LEGISLATIVE 2 3 2 1 20
LIABILITY 2 2 5 1 5
LONG 2 2 1 1 4
MAJESTY 2 3 2 1 10
MAY 2 1 1 1 14
MOST 2 3 2 1 8
NEW ******** OMITTED *********
NO 2 1 1 1 4
2 1 3 1 5
NOTE 2 1 3 2 1
OMISSION 2 1 3 2 8
OMITTED 2 1 3 2 16
PARLIAMENT 2 3 2 1 30
PREAMBLE 2 3 1 1 4
PROVISIONS 2 1 3 2 12
QUEEN 2 3 2 1 6
RELATING 2 2 5 1 3
REPRINTED 2 1 1 1 10
2 1 3 2 5
REPRINTING 2 1 3 2 23
S 2 1 3 2 18
2 3 2 1 7
SAME 2 3 2 1 38
SOUTH ******* OMITTED *********
TITLE 2 2 1 1 5
UNDER 2 1 3 2 17
WALES 2 3 2 1 28
WITH 2 1 3 2 6
2 3 2 1 13

Note that the words a, act, and, as, be, by, is, of, the, to are not concorded - they are common words.

Answers to the exercise

1 5 entries ;
article 2 chapter 1 section 1 paragraph 1 word 3 ;
yes, check for yourself!

2 animals 2 2 5 1 10

3 New 2 3 2 1 26

South 2 3 2 1 27

Wales 2 3 2 1 28

3 Improved retrieval and storage techniques

[Previous] [Next] [Title]