CA1276728C - Information retrieval system and method - Google Patents

Abstract

ABSTRACT OF THE DISCLOSURE

A computerized information retrieval system provides a break-down of the major and minor subject areas covered by a group of texts selected from a text-database. Each text has associated with it a set of descriptive keywords, which may or may not be identical to a list of the words in the text. A user enters a descriptive word, or words in some boolean combination, and the system selects from among all the texts those whose keywords which fulfill the logical test constituted by the user's search request.
A group of texts answering to the description having been found, the system organizes the texts into sub-groups defined by the keywords which the texts of each sub-group have in common, over and above those keywords which are held in common by virtue of all the texts conforming to the original search request. An additional method of analysis provides a measure of the degree of similarity between words or collections of words such as sentences. This provides a facility for searching for text whose keywords are similar to the words in a user's search request.

Description

~76~;~8 INFORMATION RRTRIEVAL SYSTEM AND METHOD

BACKGROUND OF THE I~VENTION
The present invention relates to an information retrieval system and method which analyzes and summarizes the information contained in a group of texts and identifies similar words and word collections.
"Information retrieval" is the process of selecting and presenting specific items from within a large and heterogeneous collection of texts, according to users' descriptions of the subjects in which they are interested.
Some information retrieval systems index all the words appearing in all the texts, others index "keywords" which are descriptors assigned to each text by the text's author or by someone else. In both cases the user who wants to find a text does so by asking for a search on a particular word, or on logical (Boolean) combinations of words, or on words with some maximum distance (or similar relationship) between them in the texts, etc. In addition to requesting a specific word or words, most systems allow the user to search for a character string; e.g., LEXIS* and DIALOG*.
A typical search request, on traditional systems, generates a long list or a large collection of texts all of which logically satisfy the search cri-terion, but only a small percentage of which will actually be of use. The user is forced to expend much time and much energy winnowing (searching) through the texts found by the system, to pick out those truly rele-vant to his needs.
This problem originates from the fact that theuser typically does not have EXACT knowledge in advance of how the subjects of interest to him will have been described.
*Trade Mark *If his description is very specific, he will lose information: anything relevant to his needs but described in a slightly different manner will not be found by the system.
*If his description is very general, many irrelevant texts will be found also, and the winnowing process will be costly, time con-suming, and tiring.

For this reason:
*On the level of office systems and personal computers, despite the proliferation of com-puters and the wide use of STRUCTURED data-bases, the use of personal and interpersonal catch-all text-based information retrieval systems is almost unknown -- the bother and the overhead involved in using traditional systems are too great to make the effort worth the trouble.
*On the level of massive public data-bases, most bibliographic information systems have attempted to solve the problem by limiting users to a predetermined vocabulary of accep-table keywords. (Users have a reasonable chance of quessing what their subject will have been called, since both users and authors are confined to that published list of keyword possibilities.) This solution has been workable but at a price:
(1) To be an effective user of such databases one must study and develop 3~ expertise in the use of the system.
They are, thus, inaccessible to untrained users, and inappropriate for casual use.

.~

(2) Because of their rigid structure, such systems are of limited use (and indeed are little used) in dynamic environments such as would be found, for example, in the case of an unstructured corporation-wide catch-all collection of information.

Two general types of information retrieval systems and methods currently in use are as follows:
(1) In a first method, once the information retrieval system (whatever their selection methodology) have isolated or identified the group of texts which satisfy the user's search criterion, the systems present the user with a count of the number of texts within that group, and the opportunity of sequentially reviewing the texts which are mem~ers of that group.
The user either looks through the texts them-selves, one by one, or looks through se~uential listings of some part of the information available about each text: that is, the user may choose to review a sequen-tial list of the titles of the texts, or abstracts of the texts, or lists of keywords of the texts, or the initial paragraphs of the texts, or the dates and ori gins of the texts, or some combinations of the above.
The user is then given some method of specifying (usually by number) those texts for which he wishes fuller information printouts, etc.
An example of this type of information retrieval system is the DIALOG information retrieval system.
DIALOG* provides a user with the number of records (texts) satisfying the search request. The user can then request that any or all of the records be displayed and/or printed in any one of a number of formats containing varying and differing amounts of information.
*Trade Mark 1~7672~3 (~) A second method is generally used when the number of texts presented by an initial search is too large, or the original search criterion was too general, to make it practical for the user to look through sequential listings to pick out the texts he wants. This method is essentially an extension of the original boolean search facility: the user can ask for additional searches to be made, and then can manipulate the additional lists of texts thus generated by requesting further lists to be created based on Boolean combinations of the preceding lists (e.g., the new list to include all the texts on list "A" and also on list "B" but to exclude any which appear on list "C", etc.).
An example of this type of information retrieval system is DIALOG*, where the user can make additional search requests, and create new lists of texts based on Boolean combinations of preceding lists.
Another example of this type of information retrieval system is the LEXIS* system wherein a user can modify his/her search request in an effort to narrow down the number of cases (texts) developed from the initial search request.
These methods, Boolean combinations of lists and sequential screening or printouts of the texts themselves or of some subset of the information available about each text, generally constitute the state of the art in information retrieval at this time, for the phase of the retrieval process extending between the point at which the retrieval system has identified a group of texts as being responsive to the search criterion, and the point at which the user chooses and is presented with the individual texts which he judges to be actually germane to his needs.
In addition to other advantages, the present invention solves the problems described above, by making it possible for the user to see at a glance a break-down ~Trade Mark of the -types of information contained in the texts selected by his initial request. From the generated display, the user can choose the texts which are rele-vant to his true interest both easily and quickly.
The present invention also relates to a system and method for identifying words in a target word list which are similar to a source word, and/or for iden-tifying phrases or sentences in a target population which are similar to a source phrase or sentence.
Computer programs are used in a number of con-texts to obtain words which are "similar" to some given source word, most notably in indexing and information retrieval programs and in spelling checkers. In indexing and retrieval programs, the purpose of such a search for "similar" words is to provide a more exhaustive list of terms related to the input word, such as plurals or forms modified by prefixes or suffixes.
In the case of spelling checkers, the purpose is to be able to make a suggestion as to the most likely word the user had intended, once a word is encountered which does not appear in the program's dictionary.
In the traditional and simplest solution to this problem, most often used in indexing and retrieval programs, the user specifies the exact nature of the relationship between the source word and the words being sought by means of "wild card" symbols, most typically the '?' '*' characters. In this protocol, the user instructs the program exactly which parts of a word he is interested in matching, and in which parts other characters may appear, the question mark '?' being used to signify any individual character and the asterisk '*' any sequence of characters. Thus, by way of example, the user would ask for "law" if he intended to find words like "laws", "lawyer" or "lawless". Or a search for "analy?e" could he used to locate both the American (with a 'z') and the British (with an 's') spellings of the word.

