"Automated Collation Creation"

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"Automated Collation Creation"

Abstract: A collation creation process is provided to automatically establish collation support for sorted linguistic data. The sorted linguistic data is examined to determine if it matches an existing collation support. If not. a new collation support is created for the sorted linguistic data. The provider of the sorted linguistic data may participate in the collation creation process by answering queries concerning the sorted linguistic data. The provider"s input is integrated into the sorted linguistic data before the collation creation process is applied to the sorted linguistic data. A user interface is provided that enables the interaction between the provider of the sorted linguistic data and the collation creation process. The user interface provides visual cues identifying distinctions among the strings in the sorted linguistic data.

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Notices, Deadlines & Correspondence

Patent Information

Application #

Filing Date

03 November 2005

Publication Number

45/2009

Publication Type

INA

Invention Field

COMPUTER SCIENCE

Status

Parent Application

Applicants

MICROSOFT CORPORATION

ONE MICROSOFT WAY, REDMOND, WASHINGTON 98052, U.S.A

Inventors

1. CATHERINE A. WISSINK

ONE MICROSOFT WAY, REDMOND, WASHINGTON 98052, U.S.A

2. MICHAEL S. KAPLAN

ONE MICROSOFT WAY, REDMOND, WASHINGTON 98052, U.S.A

Claims

1. A computer-implemented method for automatically establishing collation support for sorted linguistic data, comprising: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyzing the sorted linguistic data to create a new collation support.

2. The computer-implemented method of Claim 1, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string.

3. The computer-implemented method of Claim 2, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string.

4. The computer-implemented method of Claim 2, further comprising analyzing behaviors of special characters.

5. The computer-implemented method of Claim 4, wherein special characters include diacritics, combining marks, and scripts.

6. The computer-implemented method of Claim 1, further comprising preprocessing the sorted linguistic data prior to executing (a) and (b).

7. The computer-implemented method of Claim 6, wherein pre-processing the sorted linguistic data includes validating the sorted linguistic data to ensure the generation of corrected collation support based on the sorted linguistic data.

8. The computer-implemented method of Claim 7, wherein validating the sorted linguistic data includes: identifying at least one problem in the sorted linguistic data; requesting correction to the sorted linguistic data; and applying the correction to the sorted linguistic data upon receiving the correction.

9. The computer-implemented method of Claim 6, wherein pre-processing the sorted linguistic data includes normalizing the sorted linguistic data.

10. The computer-implemented method of Claim 1, further comprising: collating the sorted linguistic data using the collation support; verifying the collated linguistic data; and repeating (a) and (b) on the collated linguistic data if the verification results in a change to the collated linguistic data.

11. The computer-implemented method of Claim i 0, wherein verifying the collated linguistic data includes determines whether the ordering of the collated linguistic data is correct.

12. The computer-implemented method of Claim 1, further comprising testing the collation support.

13. The computer-implemented method of Claim 12, wherein testing the collation support includes: receiving test data: and collating the test data using the collation support.

14. The computer-implemented method of Claim 13, further comprising: applying a correction to the collated test data, upon receiving a correction to the collated test data; inserting the corrected test data into the sorted linguistic data; and repeating (a) and (b) of Claim 1.

15. A computing system for automatically establishing collation support for sorted linguistic data, comprising a programmed data processor for: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyzing the sorted linguistic data to create a new collation support.

16. The computing system of Claim 15, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string.

17. The computing system of Claim 16, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string.

18. The computing system of Claim 16. wherein the programmed data processor also analyzes behaviors of special characters.

19. The computing system of Claim 18. wherein special characters include diacritics, combining marks, and scripts.

20. A computer-readable medium containing computer-executable instructions for automatically establishing collation support for sorted linguistic data that, when executed: (a) search existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyze the sorted linguistic data to create a new collation support.

21. The computer-readable medium of Claim 20, wherein analysis of the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string.

22. The computer-readable medium of Claim 21, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on tfie secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string.

23. The computer-readable medium of Claim 21, wherein the computer-executable instructions when executed also analyze behaviors of special characters.

24. The computer-readable medium of Claim 23, wherein special characters include diacritics, combining marks, and scripts.

25. A computing system for automatically establishing collation support for sorted linguistic data, comprising: (a) an input device; (b) a display, wherein the display includes a user interface; and (c) a processor coupled with the input device and the display for: (i) receiving sorted linguistic data from the input device; (ii) establishing collation support for the sorted linguistic data; and (iii) displaying the sorted linguistic data on the user interface.

26. The computing system of Claim 25, wherein the processor also: sends a query to the user interface concerning the sorted linguistic data; and applies change to the sorted linguistic data upon receiving from the input device a change to the sorted linguistic data in responding to the query.

27. The computing system of Claim 25, wherein display of the sorted linguistic data on the user surface includes indicating visually a distinction between two compared strings in the sorted linguistic data.

28. The computing system of Claim 27. wherein the distinction includes a break point of a string in the sorted linguistic data.

29. The computing system of Claim 28, wherein the distinction includes the type of the weight difference at the break point.

30. The computing system of Claim 25, wherein establishing collation support for the sorted linguistic data includes: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and lb) if no existing collation support for the sorted linguistic data is found, analyzing die sorted linguistic data to create a new collation support.

31. The computing system of Claim 30, wherein the processor also preprocesses the sorted linguistic data so that it is consistent in its ordering and complete in coverage.

32. The computing system of Claim 30, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string.

33. The computing system of Claim 32, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weighting of the character in focus in each string.

34. The computing system of Claim 32. wherein the processor also analyzes behaviors of special characters.

35. The computing system of Claim 34. wherein special characters include diacritics, combining marks, and scripts.

36. The computing system of Claim 25, wherein the processor also sends a verification request to the user interface after establishing collation support for the sorted linguistic data.