_ " _ In the case of spelling checkers, a more flexible approach is needed, since the user does not usually know that he has made a spelling mistake, nor does he know in advance the relationship between the way he thinks a word is spelled and the way it is spelled in fact. Most typically, spelling checkers locate "similar" words by first restricting the search to words beginning with the same letter as the misspelled words and then use a list of common spelling and typographical errors to find words which differ from the source word only by these letters.
An alternate approach used by spelling checkers is to convert the word to an approximate phonetic form, and then search a dictionary of such phonetic words, on the assumption that the user typically has a clearer idea of how the word sounds than of how it is spelled.
This last approach is usually quite effective at finding spelling errors, though it suffers from the drawback of being unable to deal with typographical mistakes. This technique is therefore quite commonly combined with elements of the previously mentioned appoach, in order to obtain a more comprehensive list of possible words.
Some information retrieval programs use the phonetic approach also: along with a regular index of words (or of keywords) in their textbase, they create a parallel index in which those same words are represented phonetically. Search requests are then converted to phonetic format and the attempt is made to locate the search words' phonetic translation in the phonetic index. An example of this is the COMPUMARK* system which is used in searching for trademarks.
Regarding "similar sentences", the state-of-the-art is more simply described. There are complex systems which actually parse sentences into their com-ponent parts of speech and analyze the semantic relation~
ships among those parts; however, the applicant is not *Trade Mar~
~ 6 -,~,. ~

~276~728 aware of any retrieval systems in which the sequencing of the words in a search request (as distinguished from the identity of the search-request words and the specified logical relationships among them) is used to influence the choice of the texts to be retrieved, or the ordering or ranking of the texts once they are found.

SUMMARY OF THE INVENTION
The analyzing and summarizing aspect of the invention makes explicit the inherent relationships among a group of texts with associated keyword descrip-tions, by analyzing the keywords held in common by subgroups of texts within the overall group. The inven-tion comes into play once a group of texts has been selected using standard search methodology -- at the point at which the user would either have to make further guesses as to how to narrow down his search cri-terion, or would be presentea with a sequence of texts that would then have to be "winnowed through."
The invention is a system and me~hod of ana-lyzing and of presenting the informational content of this group of texts, as a group. The user sees pre-sented on a display medium (screen) the equivalent of an annotated "TABEE OF CONTENTS," organized as a standard outline or in some similarly graphic format, analyzing that group of texts into major subject areas, sub-categories, sub-sub-categories, etc. Each "TABLE OF
CONTENTS" outline is dynamically generated in response to specific search requests, and constitutes a kind of "birds'-eye view" of the contents of the textbase in that subject area at that time. For the user looking for a specific kind of information of which he has only a general description (thatls the typical case), a glance at the table of contents, a matter of seconds, usually suffices to eliminate from consideration most of the irrelevant material. Relevant sub-categories ~ .

1~767~8 usually are immediately evident. If necessary, the user can pick out for further analysis (in one implementation just by moving the cursor on the screen) a much-reduced group of texts (one of the categories presented to him in the table of contents) and repeat the analysis pro-cess, creating another table of contents, this time of the sub-category. One or two iterations will usually suffice, even when starting with a group of hundreds of texts, to ~et to a table of contents in which most descriptions will be of individual specific texts rather than of groups of texts. With an appropriate command;
e.g., by moving a cursor and pressing a key, the user chooses the specific text or texts he wants to see according to the descriptions he sees on the table of contents, and with an appropriate command; e.g., a keystroke, brings those texts to the screen or sends them to be printed.
It is anticipated that this technique will lead to an extension of the use of information retrieval into areas where it had not been convenient or practical to use it before, some examples being as follows:
(a) Specific information can be located much more rapidly than had been possible using prior tech-nology. Experience so far has shown that the time needed for finding specific information in a large text-base is on the order of 10~ of what it normally would take, and the process is far more agreeable.
(b) It is practical to search for information whenever the user knows some general characteristics of what he's looking for, even though he has no idea how that information may have been specifically described in the textbase.
(c) It is practical to maintain and use large heterogeneous collections of textual information, and to do searches to find specific elements of that infor-mation, without limiting authors to a predetermined lexicon of "keywords." This means that users (e.g , in _ 8 lZ767;~8 a corporate environment) can choose keywords spon-taneously and still succeed in finding relevant infor-mation among each others' entries.
(d) "Browsing" the textbase becomes a pleasant and meaningful operation, quite different from paging through texts or reading the thesaurus, which are the only "browsing" techniques available in traditional technology.
In one implementation of the invention, the information retrieval system is coupled with a word-processor, for convenience in entering texts into the textbase, and with an output screen presenting the results of the above analysis in traditional outline format. The output screen shows the categories and sub-categories of subjects found to be included in the texts selected as a result of the original search request, to any desired level of detail. The user moves the screen cursor to point to a category on the outline for which he wants a more detailed break-down, and the process continues until individual texts are being referenced on the outline. Then by pressing a key the user can direct the system to send the chosen text to the printer or bring it to the screen.
The significance of the invention is that the amount of time needed for the user to isolate texts of interest to him, from among groups of texts which satisfy his initial search request but are in fact irre-levant to him, is reduced by a large factor. The infor-mation retrieval process is made more convenient thereby; it is practical using this system to find specific texts with only the most minimal initial infor-mation as to how they may have been keyworded; and various practical constraints which have restricted the ways in which textbases needed to be organized in order to guarantee that stored information could be found again, can be relaxed.

1~76728 The invention also relates to a pr~cess which enables the computer to locate `'similar" words in a manner more flexible and more exhaustive than any currently used technique, so far as we know. In par-ticular, the invention does not require any specifica-tion by the user as to the relationship between the input word and the target words, nor does it rely on phonetic translation or any restrictive list of typical mistakes. The invention rather makes use of the actual structure of the word itself, and searches for words which have a similar structure or which include a similar structure as part of a larger structure. The invention is therefore able to locate a far more comprehensive list of "similar" words than is the case with other techniques.
The structure of the input word is analyzed in terms of groups of letters, starting with letter pairs and working up to larger groups, and accords to any word in the target dictionary which contains these letter groupings a number of points determined by the size of the group and/or its location in the word. Words which are given a large number of points by the process are then presented to the user, in descending order of the number of points allocated, for his selection.
In the case of searching for similar sentences rather than similar words, the technique is identical, except that groups of words, rather than groups of let-ters, are compared. One field of application for this invention is in information retrieval systems, where the user presents his search request in the form of a phrase or sentence, and texts are selected from the data-base and/or prioritized, according to the scores achieved when either their descriptions (keywords, title, abstract) or the texts themselves are evaluated according to this method. Since in information retrieval systems the typical search request finds many texts which are, in fact, irrelevant to the user, the ~ ~76q28 invention, when employed to automatically winnow and/or prioritize texts can save time and trouble for users of the system.
These and various other advantages and features of novelty which characterize the invention are pointed out with particularity in the claims annexed hereto and forming a part hereof. However, for a better understanding of the invention, i. advantages, and objects obtained by its use, reference should be made to the drawings which form a further. part hereof, and to the accompanying descriptive matter, in which there is illustrated and described a preferred embodiment of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS
In the drawings, in which like reference numerals and letters indicate corresponding parts throughout the several views, Figure 1 is a schematic view illustrating a stand-alone computer system wherein the present inven-tion might be utilized;
Figure 2 is a schematic view illustrating acomputer terminal system interconnected to a remote host computer system, the present invention being implemented in either or both computer systems;
Figures 3A-G are schematic views illustrating file structures of an embodiment of an information retrieval system and method in accordance with the prin-ciples of the present invention;
Figure 4 is a schematic illustrating the addi-tion of text to a textbase in accordance with the prin-ciples of the present invention;
Figure 5 is a schematic view illustrating searching the textbase of Figure 2 in accordance with the principles of the present invention;
Figure 6 is a schematic view illustrating locating of texts which match the search request;

~276728 Figur~ 7 is a schematic view illustrating ana-lyzing the texts found in the search;
Figure 8 is a schematic view illustrating pre-sentation of the analysis to a user.
Figure 9 is a view illustrating a sample pre-sentation of results at a display media;
Figure 10 is a schematic view of the process shown in Figure 7, but illustrating various additional features of analysis;
Figure 11 is a schematic view illustrating automatic keyword modification to groups of text;
Figure 12 is a schematic view illustrating the preparation of the search request prior to searching the textbase;
Figures 13A-B are logic flow designs of an embodiment of the present invention providing the abi-lity to search for similar words;
Figure 14 is a schematic view illustrating an embodiment of the present invention for calculating the degree of similarity between two words;
Figure 15 is a schematic view illustrating the calculation of the-similarity between two phrases or sentences or collections of words; and Figure 16 is a schematic view illustrating the calculation of point scores used in calculating the similarity between two phrases or sentences or collec-tions of words.

DETAILED DESCRIPTION OF A PREFERRED
-EMBODIMENT OF T~E PRESENT INVENTION
One implementation of the invention is a program written in the C language, with some sections written in Assembly language. The implemention to be described runs on the IBM-PC and compatible micro-computers. As generally illustrated in Figures 1 and 2, the program is in principle easily transportable to a large family of micro, mini, and main-frame computers, ., ~

~X767X8 and can be used in a multi-user environment. For example, as illustrated in Figure 1, the program is loaded into the memory of a computer system 30 powered by a suitable power supply 31. The computer system 30 will include a user input device 32 such as a keyboard and/or mouse. In addition, the computer system will preferably include a storage device 33 for storage of text material. A printer 34 will be provided for hard copy printout of results and a display terminal 35 will be provided for display of the program analysis at the display terminal. As illustrated in ~igure 2, the com-puter system 30 might be interconnected to a host com-puter system 36 by any number of different methods such as by telephone lines, a direct connect via a serial interface cable, a radio frequency (RF) interconnection, etc. The program of the present invention might be uti-lized in the computer system 30 and/or the host computer 36. In a multiuser environment, the program might be utilized from a dumb terminal 37 interconnected to the host computer 36.

File Structure The file structure of an embodiment of a program in accordance with the principles of the present invention will now be described.
As illustrated in Figures 3A-G, the textual information itself and the indexing information necessary to access it are kept in seven data files.
A text file 42 contains variable-size records 42a of the texts which have been saved in the textbase, there being a record for each text in the textbase.
This information is ordinarily all kept in one file, though the possiblity exists of splitting it into several smaller files if the physical limitations of the computer system being used prevent a single file of large enough size being maintained.

~2767X8 ~ text pointer file 44 contains information as to where each individual text is located within the text file 42 itself. Space in the text file 42 is allocated as it becomes available (by old texts being deleted or updated); an ordered list is therefore necessary in order to locate the desired text at any time. Each record 44a of the text pointer file 44 includes a Text Number field a signing a unique number to each of the texts, a Location field specifying the text location, a Size field specifying the size of the text, and a Date field for providing information as to the date each text was last modified, for use when searching for texts which meet specific date criteria.
A keyword file 46 contains variable-size records 46a listing every keyword which has been defined in the textbase. The keywords may have been defined in several different ways; for example, the author may define the keywords as the text is entered, the keywords may be defined automatically through the use of an automatic keywording feature as described below, text down-loaded from a commercial data base may have keywords already predefined, etc. A keynumber is allo-cated to each keyword on the basis of its position in the keyword file 46.
An index of which texts contain which keywords i5 kept in text keyword file 48. This file contains a variable-size record 48a for each text in the textbase, the entries being in the form of the number of the text being referred to, followed by a list of the keynumbers of the keywords associated with that text, followed by an end marker to indicate the end of that list and then the entry for the next text.
A text index file 50 includes a record SOa for each text providing an index to the location and size of the entry in tne text keyword file 48 for each text.
Free key file 52 and free text file 54 are lists of available space in files 48 and 42, respectively, , . .

~2'76728 so that spac~ isl those files can be reused as texts are deleted or updated. The files 52 and 54 and their associated records 52a,54a have a structure similar to that of the text index file 50.

Adding Texts To The Textbase Figure 4 illustrates the process by which texts are created and saved in the textbase. Texts might be created at 56 by a word processor function associated with the program of the present invention, or by "importing" texts from files which have been created by other programs, such as by other word processor programs or texts developed from a data base search request. The user defines the keywords which he wants to use to describe that text, either by marking them in the text itself at 58 or by entering the keywords in a separate keyword list at 60. Keywords which the user has marked in the text are automatically scanned at 62 and added to the keyword list; both the text itself and the keyword list are available for editing throughout this process.
The same process is used to modify keywords defined in a text which has previously been saved in the textbase; the text is retrieved from the textbase in the normal manner (see below), and is then available for editing in the word processor.
When the user has finished entering or modifying the text, he enters a command; e.g., presses a key, to save it at 64. A number is allocated to the text at 66, based on the next available position in the text pointer file 44. The keyword list for the text is then converted to keynumbers at 68, either by finding the existing keyword in the keyword file 46 or by adding a new keyword to the file 46. The position of a keyword in the keyword file 46 corresponds to the keyword number assigned to that keyword. The text itself, together with its keyword list, is added to the text file 42 and _ 15 _ ~;7 ~767~8 the textnumber and list of keynumbers added to the text-keyword file 48. The index files 44, 50, 52, and 54 are then updated with the appropriate information.
In the case of saving a text which previously existed in the textbase the process remains substan-tially the same; it is saved using its previous text-number rather than allocating a new number, and the old information on keywords which describe that text is deleted from the text keyword file 48 and replaced with the new information.

Searching The Textbase The procedure by which the user searches the textbase to find a particular text or texts is illustrated in Figure 5. The user initially enters his search request at 72, in the form of the keyword or keywords which describe the information he is looking for. Boolean combinations or keywords may be used in the description to logically describe the set of texts which is being searched for. If the user has asked that similar words or pre-defined "equivalent" words be substituted into his search request, the substitution is made at this time. (This process is described below and in Figures 12 through 14.) The program then searches the textbase at 74 to locate all texts which match the search request, as is shown in further detail in Figure 6. At 76, the program analyzes the set of texts which are found to satisfy the search request as shown in Figures 7 and 10, and at 78, the program displays the results of this analysis at the user's display terminal (screen) as shown in Figure 8.
As illustrated in Figure 6, texts are selected by scanning the text-keyword file 48 for each keyword in the search request, and building a list of the texts which match the request. This list is constructed ~y taking from the search request each keyword in turn at 80, looking up its keynumber at 81 in the keyword ~ ~767Z8 file 46, and then scanning the text-keyword file 48 to find all texts which contain that keynumber at 82. The numbers of the texts are added to the list as they are found at 82. This list is then combined at 83 with the list of texts which had been found by previous itera-tions of this process, which dealt with keywords men-tioned in earlier parts of the keyword request. The lists are combined according to the logical operation specified by the user. At 84, the process is repeated, the list produced by each successive iteration being combined with the list created by all previous itera-tions, until all the keywords in the search request have been dealt with. At 85, the program checks if the user has requested that the search be limited to texts created (or modified) within certain date limits. If the user has imposed no such date limits, at 86 the text selection is terminated. If the user has requested date limits, at 87 the listed texts are checked against dates stored in the text pointer file 44 and only those texts whose creation/modification dates fall within the limits are retained.
The program then analyzes the set of texts which has been found and presents the results of that analysis The process by which the analysis is carried out, and the manner in which the results are presented, will now be described.