37. The computing system of Claim 36, wherein the processor also: applies a change to the sorted linguistic data upon receiving the change from the input device in responding to the verification request; and reestablishes collation support for the sorted linguistic data after applying the change.

38. The computing system of Claim 25, wherein the processor also tests the collation support after establishing collation support for the sorted linguistic data.

39. The computing system of Claim 38, wherein the test of the collation support includes: receiving test data from the input device: and collating the test data using the collation support.

40. The computing system of Claim 39, wherein the processor also: displays the collated test data to the user interface; applies a correction to the collated test data upon receiving a correction to the collated test data from the input device; inserts the corrected test data into the sorted linguistic data: and reestablishes collation support for the sorted linguistic data.

Specification

AUTOMATED COLLATION CREATION FIELD OF THE INVENTION The present invention relates to a computer program and more particularly, to a computer program lor collating linguistic data BACKGROUND OF THE INVENTION One ot the greatest challenges in the globalization of computer technologies is to properly handle the numerous written languages used in different parte ot the world Languages may ditler greatly in the linguistic symbols they use and in their grammatical structures Consequently it can be a daunting task to support most, it not all, languages in various forms ot computer data processing To facilitate the support of different languages by computers a standardized coding system known as Unicode was developed to uniquely identity ever)' symbol in a language with a distinct numeric value 1 e , codepoint and a distinct name Codepomts are expressed as hexadecimal numbers with four to six digits For example, the English letter "A" is identified by the codepoint 0041, while the English letter "a" is identified by codepoint 0061, the English letter "b" is identified by the codepoint 0062 and the English letter "c" is identified by the codepoint 0063 in the Unicode system A fundamental operation on linguistic characters for graphemes) of a given language is collation which may be defined as sorting strings according to a set of rules that is culturally conea to users of a particular language Collation is used any time a user orders linguistic data or searches tor linguistic data in a logical fashion within the structure ot a given language Support ot collation on a computer requires an in-depth understanding ot the language Specificall) there must be a good understanding ot the graphemes used in the language and the relationship between the graphemes/phonemes and the Unicode codepoints used to construct them For example, in English, a speaker expects a word starting with the letter "Q" to sort atter all words beginning with the letter "P" and before all vvotds starting with the letter "R " As another example in the Traditional Chinese, the ideographs are often stored according to their pronunciations based on the "bopomoto" phonetic system as well as by the numbers ot strokes in the characters Further the proper sorting of the graphemes also has to take into account variations on the graphemes Common examples of such variations include casings (upper or lower case) ot the symbols and modifiers (diacritics, Indie matras, vowel marks) applied to the symbols Collation 1 e sorting, is one ot the most fundamental features that a user expects to simpl) work Ideally collation should be transparent People simply expect that when they click on the top of a column in Windows® Explorer, that the column will be sorted according to their linguistic expectations Such expectation may be easy to meet trom a technical perspective for simple languages, such as English however, when support for additional languages is needed, such support can be more complicated The challenges in achieving proper collation are due to several factors For example, people usually have a clear idea ot how the information they choose to collate should be ordered However few people can reallv describe the rules by which collation works for any but the simplest of languages such as English To make the matter even more complicated collations that are appropriate for one language are often not appropriate for another, in tact, many collation schemes contradict each other Furthermore, people who generally understand the technical issues of collation do not understand the language or the linguistic structure Contrariwise experts in languages often lack the technical expertise to provide collation in a form that can be used in a traditional multi-weighted collation format In addition existing platforms providing collation extensibility require full collation information as input This requires extensive technical skill knowledge of internal methodology and structures, and overt collation knowledge Usually collation is done manually by professional collation providers such as professional linguists FIGURE 1 illustrates a linguist 102 operating a computer 104 to collate linguistic data such as the set ot strings 106 Linguistic data can be comprised or as few as a handful ot strings or as many as tens ot thousands of strings and characters included m a language However, a single professional collation provider, or even a small group ot them can only do so much at a time Thus there is a need to automate the collation process so that collation support for a given language can be easily provided Additionally, different institutions often need the capability of collating data in a linguistically appropriate fashion Such institutions tor example, the U S Homeland Security Agency, mav prefer not to share data with a professional collation provider Therefore, there is a need to provide an automated collation support so as to allow data to be collated in a private matter In summary proper collation support requires a comprehensive understanding of the language of the linguistic sttucture Manually input collation information by professional collation pro\iders, such as linguists limits the ability to add collation support tor linguistic data As a result, there is a need to automate the collation process such that collation support can be easily extended for any given language and collation can be done by a general user when privacy is preferred The invention described below is directed to addressing this need SUMMARY OF THE INVENTION The invention is directed to a tool that automatically establishes collation support for sorted linguistic data The tool analyzes the sorted linguistic data to identity the underlying collation rules During the analyzing process, the tool mav ask the user who provided the sorted linguistic data iterative questions concerning the sorted linguistic data thus collaborating with the user in reaching a correct collation support tor the sorted linguistic data The tool may further test the resultant collation support by sorting test data provided by the user In accordance with one aspect of the invention, analyzing the sorted linguistic data to establish collation support includes searching existing collation support schemes and locating a matching collation support scheme for the sorted linguistic data If no existing collation support scheme is available tor the sorted linguistic data a new collation support is established bv analyzing the sorted linguistic data In accordance with another aspect of the invention to establish a new collation support based on the sorted linguistic data, each character in each string contained in the sorted linguistic data is analyzed to identity the underlying weighting structure, beginning with the first character in each string When analyzing each character in a string the