Analyzing The Texts Found In The Search The program analyzes the set of texts which has been found to match the initial search re~uest, by means of the process shown in Figure 7. First, at 88 the program obtains the list of ~eynumbers associated with each text in the set. These lists are obtained by reading them from the text-keyword file 48. The lists for each text are then scanned at 90 and the number of texts in which each keynumber occurs is counted in order to identify the "criterion key" -- the most fre~uently ~276728 occurring keynumber, i~e., the keyword which is associated with the greatest number of texts in that set.
The set of texts is then divided into two subsets at 92; the "right-group" containing all texts which are described by the "criterion key", and the "down-group" containing those texts which are not described by the criterion key. The "right-group" is thus a list of all the texts in the current set which include among their keywords the "criterion key"; all remaining texts from the current set are listed in the "down-group".
As will now be described, these two subsets are then in turn analyzed by the same process of finding the most commonly occurring keynumber and using it to split the set of texts; the two sections of the program at 90 and 92 being performed recursively until all the texts have been analyzed, or until such time as a deci-sion is reached not to continue analysis further in either the "right" or the "down" direction.
If at 94, a decision is made to continue the analysis further in the "right" direction, then note is taken at 94a of the identity of the current group, as it will be the "parent" group for the forthcoming recursive iteration of the process. Then at 94b, the sub-group which had been the "right-group" created at 92 is marked as the new upcoming "current" group, and note is taken at 94c that it was originally created as a "right-group"
at 92. The analysis routines now invoke themselves recursively; that is, handling of the previous current group (now the parent group) is interrupted and the system begins the analysis of the new current group at 90. ~he full analysis is thus a set of nested pro-cesses; for a group of text to be fully analyzed, the analyzing routines first split the initial group into two sub-groups, and then invoked themselves to handle the further analysis of each of the resulting sub-groups. Thus, the process proceeding to handle the new current group at 90 and at 92 may again be interrupted .~

~276728 at 94 to handle yet another right group produced at 92during this second iteration, and/or at 96 to handle analysis of the "down-group" produced at 92 during this second iteration. The procedure, if the iteration is interrupted at 96, is similar to that described above at 94a, 94b, and 94c: note is taken at 96a of the identity of the current group, which will be the parent group for the upcoming iteration. Then at 96b the sub-group which had been the "downgroup" created at 92 during the current iteration is marked as the current group for the upcoming iteration, note is taken at 96c that it had originally been a "down-group" and it is processed starting at 90.
During any iteration, if a decision at 94 is not to analyze further right, and the decision at 96 is not to analyze further down, then at 97 a check is made whether the current group has a parent group, (since the existence of the parent group means the existence of a group whose processing had been interrupted at 94 or 96). At 97a, a check is made as to whether the current group had originally been a "right-group" or a "down-group", this being a way of identifying the point at which processing of the parent group had been inter-rupted. If the current group had been a "right-group", its parent group (noted at 94a) is reidentified at 97b as the current group, and its processing is taken up at 96. If the current group had not been a "right-group", then it had been a "down-group". Its parent group noted at 96a is reidentified as the current group at 97c and processing of this reinstated current group continues at 97. ~hus, the process of analysis having been inter-rupted potentially numerous times for the analysis of sub-groups and sub sub-groups eventually completes all the interrupted analyses until eventually a parent group is reinstated as the current group, which was the origi-nal group with which the whole analysis procedure was begun. When th~ analysis of this group proceeds to 97, ~2767Z8 it will be founA to have no parent: group, and the analy-sis procedure terminates.
In this way every sub-group of the original group of texts is analyzed to the desired depth and a "tree" built out of the original list of texts. This tree is an analysis of the relationships among the various texts in terms of the keywords which describe them; it groups related texts together according to the similarities in their subject matter and locates all the texts in a structure of headings and sub-headings.
At each node of the tree, the list or node in the "right" direction defines the texts which belong to the largest category from the set of texts which was input to the node, and the list or node in the "down"
direction defines those texts not included in that largest category. Starting at the root node (the list of texts generated by the user's original search request) and reading down from node to node, provides a Listing of the major categories into which the original group of texts has been divided.
This listing is automatically sorted into "order of importance" through the above procedure of selecting the successive "criterion keys"; the larger the group of texts described by any particular criterion key, the closer it will be to the top of the list. The tree, then, provides a break-down of the original list of texts into its various subject matters, and can be extended to any desired level of detail.
Control of the analysis 94, 96 is achieved either under interactive user control or automatically on the basis of the number of texts already found and displayed. In automatic mode, analysis to the "right"
(that is, more detailed analysis of a group of texts which are described by the "criterion key") is ter-minated at 94 either when all the texts in the set have been shown, or when the depth of analysis of that set is such that further analysis would take up too much space, ~276728 making it impossible to show the "down-list" within the limits which have been set for the number of lines of analysis to display. Analysis "down" (those texts which are not described by the current "criterion key") is terminated at 96 either when all texts have been shown, or on reaching a predetermined limit as to the number of lines to show.
The user may control the analysis process by setting in advance the number of display lines at which he wishes automatic analysis to stop, or interactively by at each stage in the process deciding whether to further continue analysis either "right" or "down", and how far to continue it in either direction. In addi-tion, as specified below, the user may invoke various additional features affecting the procedure of analysis as generally illustrated in Figure 10.

Results Of The Analysis As generally illustrated in Figure 8, the results of the analysis procedure described above is presented to the user as a screen display, indicating the groups of texts which have been found and their relationships to each other, in the form of a "table of contents" of headings, sub-headings, and tests.
The process by which this table of contents is created is illustrated in Figure 8. First, the descrip-tion of the original search request is displayed on the screen at 98. Then the first node, or "trunk" of the tree, (being the information provided by the analysis of the first group of texts to be analyzed at 90) is referenced at 100 and the keywords which describe it (the criterion key, and any other keywords which are common to all the texts of the group) are put on the screen at 102. The existence of any texts which that node completely describes (that is, texts all of whose ~eywords have by now appeared on the screen) is then indicated on the screen at 102 by using an arrow symbol ~ - 21 -~76728 to represent ~hem. Non-printing codes which include the text's text-number are embedded in the table of contents at this point. These codes are used later, if the user asks to see the text whose existence is indicated by the arrow displayed If there is at this time no right node (because the user, controlling the analysis interac-tively, chose not to split the group, or because a pre-set maximum depth of analysis had been reached), yet the group does still contain texts which have not yet been completely analyzed (i.e., texs some of whose keywords have not yet appeared on the screen), then this fact is indicated by showing the number of such texts in brackets; e.g., "(8)". In this case, non-printing codes are embedded in the table of contents giving the loca-tion in memory of information about this node/subgroup, including the list of texts belonging to it. This information is used later if the user asks to "expand"
the analysis of this group's texts, or to perform some other manipulation on the texts of this group.
At 104, the program then checks if there is a "right-node" associated with the current node (such a right node will have been produced by the analysis if there is room to expand further to the right in the outline, and if there are still texts with unexamined keywords in the node). If such a "right-node" exists, the count of how far to indent the next line on the screen or printer is increased by one at 106.
At this point, the routine we are describing invokes itself recursively. ~he handling of the recursive process ~104a,b,c) parallels that described above for 94a,b,c as the handling of 108a,b,c follows that of 96a,b,c. Indeed, the entire procedure described in Figure 8 para~lels that described in Figure 7, with the difference that Figure 7 describes the splitting of the groups of text into sub-groups (at 90,92), and Figure 8 describes the display of information about each group at " .

~7~76728 102 and controls the level of indentation of the display lines at 106 and 110. Control of the return from recur-sive iterations at 112, 112a,b,c parallels that described above for 97, 97a,b,c.
Thus the transition from 106 to 100 in Figure 8 is a recursive invokation of the routine being described.
Without the routine having completed its activity, the "right-node" is now designated as the current node to be handled 104b (the node whose processing is interrupted being referred to as the "parent~node") 104a, the level of indentation on the display is incremented 106, and the very same routine starts out "from the top" handling the current node (which had been the right-node) as if it were being invoked for the first time. Thus, the routine described in Figure 8 invokes itself; while still in the middle of handling the root node, it calls itself to handle the right-node.
The new current node is then handled as described, including the handling of its own right and down nodes, until the process runs to completion at 112. At 112 there are unfinished nodes to be handled, at 112a this node's parent node is seen to have been a right node, at 112b it is reinstated as current node and its processing continues at 108, which is just after the point at which handling of the node had been interrupted in order to handle its right node.
Next, the progam checks whether a "down-node"
exists at 108. If so, it is identified as the current node (without changing the indentation), a processes similar to the one just described is undertaken at 108a,b,c, and the routine invokes itself again 100. Thus, handling of the parent node is again suspended while the down-node (now the current node in the new invokation) is handled. When work on the down-node (which includes work on any of its subordinate nodes) reaches 112 and 112a, the parent node is reinstated as current node at 112c, and the level of indentation used (at 102) in creat-in~ ~isL)lay lin~s is reduced by one at llO. Since, in the example we have keen running through, the node which is now the current node was the original "root"
node, at 112 the display process terminates at 115.
Thus, processing of the root-node (the node first supplied by the original text search) is interrupted first to process the right-node, and then to process the down-node. Each of those processes may in turn be interrupted to process right-nodes and down-nodes, each of which may in turn be interrupted, etc.
Each time that the processing of a given nodeterminates (when there is no further right-node and no further down-node to be handled) the program checks at 112 if there are unfinished nodes to process. If such nodes exist, at 112a control is returned to the parent node from which the routine was invoked, and the processing picks up where it left off. In the case of the root-node, there is no parent-node, and the process ter-minates at 115, the whole table of contents having been displayed.
Illustrated in Figure 9 is an example of such a screen display. The first-line is a heading indi-cating the search request which created this analysis.
The remainder of the display represents, by showing the successive criterion keys as headings, the results of the analysis in the form of an originated "table of con-tents" of the section of the textbase under analysis.
In this "table of contents", lines ending with an arrow, such as line I.B.2, represent the presence of a text which includes only the keywords shown in that line and in the headings above it. In the case of this example, a text has been associated with the keywords "fruit", "oranges" and "jaffa". Analysis right on this text has been completed. If there were more than one text with these keywords, a series of right arrows would be shown on the line, one for each text.

~76728 Lines in the table of contents with a number shown in brackets, such as line I.A.l, indicate that there are that number of texts including the keywords shown in that line and in the headings above, as well as other keywords, and those texts are not shown indivi-dually in this analysis (i.e., analysis "right" has been terminated at this level).
Line I.C. above shows that there are other categories of texts not included in this table of con-tents (i.e., analysis "down" has been terminated at this point).

Using The Table of Contents The user can either review the texts indicated by the analysis, or ask for a further "expansion" of a group of texts which have not been fully analyzed. The user moves the cursor up and down on the screen to point at the text or group of texts he is interested in; and then presses a key to request that the text be displayed by the word processor or that the group be expanded.
If a text is to be displayed, its number is taken from the non-printing codes embedded in the table of contents. That number corresponds to an entry in the text pointer file 44, where the location of the text itself (within the text file 42) is indicated. The text is read from the text file and passed to the word pro-cessor for reading, editing, or printing.
If a group's analysis is to be expanded, the program refers to the non-printing codes embedded in the table of contents to find the location in memory of the list of texts and other information associated with the group. The information is then passed to the analysis and display routines previously described (Figure 7 and 8). This new analysis is presented in a new screen display, to be used in the same way as the "parent"
analysis; the user can continue to "expand" any group until 1~767X8 he finds and loads the text he is searching for, or can at any time return to a previous "parent" table of con-tents to look at a different group of texts.
Further information might be provided to the user by a special header which appears at the top of the screen whenever he stops moving the cursor on the table of contents; this header indicates the list of keywords describing either the text or the group of texts which that line represents. In addition, screen highlighting might be used to indicate that the cursor is pointing at a specific text, or to indicate all lines of the display which are contained in the group referred to by the cursor.

ADDITIONAL FEATURES
Figure 10 illustrates the basic text analysis procedure of Figure 7 with additional features being present for enabling the user to modify the basic text analysis.

Keyword Manipulation During Analysis It is possible to "hide" specified keywords so that they are removed at 88a and do not appear in the analysis at all, to "ignore" keywords at 90a so that they are shown in the results of the analysis but are never used to split the set of texts, or to declare cer-tain keywords as "equal" to each other and substitute therefor at 88b so that they are treated as identical for purposes of the analysis.
Hiding keywords can be useful in cases where some group of keywords, which would otherwise influence the display, are irrelevant for a particular purpose at hand. If the user has asked for words to be "hidden", then at the time that the program obtains the lists of keynumbers associated with each text at 88 by reading them from the text-keyword file 48, the lists are compared to the list of words to be hidden. Keynumbers found ~;
.~,._, on the "hidden" list are simply skipped at 88a, not included in the keyword lists which are subsequently used for the analysis.
Making keywords "equal" to each other is useful in cases where disparate categories are equivalent with r~spect to a particular task at hand. One frequent case, in particular, is that in which several different keywords have been used (perhaps by different users of a common textbase) to describe what is in fact the same category of information. Words on the "equal" list are arranged in groups of words which will be made "equal"
to each other-. If the user has asked for words to be made "equal" to each other, then at the time that the program obtains the list of keynumbers at 88 by reading them from the text-keyword file 48, the "equals list" is scanned with each keynumber read from the file. Whenever a match is found, the keynumber which headed the group of equivalent keys on the "equals list" is retained, in place of the number which was actually read from the file at 88b. Thus, the user has effectively changed categories, and potentially combined categories, for the purpose of the current analysis, although the files themselves remain unchanged.
Words which the user has specified are to be "ignored" are included in the lists unchanged, but such a word is never allowed to become a "criterion key". If the user has supplied a list of words to be "ignored", then each time a criterion key is chosen at 90, the chosen keynumber is compared to the list of numbers of keys to be ignored at 90a. If the chosen key is found to be on that list, then it is disallowed as a criterion key at 90b, and the most popular key not on the "ignore"
list is chosen as criterion key in its stead. This allows such words to appear on the display without affecting the manner in which sub-groups are defined.

~76~Z8 User Supplied Criteria During Analysis If the user is controlling the analysis interactively and indicates at 88c a desire to specify the criterion key directly, a list of all the keywords associated with the texts in the current subgroup (available from the procedure at 90) is placed on the screen. The user enters his choice for the criterion key at 90c, that word's keynumber is found by scanning the keyword file 46 (the key number is the word's posi-tion in that file), and that number becomes the cri-terion key according to which the group of texts is split into "right" and "down" subgroups at 92. This input of a criterion key by the user at 90c then repla-ces the counting operation at 90 and is used to split the group into two groups at 92.
The user may, in fact, supply at 90c not just one word, but a logical combination of keywords, thus creating a "local" Boolean search request, which is then ! analyzed just as was the original main search request, as described above and in Figure 6. The current group of texts is then split into two groups, those satisfying the "local" Boolean search request going to the "right"
group, those which do not satisfy it going to the "down"
group.