strings in the sorted linguistic data are first grouped based on the primary weight, 1 e the alphabetic weight ol the character in each string The strings lesulting from the first grouping are then further grouped baj>ed on the secondary weight, i e , the diacritic weight ot the character in each string The strings are then hirther grouped based on the tertiary weight 1 e the casing weight of the character in each string To establish a new collation support based on the sorted linguistic data further includes analyzing the behaviors of special characters, such as diacritics combining marks and scripts In accordance with yet another aspect of the invention, when analyzing the sorted linguistic data to establish collation support for the sorted linguistic data, the sorted linguistic data is preprocessed The preprocessing first validates the sorted linguistic data to ensure that it is consistent in ordering and complete in coverage Preferably, validating the sorted linguistic data includes identifying a problem in the sorted linguistic data, requesting correction to the sorted linguistic data and applying the correction to the sorted linguistic data Preprocessing the sorted linguistic data may also include normalizing the sorted linguistic data In accordance with yet another aspect ot the invention after establishing collation support for the sorted linguistic data, the collation support may be verified, preferably by the user who provided the sorted linguistic data The user may correct the collation support by adjusting the ordering of the sorted linguistic data which has been collated bv the collation support Any changes provided by the user are integrated into the sorted linguistic data, which is analyzed again to establish a correct collation support reflecting the changes made by the user In accoidance with a further aspect of the invention after establishing the collation support for the sorted linguistic data, test data may be provided to test the collation support The test data can be sorted itself to verify if the application of the collation support on the sorted test data maintains the ordering of the test data The test data can also be unsorted Upon applying the collation support to the unsorted test data the ordering of the collated test data is preferably examined to verify whether it reflects the user's expectation If the collated test data does not meet the user's expectation, the ordering of the test data may be adjusted by the user and the adjusted test data may then be integrated into the sorted linguistic data which may be analyzed again to generate the correct collation support In accordance with another aspect of the invention, the collation support information ma> be built into a bmmbol m a string For example, as illustrated in FIGURE 4 the Latin small letter "a" is identified by the codepoint 0061 the Latin small letter "d" is identified by the codepoint 0064 and the Latin small letter "m" is identified by the codepoint 006d Consequently, the string "adam" is identified by the four codepomts 0061, 0064 0061, and 006d In addition advanced window 402 also include** a checkbox 404 tor "Unicode Property Info " Upon the selection of checkbox 404, user interface 300 provides information about character properties tor the characters in a string buch intomiation about character properties provides better understanding of the string In embodiments of the invention, typical character properties include General_Category Bidi_Class Canonical_Combming_Class, Decomposition_Type Decomposition_Mapping NumencalType and NumencalValue For a detailed description about character properties, please see Unicode Character Database hup //unicode or g/public/unidutu/ucd html FIGURES 5A-5D illustrate some of the character properties provided bv advanced window 402 For example FIGURE 5A reveals the General_Categor\ value of the Latin small letter "a" is "LI," an acronym for "Letter, Lower case " FIGURE 5B illustrates that the character name ot the codepoint 0061 is Latin small letter "a " FIGURE 5C illustrates that the lower case character "a" in the string "apple" has a Bidi_Class value ot "left to right" and a Canonical_Combming_Class value ot "0," which stands for "spacing, split enclosing reordrant, and Tibetan subjoined " Furthermore, FIGURE 5D illustrates that the string "apple" is displayed in the normalization form D which means the combined characters "a," in the string are displayed in the decomposed torm This means that the "a" is represented by two codepomts 0061 and 030a, representing the Latin lower case letter "a" and the non-spacing mark"°," respectively In embodiments of the invention, user intertace 300 further displays strings in different normalization forms As those skilled in the art or related fields know normalization is the process ot removing alternative representations of equivalent sequences from textual data in order to convert the textual data into a form that can be compared for equivalency In the Unicode standard, normalization reters specifically to processing to ensure that canonical-equivalent and/or compatibility-equivalent strings have unique representations For more information on normalization in the Unicode standard, please see Unicode Normalization Forms http //www Unicode org/tepoit/u 15/ Generally, there are tour Unicode normalization forms, namely, Normalization Form C Normalization Form D Normalization Form KC, and Normalization Form KD User interlace 300 gives a CU the option to decide which normalization form(s) will be displayed For example as illustrated in FIGURE 6A, a CU may choose to display a string in all or its normalization forms As illustrated in FIGURE 6B, a CU may select to display a string in Normalization FormC Normalization Form C results from the canonical decomposition ot a Unicode string followed by the replacement of all decomposed sequences with primary composites where possible FIGURE 6C illustrates a string that is displayed in Normalization Form D, which results from the canonical decomposition of the string In embodiments ot the invention, TOOL 204 provides a CU with the ability to test collation rules identified by the collation engine on applicable data that is not part of the custom data being used tor collation creation A CU can use the testtng feature to determine if the collation engine has identified the expected collation rules A CU can input test data into a test user interface (hereinafter "Test Surface") to have the collation rules applied to the test data to determine if the collation of the test data is correct In such embodiments ot the invention, user interface 300 therefore further includes a test surtace FIGURE 7A illustrates one exemplary implementation of a test surtace 700 Test surface 700 first requests a CU to enter a list of strings For example as illustrated in FIGURE 7A, test surtace 700 displays a set of strings in first column 304 "Cathy," "Resume," "Adam " "Spam," "Deal," "spam," "resume," and "Catherine " Test surtace 700 may also ask a CU to specify whether the test data was sorted betore entry For example, as illustrated in FIGURE 7A the set of strings contained by first column 304 is indicated as being unsorted Regardless ot whether the test data is sorted or unsorted before it is entered into test surtace 700, a CU may test the current collation rules by actuating "Sort" button 702 to collate the test data In embodiments of the invention, test surface 700 can receive a correctly sorted list of strings from a CU By inputting a correctly sorted list of strings to test a CU can verify whether applying the current collation rules keeps the current order of the test strings intact If the current order or the test data is changed the changes can be highlighted so that they ma> be resolved by the CU Test surface 700 can also accept an unsorted list of strings as test data TOOL 204 can then collate the test data upon the CU actuating "Sort" button 702 The CU can then indicate whether the resultant collation of the test data was correct It it is not, the CU can assist in the resolution of the problem by correcting the ordering of the collated test data which is then used to produce correct collation rules By using test surface 700 a CU can test the collation rules prior to building a collation binary file After viewing the collated test data, a CU can identity problems and make corrections to the sorting of test data The corrections will trigger TOOL 204 to adjust the collation rules accordingly The collated test data may be added to the custom data as soon as it is verified by the CU For example, FIGURE 7B illustrates sorting test data contained in first column 304 in FIGURE 7A After a CU actuates the "Sort" button 702, test data in first column 304 is collated using current collation rules For example as shown in FIGURE 7B test data is now in the order of "Adam " "Catherine," "Cathy " "Bill," "Resume " "Resume " "Spam " and "spam " FIGURE 7B also includes a query window 710 which asks a CU to confirm whether the sorting as a result of using current collation rules is correct If the answer is YES the CU actuates the "Yes" button 712 This confirms that the current collation rules are accurate If the answer is NO the CU actuates the "No" button 714 In this case, the CU may proceed to adjust the sorting in first column 304 to show the proper collation In summary, user interface 300 enables a CU to interact with the collation creation process executed by the collation engine component of TOOL 204, in real time so as to ensure creation ot the collation support expected by the CU Lfser interface 300 also provides an engaging and straightforward way for the CU to participate in the collation creation process by hiding the complexity ot the collation creation process that is discussed in detail below After receiving custom data from a user interface, such as user interface 300 illustrated in FIGURES 3-7B, the collation engine component of TOOL 204 analyzes custom data to identify proper collation rules inherent in the ordering of custom data During the analysis piocess the collation engine asks CU iterative questions to clarify inconsistencies and ambiguities in the custom data for example through user interlace 300 In some embodiments or the invention the collation engine receives test data to \enfy the identified collation rules FIGURES 8-13 illustrate one exemplary implementation of the functionalities provided by the collation engine of TOOL 204 This exemplary implementation illustrates the collation engine's behavior in the context of some of the distinct and anticipated custom data input scenarios A CU may input custom data to TOOL 204 in different ways For example, a CU may provide the entire linguistic data in a single input Alternatively, a CU may provide only known exceptions to a typical collation of which the CU is aware For example a CU may provide known exceptions to collation support tor the English language On the other hand a CU may insert data on specific linguistic boundaries, such as each letter in a script or all or the diacritic svmbols Finally a CU may provide different sets ot sorted data, where the boundaries ot the data have no specific linguistic basis Because ot the wide variety ot possible scenarios on custom data input the collation engine does not have boundaries for the initial size of custom data being provided Duiing the process ot analyzing custom data to identify the underlying collation rules, the collation engine is able to receive additional custom data from a CU FIGURE 8 illustrates one exemplary implementation ot a collation creation process 800 for establishing collation support for given sorted linguistic data (I e custom data) Process 800 is described with reference to TOOL 204 (FIGURE 2) and its user interface 300 illustrated in FIGURES 3-7B In essence, upon receiving custom data process 800 analyzes the custom data to identify corresponding collation rules inherent in the ordering of the custom data In some embodiments of the invention, process 800 asks the CU to verify the custom data after the analysis Process 800 also allows a CU to enter test data to test the identified collation rules Process 800 may turther build the identified collation rules into a binary file tor future use Optionally, the entire collated custom data may be saved as a word list More specifically process 800 first receives custom data, tor example, through a user interface such as user interface 300 of TOOL 204 See block 802 As mentioned above regarding user interface 300, there are essentially three different approaches to input custom data The first approach considers the received custom data to have been verified by a CU This means that the custom data has been sorted and the ordenng is consistent with the target collation the CU attempts to emulate Inputting sorted custom data can be done all at once in batches or one entry at a time The second approach, on the other hand, relies on the existing collation information the collation engine is holding No additional custom data will be used until the collation engine has \alidated custom data it currently holds As noted earlier, validation is a process that the collation engine uses to determine whether the custom data is both consistent in ordering and complete in coverage This process usually occurs before the collation engine analyzes the custom data to identity the underlying collation rules FIGURE 10 illustrates one exemplary implementation of the validation process, and will be discussed in detail later Therefore under the second approach, additional validation is an implicit requirement when inserting additional custom data so that the collation engine can continue to consider all the custom data validated The third approach is specific to languages that use ideographic systems Such languages are primarily Chinese Japanese and korean The third approach is similar to the first approach in that custom data is considered verified In embodiments of the invention the collation engine has a basic understanding of many ol the phonetic stroke-based and other indexing systems Thus, a CU with a dictionary implementing such an indexing system in electronic form can pass the information in the dictionary directly to the collation engine In general, under the third approach, it does not matter whether the custom data is in a sorted order or not because explicit collation support tor die custom data is already available Such existing collation support includes pronunciation-based ordering such as the "bopomofo" system for collating Traditional Chinese Such existing collation support may be stroke count-based orderings For example, one such ordenng is based on the total stroke count within a Han character Other existing collation supports include government or industry encoding standard-based ordering, such as the GB official standard of the People's Republic ot China In other cases, combinations ol the various orderings are used For example, the "bopomoto" pronunciation-based ordering for traditional Chinese could be used along with all ideographs that ha\e identical pronunciations sorted in stroke order Another example is the kanji dictionary, which allows a Japanese reader to easily look up Chinese ideographic characters used in Japanese General 1\ kanji ideographic characters ate ordered b> radical (an element in the ideograph that can represent a pronunciation or a core concept) and bv stroke (the number of brush strokes needed to draw the character) Because a given character may have multiple pronunciations in pronunciation sorts embodiments ot TOOL 204 support a frequency count, which identifies the number ot pronunciations a given character may have \t one given time TOOL 204 may enable only one pronunciation TOOL 204 may