User Supplied Criteria In Outline Format It is possible for the user to specify in advance the criterion keys or local search requests which are to be used for some or all iterations of the analysis procedure (i.e., for splitting some or all of the resultant sub-groups). The user provides this specification in traditional outline format. The first line of the outline becomes the "current line", and provides the criterion (word or logical combination of words) for the first analysis at 90d, which then proceeds as described in the preceding paragraph at 92.
If the line following the current line on the outline is ~2767Z8 indented further to the right, then it will be used (and become the current analysis, if any, is in turn analyzed.
The next line of the outline which has the same level of indentationas the current line, if any, provides the criterion for analysis (and becomes the "current line") when the down-group created by the current analysis, if any, is in turn analyzed. When the right and down groups are in turn analyzed, the lines of the outline which had been respectively selected are then treated as the "current line", and further lines are identified for use in analyzing the resultant new right and down groups. Thus, input of a criterion key from a user-supplied outline at 90d then replaces the counting operation at 90 and is used to split the group into two groups at 92. Any group being analyzed for which no line of the outline has been designated to provide the analysis criterion, is split in either the usual automa-tic or the usual interactive manner, as described above.
Lines from the outline supplying criteria in the analysis of groups of text at 90d are then repro-duced as part of the display lines generated at 102.
The result of this procedure is that the user provides an outline of his subject matter, and the system fills that outline with references to whatever texts in his textbase are relevant to each part and sub-part of the outline.

Text-Scanning as Criterion During Analysis An additional method of controlling the analysis is to split the group according to the success or failure of a scan for the presence or absence of words (or pairs or groups of words with a specified degree of contiguity) within the texts themselves (rather than checking for words within the keyword list) at 90e.
This scanning operation at 90e then replaces the counting operation at 90 and the results are used to split the group into two groups at 92. This provides a ! 29 ., .~

12767~8 facility for the use of what are called "full text searching techniques" (information retrieval techniques not based on designated keywords) in the context of a retrieval system whose major functions are based on the use of keywords. Thus, for example, one could search for a designated pair of words occurring within a same paragraph by scanning for the words within the restricted group of texts which has been isolated at some intermediate stage of the analysis.

Mathematical Calculations Used as Criterion During Analysis An additional method of controlling the analysis is to split the group of texts into two groups in a manner dependent on the results of a mathematical calculation performed on a number or numbers found either within the text or among the text's keywords at 90f. The numbers to be used are identified by having a particular position on the text's keyword list, or by having a particular position with respect to some designated word found on the keyword list, or by having a particular positional relationship to some designated word found in the text itself. This numerical calcula-tion at 90f, either on the data found in the keyword list at 88 or on the data found by scanning the text at 90e, then replaces the counting operation at 90 and the resu]ts are used to split the group into two groups at 92. As an example, a group of texts might be split into a right-group and a down-group according to their suc-cess in fulfilling the criterion "cost is greater than 100", where the number to be inspected is either whatever keyword follows the keyword "cost" on the text's keyword list, or whatever word follows the word "cost"
in the text itself.

Similarity Scoring as Criterion Durinq Analysis An additional method of controlling the analysis is to inspect the sequence of the keywords for each text (as read in from the text-keyword file 48 at 88) and to split the group of texts into two groups according to criteria dependent on the order of the keywords at 90g. The process by which this prioritiza-tion takes place is described below and in Figures 15 and 16. This process then replaces the counting opera-tion at 90 and the results are used to split the group into two groups at 92. One use of such an analysis is to provide for prioritization of a group of texts according to the degree of similarity between the set of that text's keywords, treated as a phrase or sentence, and the user's search request, treated as a phrase or sentence, according to the method of measuring simi-larity described below and in Figures 15 and 16. The same method can be implemented in a comparison between the search request and the texts themselves, or portions of the texts, or non-keyword information (e.g., titles, abstracts) associated with the texts.

Automatic Keywording One embodiment of the present invention enables users to allocate keywords automatically to texts created by the word processor or texts imported from outside sources.
The procedure by which keywords are allocated automatically will now be described, referring generally to Figure 11. The user designates a group of texts by pointing to it on a table of contents at 200, as he would if he were asking to further expand that group.
He then presses a key to indicate his desire to alter the keywords of that group (which can, of course, include the entire textbase). He supplies a list of keywords to be added ("add words"), keywords to be ~276728 eliminated wherever found ("delete words"), and words to become keywords if they are found within the texts them-selves ("scan words") at 202. Among the scan words, the symbol "**" is understood to mean that all keywords in the keyword file 46, or all keywords in the keyword file 46 with the exception of a designated list of words, are to be used as scan words.
The implementation then uses the normal text handling routines to load the texts one by one at 204, 10 the keywords list is read, add words are added to it at 206 and delete words are eliminated from it if found at 208. The text is then scanned on a word by word basis, the words of the text being compared to the "scan words" at 210. When a match is found, the scan word is added to the keyword list at 210. By user-selected alternatives, when a match with the scan word is found a) a preliminary check can be made to find whether some designated additional scan word(s) are found within a designated proximity, and only if so is the scan word(s) 20 added to the keyword list, and b) when a match with a scan word is found, some other designated word can be added to the keylist. When the scan is completed, if any changes have been made at 212 in the keylist the files are updated at 214 using normal text-saving proce-dures. At 216, a che::k is made if there are any more texts to be handled. If there are no more texts, the routine terminates at 218, otherwise the next text is handled starting at 204.
In the case of automatic keywording of an 30 "imported" file, the implementation reads that file from the disk, creates a new empty keyword list for it, modi-fies that keyword list as in the above paragraph, and then saves it in the normal fashion.
The result of the "**" scan is particularly noteworthy: using it, all texts in the textbase can be keyworded retroactively, so that any mention made (in a text) of a subject which subsequently becomes a lX76728 "category" (i.e. is keyworded somewhere in the textbase) will subsequently be recognized and found when the sub-ject is searched for. Similarly, the "**" scanning of an imported text assures that any subjects mentioned in the imported text which have already been keyworded somewhere within the textbase, will be keyworded in this imported text also~

Automatic Modification of Search Request by Substitution of "Equivalent" Words As illustrated in Figure 12, in the process of the preparation of the search request in Figure 5, one embodiment of the invention enables the user to cause the search request he enters at 300 to be modified auto-matically in several ways.
At 301, the program inspects the search request to determine whether the user has indicated (by means of an appropriate symbol) that "equivalent" words or combinations of words may have been previously defined to the system. (This technique is useful both to permit a single word to represent a complex and oft-repeated search request, and to provide the means for providing automatic "equivalence" between the habitual keyword vocabularies of different users of a common textbase system.) If a word in the search request is proceeded by such a symbol (in this implementation a dollar sign was used), then the word (including the proceeding dollar sign) is searched in the keyword file 46. If found at 302, the text keyword file 48 is scanned to locate the associated text. The text, if one is found, is not displayed through the normal display process, rather the entire text is taken to be a redefinition of the word which had been preceeded by the dol]ar sign, and is substituted for it in the user's search request at 303. The search request is then again made available to the user for further editing at 300 or for him to _ 33 _ ~276728 repeat his command that the search request be processed.