leave the alternate pronunciations in a disabled state indicating that they are not being used Upon receiving custom data under any of the three approaches process 800 executes a routine 804 to analyze the custom data and identify collation rules manifested by the ordering ot the custom data FIGURE 9 illustrates one exemplary implementation ol routine 804 and will be discussed in detail later In some embodiments ot the invention, after executing routine 804, process 800 proceeds to check it the now collated custom data was previously verified bv the CU See decision block 806 As discussed above depending on how the custom data is initially input, the custom data received by process 800 may or may not have been verified by the CU In embodiments of the invention, verification is a process that the CU uses to determine whether the ordering ot custom data is consistent with the target collation the CU is attempting to emulate li the custom data was not previously verified process 800 proceeds to request the ClI to verify the now collated custom data See block 808 In some embodiments of the invention, process 800 may query the CU through user interface 300 ai to whether there is any inconsistency in the collated custom data The query may further ask whether the collation is correct In embodiments of the invention custom data is assumed to have been verified unless the CU negates this assumption by answering NO to the query If the CU replies that the custom data was not previously verified the CU may proceed to verify the collated custom data In this situation process 800 loops back to routine 804 to analyze the now verified custom data because the CU may have changed the ordering ol the custom data when verifying the custom data If the answer to decision block 806 is YES meaning that the custom data has been verified process 800 proceeds to check if the CU has input more custom data See decision block 810 If the answer is YES, process 800 loops back to block 802 to receive the additional custom data, which is then analvzed and checked for verification If the answer to the decision block 810 is NO, meaning that there is no additional custom data from the CU process 800 proceeds to check if the CU wants to test the current collation rules identified bv executing routine 804 See decision block 812 It the answer is YES. process 800 executes a routine 814 that tests current collation rules upon receiving test data from the CU FIGURT 13 illustrates one exemplary implementation ot routine 814 and will be discussed in detail later It the answer to decision block 812 is NO, meaning that process 800 receives no request to test current collation rules, process 800 may proceed to build the current collation rules into a binary file The resultant collation information can be used in the future tor collating other linguistic data See block 816 In some embodiments ot the invention process 800 also allows the CU to save the complete custom data, preferably along with other information For example, process 800 may save the custom data possibly along with its Unicode codepoints TIGURE 9 illustrates one exemplary implementation of routine 804 that analyzes custom data and identifies collation rules inherent in the ordering of the custom data In exemplary embodiments ot the invention, routine 804 contains four phases Phase 0 is a preprocessing phase that validates and normalizes the custom data In embodiments of the invention routine 804 executes a process 830 to preprocess the custom data FIGURE 10 provides an exemplary implementation of process 830 and will be discussed in detail later After executing process 830 that validates and normalizes the custom data, routine 804 proceeds to Phase 1, which is the first step of identifying collation rules based on the ordering in the custom data In this phase, routine 804 compares the ordering of the custom data with existing collation support schemes For example in the exemplary embodiment of the invention, routine 804 compares the ordering of the custom data with the Windows1?) default sorting table See block 832 The Windows® default sorting table is a flat table ot "*2-bit values that contains the default sort weight tor each character whose Unicode codepoint is in the range of 0000-FFFF The Windows® default sorting table is the basis tor all collations Currently more than 70 locales are supported by the Windows® default sorting table In general, a locale is a unique combination of language, religion, and script that defines a set of preferences tor fomiatting and sorting Linguistic data Thus it is possible that the desired colhtion for the custom data may be co\ered in the Windows® default sorting table In such a case no further processing will be required As illustrated in FIGURE 9, routine 804 checks if there is a matching collation tor the custom data in the Windows® default sorting table See decision block 834 It the answer is YES the collation rules tor the custom data have been identified routine 804 exits, and process 800 (FIGURE 8) proceeds to the ne\t action which can be testing the collation rules and/or building the collation rules into a binary file for future use It there is no matching collation in the Windows® default sorting table routine 804 proceeds to Phase 2 Phase 2 determines if any of the available compression and exception tables matches the differences resulting fiom the comparison that occurred in Phase 1, i e, the differences between the Windows* default sorting table and the ordering of the custom data See block 836 As known to those of ordinary skill in the art or other related fields an exception table lists changes that are to be made to the Windows® default table for a given language An exception table should be a minimal subset ot characters that must have their assigned weights changed for the sake ot the given language's collation Meanwhile, a compression table registers each type ot compression, l e, sort elements that contain more than one Unicode codepoint In embodiments of the invention the knowledge that a particular compression or exception table has a resemblance to the custom data may help the collation engine formulate clarifying questions to be presented to the CU In situations where the custom data closely matches an existing exception or compression table, the possibility ot a mistake will be presented to CU It there is a match between the differences resulting fiom the comparison that occurred in Phase 1 and the information in one ot the compression and exception tables (see decision block 838) routine 804 returns to process 800 (FIGURE 8) Process 800 has found a collation match for the custom data and proceeds to the next action which can be to test and/or build the collation information If no match is found in Phase 2 routine 804 proceeds to execute a process 840 to generate new collation support by analyzing the ordering of the custom data See block 840 This is the last phase, I e, Phase 3, for routine 804 FIGURE 12 illustrates one exemplar)' implementation of process 840 and will be discussed in detail later Routine 804 then exits As noted above FIGURE 10 illustrates one exemplary implementation of process 830 that preprocesses custom data in preparation for the generation of proper collation support Process 830 first validates the custom data bv checking the custom data for nn> inconsistencies or contradictions See block 842 Process 830 then proceeds to determine if any problem has been found with the custom data See decision block 844 It there are inconsistencies and/or contradictions in the custom data process 830 executes a routine 846 which communicates the problem to the CU who input the custom data Alter executing routine 846, process 830 determines whether it has received any correction addressing the problem See decision block 848 It the answer to decision block 848 is YES, process 830 proceeds to apply the