Automatic Modification of Search Request by Substitution of Similar Words In similar manner, another symbol, the asterisk, is employed when the user wishes his search request to be expanded to include all the keywords in the system which are similar to words in his search request. At 304 the program inspects the search request to determine whether any of the search words begin or end with an aQterisk. If so, the similarity checking routines (described below and in Figures 13 and 14) are invoked to find all the keywords in the keyword file 46 which are similar to the given word in the search request. If a list of similar words is found at 305, the words are separated by the word "or", the list is enclosed in parentheses, and the whole is substituted for the original word at 303 in the users search request. Here too, the modified request is again pre-sented to the user for further editing, or for his com-mand to proceed with the processing the request 300.
Once all requested substitutions have takenplace, processing proceeds to the locating of texts matching the search request description.
Illustrated in-Figures 13A-B is a process in accordance with the principles of the present invention, which enable words in a target word list to be identi-fied which are similar to the key words. Indeed, as previously discussed, this aspect of the invention will have application in several other uses, such as spelling checkers and the like. In one implementation, the invention is part of an information retrieval system, where it is used to find key words related to words from a search request provided by the user (whether similar words, or misspellings of the same word).

The source word, i.e., the word to which we intend to find similar words, is inpu~ to the process at 342. If it has a suffix, a second copy of the word is made without the suffix 344,346 and this new version of the word is kept for later use.
The program now fetches a word from the target dictionary at 348, and the original word (including suf-fix) is compared to the target word at 350 by the pro-cess described below and in Figure 14. If that comparison yields a score of zero or less than zero at 352, the program then checks at 372 if there is another word in the target dictionary to look at, and if there is such a word, fetches it at 348 and continues the com-parison process.
If the first comparison results in a score of more than zero, the program now compares the target word with the source word at 354. The comparison is repeated here because the process is essentially asymmetric - the i first comparison at 350 checked whether the letter groupings of the source word are to be found in the target word; the second comparison checks whether the letter groupings of the target word are to be found in the source word.
The scores resulting from these two comparisons are now added together and the total score examined. If the total is very low (below 200 out of a possible 2000 at this point), the comparison is abandoned and the program continues to examine the next target word at 348-352. If the total is very high (above 1900 out of 2000), the next stage in the comparison process is bypassed as being unnecessary and the program continues directly to calculating the average score at 366.
Alternately, at 358,360 if the total score falls somewhere between the above cut-off values, the target word is examined to see if it has a suffix; if so, the suffix is removed. If either the source of the target had a suffix, at 362 the suffix-less copy of the source _ 35 -word is now compared with the suffix-less copy of the target word at 364 and the score resulting from this comparison added to the total score. An average is now calculated at 366 for all the comparison which have been carried out, and if 368 that average score is above a set threshhold (400 out of a possible score-of 1,000), at 368,370 the word is added to the list of similar words found.
At 372, the program now checks whether there are any more target words to which the source word should be compared; once the whole target dictionary has been scanned in this way, the list of similar words is sorted into descending numerical order at 374 and the list of words (cut off at some convenient threshhold) is returned to the user at 376 for further editing and/or use at 300.
Three comparisons are thus made: the first comparing the source word to the target, then comparing the target to the source, and finally comparing the source to the target where both words have had their suffixes removed Depending on the scores reached at each stage in the process, further comparisons are halted if a very low score is received, or bypassed in the case of a very high score. (Halting or bypassing comparisons results in very quick processing in the case of clear-cut similarities or differences, and further comparisons are made only if justified by apparent simi-larities between the words.) The resulting average score is a balanced total of the three different com-parisons. The threshholds set at each stage in the analysisare quite arbitrary and may be set to various values depending on the needs of a particular application.
Likewise, the different comparisons can be weighted if it is desired to emphasize one comparison over the other, and the words can be examined with prefixes removed as well as, or instead of, removing the suffixes.

,~

The comparison routine which carries out the comparisons and allocates the points used in calculating the scores is described in Figure 14.
The first letter is taken from the source word, and from the target word at 378,380. At 382, these letters are compared to each other. If the letters do not match, then at 392 the program keeps trying to find a match by taking one more letter at a time from the target word until there are no more target letters.
The next source letter is then taken, and compared to each target letter in turn, and so on until there are no more source letters to compare.
Once a match is found between the source and target letters, that match is counted at 386, and the following two letters from each word are compared at 388. The process repeats until two letters are found which do not match each other.
The comparison score is then calculated at 390, based on the number of consecutive letters which matched in the two words. This score is obtained from a table which converts the number of matching letters to the appropriate score value. The start of this table is shown below:

number of matching letters 1 ¦2 ¦ 3 ¦ 4 ¦5 ¦ 6 ¦ 7 . . .
point score 0 ¦1 ¦3 ¦6 ¦10 ¦ 15¦ 21. . .

These values are calculated on the basis of one point for each subgroup (pair, triple, quadruple, etc.) of letters contained in the group of matching letters. A pair contains only one pair, and so is allo-cated 1 point; a triple contains one triple and twopairs, and so is given 3 points; a quadruple contains three pairs, two triples and one quadruple and so is allocated 6 points. Different weighting systems can easily be implemented depending on the particular needs of a given implementation, to emphasize different sizes ~7 ~76~728 of letter groupings.
This process is continued until the entire source word has been scanned at 394. Once the word has been scanned letter by letter in this way, a total weighted score is calculated at 396. This total is obtained by adding together all the subscores generated during the comparison, and dividing them by the total score possible based on the length of the source word.
This highest possible total is simply the value found in the scoring table for the length of the source word itself, as this is the value that would have been found by comparing the source word with itself. In this way, the score is adjusted for the length of the word so that the same score will be obtained for words of comparable similarity, no matter their lengths. This final score is multiplied by 1000 in order to convert it to an integer value between 0 and 1000.
The suffixes which this implementation looks for and removes at 346,360 for the final comparison are "ing", "ed", "er", "e", "tion", "al", "s", and "ly".
These suffixes are removed so that gram-matically related words (verb forms, adjectives, plurals, etc.) will be found by the program; a different list or a list of prefixes could easily be substituted to suit the needs of a different implementation.

Similar Sentences As identical technique may be used to compare word phrases or sentences rather than individual words.
This procedure is detailed in Figure 15. An input sen-tence is obtained at 431, and stripped of any wordswhich appear on a list of "noise words" at 432,433. A
target sentence is obtained at 434 from a list of sen-tences describing the texts in the database, and the original source sentence is compared with this target sentence at 435, using the method described below and illustrated in Figure 16. Depending on the score thus ~;