correction See block 850 Process 830 then returns to block 842 to determine whether there are inconsistencies or contradictions m the corrected custom data In some embodiments of the invention the collation engine is not flexible about problems such as inconsistencies or contradictions in the custom data Unless such problems are corrected the collation engine will not proceed Therefore if the answer to decision block 848 is NO meaning the process 830 received no correction to the problem identified when validating the custom data, process 830 parent routine 804 and process 800 terminate In some embodiments of the invention, the collation engine sends messages concerning the problems it finds in the custom data only when a certain point is reached i e , when there are too many problems lor the collation engine to proceed further In most situations custom data received by the collation engine will contain primarily valid data with only minor discrepancies Thus the collation engine assumes that the custom data is accurate information The iterative nature of questions and answers during process 830 is collaborative working with the CU in real time to determine the proper collation support for the custom data In some embodiments of the invention when the quantity of the custom data and its coverage is acceptable to the collation engine, l e , that nothing is incomplete or inconsistent the collation engine sends a message to a user interface, such as user interlace 300, to indicate to the CU that the data has been validated As illustrated in FIGURE 10 it the answer to decision block 844 is NO meaning that process 830 finds no problem with the custom data process 830 proceeds to normalize the custom data See block 852 Normalization ensures that both the composed \ersion (Normalization Form O and the decomposed version (Normalization FormD) ot a string are treated equally Process 830 then exits In some embodiments ot the invention, only after process 830 has been successfully completed does routine 804 (FIGURE 9) begin to anal>ze the ordering ot the custom data to identity collation rules \fter identifying the problems in custom data (FIGURE 10), routine 846 (FIGURE 11) communicates the problems to the CU through user interlace 300 The CU can then provide intormation to fix the problem For example, if there are inconsistencies and/or contradictions in the custom data (see decision block 854), routine 846 sends a message to user interface 300 to prompt the CU to help determine how to resolve the inconsistent) See block 856 The message ma\ explain the inconsistency and even provide proposals tor resolving the inconsistency Inconsistencies and/or contradictions in custom data occur lor example when the same linguistic characters are sorted in two different ways One example of the inconsistency is that two canonically equivalent strings are distanced from each other in the custom data As known by one of ordinary skill in the art and related fields, canonically equivalent strings are not distinguishable by a user, and therefore should be treated as the same, be displayed identically and be sorted identically Further, when there is a problem of missing and/or incomplete data (see decision block 858) routine 846 will send a message to user interface 300 to prompt the CU to provide additional strings that use the character in question to further illustrate collation behavior ot the character See block 860 Such a problem may occur when, for example, it is clear that there seems to be a special behavior for a linguistic character or accent, yet there is not enough information to determine what the behavior is Additionally small differences from an existing collation support scheme may exist in the custom data In this case (see decision block 862), routine 846 sends user interface 300 a message that points out the similarity, and prompts the CU to verify the difference In some embodiments ot the invention, the message does not reference the specific language with which the similarity exists so as to avoid anv potential geo-pohtically sensitne issues See block 864 This occurs when there appears to be specific variances to the collation used elsewhere such as a script sorting uppercase before lowercase, despite the usual converse policv At times, additional information may be needed tor a script or range of characters This occurs when there appears to be mining information that may or may not be important For example, it a CU is using the Latin script, but is missing letters within the Latin range the collation engine may suggest a position in the collation rules tor a missing letter The collation engine then prompts the CU to confirm the suggested position or to reject the position and suggest an appropriate position In such a case (see dectsion block 866) routine 846 sends a message to user interface 300 to ask for the specific information needed See block 868 Furthermore, custom data may treat two equivalent strings as if thev are not equal For example two strings may be equivalent because ot the Unicode character properties and/or Unicode normalization However the custom data treats them as if the> are not equal In this case (see decision block 870), routine 846 sends a message to user interface 300 to prompt the CU to choose which position is correct See block 872 Upon a user selecting a position the other position is removed Because correct data is the essential premise of any effectixe collation creation effort custom data usually needs some adjustment in order for it to be correct data for collation creation Therefore, routine 846 may be invoked at any time for the CU to adjust custom data during the collation creation process FIGURE 12 illustrates one exemplary implementation of process 840 that is used to generate new collation rules based on the ordering of custom data In essence, process 840 analyzes the custom data to determine the collation rules inherent tn the ordering of the custom data Specifically process 840 parses the characters in the sorted strings to determine the break points in the strtngs and the nature ot the break i e , whether the break is based on primary ditierence, secondary ditference, tertiary difference or other differences among the compared strings Process 840 achieves this goal bv making use ot Unicode character properties and collation pattern inherent in the ordering of the custom data During the execution of process 840 the collation engine may send clarifying questions to a CU because it any problem with the custom data occurs in process 840 it is likely that more information is needed to generate collation support that is completely correct For example, it process 840 wants to confirm a specific behavior ot a certain character process 840 may ask the CU to input more strings containing the character to exemplify the behavior of the character The query may also specitv the options of positioning a character and ask a CU to choose an option Further process 840 displays visual cues in the custom data to indicate the collation support A CU can thus adjust the ordering ot the strings to provide the collation engine instant feedback about the collation support In an exemplary embodiment of the invention at each action in process 840 the current representation of the relationship between codepoints and sort weights as deseubed by the custom data and validated by the collation engine is stored The collation engine can then reference stored collation data at any time thus enabling the CU to continue to refine the collation data In embodiments of the invention when analyzing the collation patterns tor example the weighting structures in the custom data, the collation engine first starts with the Windows*1 default table The collation engine then goes to the existing exception and compression tables, and then creates internal exception and/or compression tables as well as additional