obtained at 436 the program may then continue to compare the target sentence with the source sentence at 437 and depending on the average score so far obtained at 438 will then strip the target sentence of any words appearing on the "noise list" at 439,440. If either source or target sentence had words appearing on this "noise list" at 441, the source sentence is compared a third time to the target sentence, this time where neither sentence includes "noise words" 442 The total scores of all these comparisons is now averaged at 443, and ir the average score lies above a predetermined threshold at 444, the target sentence is added to the list of similar sentences found at 445. This procedure is repeated until no more sentences exist in the list of target sentences being examined at 446; at this stage the list of similar sentences found is sorted at 447.
At 448, the sorted list is either returned to the user, or is used by the program to control the selection and/or display of texts. In the implementation described above, the list is used at 90g, where a group text is divided into two sub-groups depending on whether each text's comparison score falls above or below a given threshold.
The mechanism of the sentence comparisons carried out is detailed in Figure 16. Here a word of the source sentence at 449 is compared to successive words of the target sentence at 450,452 until matching words are found at 451. Once a matching word is found, the number of successive matching words in the two sen-tences is counted 453,454 and a score calculated, based on the number of matching words 455, using a table as was described above for the calculation of scores at 390. This process is repeated until all words of the source sentence have been processed 456, at which point a total weighted score is calculated at 457 based on the total score achieved as a proportion of the total ~X~76728 possible score for an identical match.
It should be noted that the "sentences"
referred to in the paragraphs above may be, but are not necessarily, grammatical natural language sentences.
The procedure is also applied to "sentences" which are actually the keyword list provided by the user when he describes the text on saving it in the textbase.
Moreover, the request might be a collection of words in a predetermined order having no sentence structure. The program will then search for this collection of words appearing in the specified order within an area of the text. The area of the text might be limited to a prede-termined sub-area of the text such as the title, abstract, paragraph, etc. or within a certain number of words. This feature enables the program to distinguish between areas of text having the same words but an entirely different meaning.
It is to be understood that even though the above numerous characteristics and advantages of the invention have been set forth in the foregoing descrip-tion, together with details of the structure and the function of the invention, the disclosure is illustra-tive only, and changes may be made in detail, within the principle of the invention, to the full extend indicated by the broad general meaning of the terms in which the appended claims are expressed.

~ .

Claims (23)

1. A method utilizing processor means and associated memory means for making explicit the inherent relationships among a first group of texts stored in the memory means and being associated with at least one keyword, the method comprising the steps of:
(a) defining as a "criterion key" that keyword which among all the keywords associated with any of the texts in the first group of texts, is associated with the largest number of texts within the first group of texts;
(b) separating the first group of texts into first and second sub-groups of texts which are sub-groups of the first group of texts, the first sub-group of texts having as one of its keywords the criterion key and the second sub-group of texts not including the criterion key as a keyword; and (c) displaying results obtained from the above-described steps on a suitable display medium.

2. A method in accordance with claim 1, wherein the above process is applied recursively to at least one of the first and second sub-groups.

3. A method in accordance with claim 2, wherein the results of separating the first group of texts into sub-groups is displayed according to the following method:
(a) if there exist keywords common to all of the texts in the first group, a line of display representing the first group is generated by listing on the display medium some or all of those keywords associated with all of the texts in that group;

(b) if a first sub-group is present, another line of display is generated representing the first sub-group, indented further to the right, listing some or all of the keywords common to that first sub-group;
(c) additional lines of display may be generated by treating the first sub-group as the "first group" and applying the method of claim 1 and the pre-sent claim to that group recursively; and (d) if a second sub-group is present, then another line of display is generated representing the second subgroup, below the line generated in step (a) and below any lines generated by step (b) and (c), and with the same indentation as the line generated by step (a), by treating the second sub-group as the "first group" and applying the method of claim 1 and the pre-sent claim, whereby a listing is created in traditional outline format, such that whenever a line of display represents a group which is further analyzed, the con-sequent subdivision into further sub-groups is detailed in a line or lines appearing below it and to the right, and inversely when a group of texts is included within another group, the line representing the included group appears below and to the right of the line representing the group of which it is a part.

4. A method in accordance with claim 1, wherein the above method is applied recursively to each of the sub-groups until the sub-groups contain only one text.

5. A method in accordance with claim 1, wherein the process of choosing the criterion key is applied repeatedly to the first group of texts, excluding as criterion keys those keywords which have already been used as criterion keys in a previous iteration.

6. A method in accordance with claim 1, wherein the criterion keys are chosen in alphabetical order.

7. A method in accordance with claim 1, wherein the criterion key is specified by the user.

8. A method in accordance with claim 2, wherein the criterion keys are selected from a user-supplied outline of keywords.

9. A method in accordance with claim 1, wherein substantially all the words of each text are considered to be keywords.

10. A method in accordance with claim 1, wherein all the words of the texts, with the exception of a pre-determined list of words, are considered to be keywords.

11. A method in accordance with claim 1, including the step of scanning the keywords associated with all the texts in the first group of texts for the presence of a designated keyword and eliminating that keyword from the keyword list of each text.

12. A method in accordance with claim 1, including the step of scanning the keywords associated with the first group of texts for the presence of a designated keyword and replacing that keyword with a designated substitute keyword.

13. A method in accordance with claim 1, including the step of automatically allocating keywords by scanning the texts of the first group of texts and com-paring the scanned text on a word by word basis with a set of "scan" words and allocating the scan word, or a designated substitute for the scan word, to be a keyword for that text, for all of the texts for which a match is found.

14. A method in accordance with claim 12, wherein all the words allocated as keywords for any of the texts are used as scan words.

15. A method utilizing a processor means and asso-ciated memory means for finding words in a collection of target words stored in the memory means which are simi-lar to a source word, comprising the steps of:
(a) separating the source word into its com-ponent character strings, where a character string is any contiguous subset of a word's characters in their original order;
(b) searching the target words for the same character string;
(c) allocating points to each target word for each match between a character string representing part of the source word and a character string representing part of the target word; and (d) ranking words of the target word list in terms of similarity to the source word, according to the total number of matches that are found between the character strings of the source word and the character strings of the target words.

16. A method in accordance with claim 15, in which the points allocated to each of the target words are weighted by a factor which depends on the length of the character string being compared.

17. A method in accordance with claim 15, in which the points allocated to each of the target words are weighted by a factor which depends on the size of at least one of the source words and the target words.

18. A method in accordance with claim 15, wherein the points allocated are weighted by a factor which depends on the location of the character string grouping within at least one of the source and target words.

19. A method in accordance with claim 15, wherein the points allocated are modified by a factor which depends on whether the particular character string being compared exists in a supplied list of character strings, such as common suffixes and/or prefixes, or any other supplied list of character strings.

20. A method in accordance with claim 15, wherein the source word is first converted to an approximate phonetic form prior to searching the target word list.

21. A method in accordance with claim 16, wherein the source word is first separated into its component syllables prior to searching the target words list, and points are allocated based on matches between the resulting groups of syllables.

22. A method utilizing programmed processor means and associated memory means for finding a collection of words, such as a phrase or a sentence, in a target word list stored in the memory means similar to some source word collection, comprising the steps of:
(a) separating the source word collection into its component sub-groups of words, wherein a sub-group is any set of one or more words which were con-tiguous in the source word collection in their original order;
(b) searching the target word list for iden-tical sub-groups;

(c) allocating points to each word collection in the target word list for each match between a sub-group of words representing part of the source word collection and a sub-group of words representing part of that target word collection; and (d) ranking word collections of the target word list in terms of their similarity to the source word collections, according to the total number of matches that are found between the sub-groups taken from the source word collection and the sub-groups belonging to word collections of the target word list.

23. A system utilizing processor means and memory means for making explicitly inherent relationships among a first group of texts stored in the memory means and being associated with at least one keyword, the system comprising:
(a) first program means located in the memory means and executable by the processor means for defining as a "criterion key" that keyword which among all the keywords associated with any of the text in the first group of texts, is associated with the largest number of texts within the first group of texts;

(b) second program means located in the memory means and executable by the processor means for separating the first group of texts into first and second sub-groups of texts which are sub-groups of the first group of texts, the first sub-group of texts having as one of its keywords the criterion key and the second sub-group of texts not including the criterion key as a keyword; and (c) third program means located in the memory means and executable by the processor means for displaying on a suitable display medium a visual indication of which texts comprise the first and second sub-groups of texts.