data when necessary The goal ot the collation engine is to create the minimum subset of the collation support required to capture the ordering in the custom data Theretore if a CU knows what the minimum subset is, the CU may present it to TOOL 204 duectly The majority ot the complexity ot the collation engine's analysis work comes from the fact that a CU rarely has the minimum subset concerning a given language More specifically as shown in FIGURE 12, process 840 parses the characters in each string one character at a time In one exemplary embodiment of the invention, process 840 first creates a pointer pointing to the first character of each string in the custom data See block 874 Process 840 separates each string into different groupings based on the character that the pointer is pointing to (hereinafter "pointer character") Process 840 first groups strings based on the primary difference I e the alphabetic weight ot the pointer character in each string See block 876 Process 840 analyzes the ordering ot the strings and determines the alphabetic weight of each pointer character Process 840 further groups the groups ot strings resulting trom executing block 876 based on the secondary differences 1 e , diacritic weighting ot the pointer character in each string See block 880 Ne\t, process 840 further groups the groups ot strings resulting trom executing block 878 based on the tertiary difference le the casing weight ot the pointer character in each string See block 880 After finding the break point and the nature ot the break based on the pointer character in each string, process 840 determines it there are other characters in the strings See decision block 882 It the answer is YES process 840 advances the pointer in each string to the next character in each string or to NULL if there is no further character in a string See block 884 From there process 840 returns to block 876 and begins to group strings based on the primary, secondary or tertiary difference ot the pointer character in each string At the end of the loop, process 840 identifies both the first break point for each string and an initial ordering ot the initial characters in the strings In embodiments of the invention, process 840 treats each character as being a unique sorting element and waits until an apparent contradiction is found in the data prior to looking for any expansions compressions and other constructs that cause collation to be more complicated In embodiments of the invention during one grouping section if a ditteience appears to be ignored at some level it will be ignored by the collation engine for the rest of this grouping section For example process 840 may examine the following custom data In this sample there are variations in case and diacritics The first grouping (block. 876) groups the data into "c" grouping based on the alphabetic weight of the first character It ignores the variations in case and diacritics However during the second grouping (block 878) process 840 notices that the lower case "c" comes after the plain lower case "c " During the third grouping (block 880) process 840 further notices that the lower case "c" comes before the upper case "C " 1 heretore by analyzing this sample data process 840 identifies these collation rules lower case "c" comes before upper case "C " and the plain lower case V comes before the lower case "c"" During Phase 3, the presence of special collation rules is determined and analyzed as well The special collation rules include for example, the "REVERSE DIACRITIC" rule tor collation in French In French diacritics are evaluated in a string from back to from front Therefore the word "cote" sorts before the word "cote" in French, while other languages would not sort the words this way Another example is the "DOUBLE COMPRESSION" rule seen in Hungarian where the existence of a grapheme such like "dsz" implies that the grapheme "ddsz" is treated as "dszdsz" for collation purpose In embodiments ot the invention, these special rules' are saved as additional data for the collation support of the custom data If the answer to decision block 882 is NO, meaning that process 840 has processed all the characters in each string process 840 performs a meta-analysis of the groupings See block 886 The meta-analysis examines the way that specific characters such as diacritics and other combining marks, as well as scripts in general, are handled as compared with existing Windows® sorts For example the meta-analysis may note the different behavior of the use of Anusvara across manv of the Indie languages within Windows® and the custom data The meta-analysis will use similarity to guide decisions about the custom data It the decision is incorrect the CU tan override it in later review of the collated custom data After identifying collation rules tor the custom data, in some embodiments ot the invention, the collation engine may test the collation rules FIGURE 13 illustrates a routine 814 that tests current collation rules The discussion of routine 814 will leference the test surface 700 illustrated in FIGURES 7A and 7B As discussed above, test surface 700 may receive either a correctly sorted hst of strings or an unsorted list ot strings If the test surface 700 receives a correctly sorted list of strings, a CU mav ventv whether the list of stnngs remains unchanged after applying the current collation rules It test surtace 700 receives an unsorted list ot strings, the CU is then given the opportunity to confimi whether the collation on the unsorted test data is correct If the collation is not correct the CU can adjust the ordering to assist the resolution ot the collation problem More specifically, as shown in FIGURE 13, routine 814 first determines whether it receives test data See decision block 888 If the answer is NO, routine 814 will not proceed If the answer is YES routine 814 collates the test data based on current collation rules See block 890 As illustrated in FIGURE 7B, in embodiments of the invention, the collated test data will be presented to the CU through a test surface 700 The CU indicates whether the collation is correct or not Routine 814 determines whether it has received atfimiation trom the user See block. 892 If the answer is YES meaning that the collation is correct routine 814 proceeds to insert the collated test data which has been properly validated and verified into the custom data See block 894 Routine 814 then returns to decision block 888 to determine whether additional test data has been received trom the CU If the answer to decision block 892 is NO meaning that the CU does not approve the collation support routine 814 proceeds to present an interface for recen ing corrections from the CU to the current ordering of the collated test data The test data will then be regarded as verified by CU See block 896 In some embodiments of the invention the test surface 700 allows the CU to drag and drop a string to its proper place Routine 814 then proceeds to insert the verified but invalidated test data back to the custom data See block 898 In this situation, the collation creation routine 804 (FIGURE 9) will be performed on the updated custom data again As a result, proper collation rules will be created accoidirtg to the verified test data While the preferred embodiment of the invention has been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the invention. We Claims :- The embodiments of the invention in which an exclusive property or privilege is claimed are defined as follows: 1. A computer-implemented method for automatically establishing collation support for sorted linguistic data, comprising: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyzing the sorted linguistic data to create a new collation support. 2. The computer-implemented method of Claim 1, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string. 3. The computer-implemented method of Claim 2, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string. 4. The computer-implemented method of Claim 2, further comprising analyzing behaviors of special characters. 5. The computer-implemented method of Claim 4, wherein special characters include diacritics, combining marks, and scripts. 6. The computer-implemented method of Claim 1, further comprising preprocessing the sorted linguistic data prior to executing (a) and (b). 7. The computer-implemented method of Claim 6, wherein pre-processing the sorted linguistic data includes validating the sorted linguistic data to ensure the generation of corrected collation support based on the sorted linguistic data. 8. The computer-implemented method of Claim 7, wherein validating the sorted linguistic data includes: identifying at least one problem in the sorted linguistic data; requesting correction to the sorted linguistic data; and applying the correction to the sorted linguistic data upon receiving the correction. 9. The computer-implemented method of Claim 6, wherein pre-processing the sorted linguistic data includes normalizing the sorted linguistic data. 10. The computer-implemented method of Claim 1, further comprising: collating the sorted linguistic data using the collation support; verifying the collated linguistic data; and repeating (a) and (b) on the collated linguistic data if the verification results in a change to the collated linguistic data. 11. The computer-implemented method of Claim i 0, wherein verifying the collated linguistic data includes determines whether the ordering of the collated linguistic data is correct. 12. The computer-implemented method of Claim 1, further comprising testing the collation support. 13. The computer-implemented method of Claim 12, wherein testing the collation support includes: receiving test data: and collating the test data using the collation support. 14. The computer-implemented method of Claim 13, further comprising: applying a correction to the collated test data, upon receiving a correction to the collated test data; inserting the corrected test data into the sorted linguistic data; and repeating (a) and (b) of Claim 1. 15. A computing system for automatically establishing collation support for sorted linguistic data, comprising a programmed data processor for: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyzing the sorted linguistic data to create a new collation support. 16. The computing system of Claim 15, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string. 17. The computing system of Claim 16, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string. 18. The computing system of Claim 16. wherein the programmed data processor also analyzes behaviors of special characters. 19. The computing system of Claim 18. wherein special characters include diacritics, combining marks, and scripts. 20. A computer-readable medium containing computer-executable instructions for automatically establishing collation support for sorted linguistic data that, when executed: (a) search existing collation support schemes to locate a matching collation support for the sorted linguistic data; and (b) if no existing collation support for the sorted linguistic data is found, analyze the sorted linguistic data to create a new collation support. 21. The computer-readable medium of Claim 20, wherein analysis of the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string. 22. The computer-readable medium of Claim 21, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on tfie secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weight of the character in focus in each string. 23. The computer-readable medium of Claim 21, wherein the computer-executable instructions when executed also analyze behaviors of special characters. 24. The computer-readable medium of Claim 23, wherein special characters include diacritics, combining marks, and scripts. 25. A computing system for automatically establishing collation support for sorted linguistic data, comprising: (a) an input device; (b) a display, wherein the display includes a user interface; and (c) a processor coupled with the input device and the display for: (i) receiving sorted linguistic data from the input device; (ii) establishing collation support for the sorted linguistic data; and (iii) displaying the sorted linguistic data on the user interface. 26. The computing system of Claim 25, wherein the processor also: sends a query to the user interface concerning the sorted linguistic data; and applies change to the sorted linguistic data upon receiving from the input device a change to the sorted linguistic data in responding to the query. 27. The computing system of Claim 25, wherein display of the sorted linguistic data on the user surface includes indicating visually a distinction between two compared strings in the sorted linguistic data. 28. The computing system of Claim 27. wherein the distinction includes a break point of a string in the sorted linguistic data. 29. The computing system of Claim 28, wherein the distinction includes the type of the weight difference at the break point. 30. The computing system of Claim 25, wherein establishing collation support for the sorted linguistic data includes: (a) searching existing collation support schemes to locate a matching collation support for the sorted linguistic data; and lb) if no existing collation support for the sorted linguistic data is found, analyzing die sorted linguistic data to create a new collation support. 31. The computing system of Claim 30, wherein the processor also preprocesses the sorted linguistic data so that it is consistent in its ordering and complete in coverage. 32. The computing system of Claim 30, wherein analyzing the sorted linguistic data to create a new collation support includes: analyzing each character ("character in focus") in each string contained in the sorted linguistic data, starting from the first character in each string. 33. The computing system of Claim 32, wherein analyzing each character in each string includes: identifying a primary weighting structure by grouping strings based on the primary weight of the character in focus in each string; identifying a secondary weighting structure by further grouping strings based on the secondary weight of the character in focus in each string; and identifying a tertiary weighting structure by further grouping strings based on the tertiary weighting of the character in focus in each string. 34. The computing system of Claim 32. wherein the processor also analyzes behaviors of special characters. 35. The computing system of Claim 34. wherein special characters include diacritics, combining marks, and scripts. 36. The computing system of Claim 25, wherein the processor also sends a verification request to the user interface after establishing collation support for the sorted linguistic data. 37. The computing system of Claim 36, wherein the processor also: applies a change to the sorted linguistic data upon receiving the change from the input device in responding to the verification request; and reestablishes collation support for the sorted linguistic data after applying the change. 38. The computing system of Claim 25, wherein the processor also tests the collation support after establishing collation support for the sorted linguistic data. 39. The computing system of Claim 38, wherein the test of the collation support includes: receiving test data from the input device: and collating the test data using the collation support. 40. The computing system of Claim 39, wherein the processor also: displays the collated test data to the user interface; applies a correction to the collated test data upon receiving a correction to the collated test data from the input device; inserts the corrected test data into the sorted linguistic data: and reestablishes collation support for the sorted linguistic data.