Multilingual electronic transfer dictionary containing topical codes and method of use

Abstract

A multilingual electronic transfer dictionary provides for automatic topic disambiguation by including one or more topic codes in definitions contained the dictionary. Automatic topic disambiguation is accomplished by determining the frequencies of topic codes within a block of text. Dictionary entries having more frequently occurring topic codes are preferentially selected over those having less frequently occurring topic codes. When the topic codes are members of a hierarchical topical coding system, such as the International Patent Classification system, an iterative method can be used with starts with a coarser level of the coding system and is repeated at finer levels until an ambiguity is resolved. The dictionary is advantageously used for machine translation, e.g. between Japanese and English.

Claims

I claim:

1. An electronic dictionary comprising; a memory that contains a data structure composed of a plurality of records, each record comprising representations of the following:

a first term, a second term, and a topical code, wherein said first term is in a first language, said second term is in a second language and said topical code indicates a topical area in which the second term is a translation of the first term,

a record in the data structure having been generated by (a) providing a collection of textual objects in the first or second language, each textual object having been associated with one or more codes that identify a topic to which the textual object pertains, and (b) entering into the record representations of a term from the textual object, a topical code associated with the textual object, and the corresponding term from a translation of the textual object into the other language.

2. A method using an electronic dictionary of claim 1 for selecting topic-appropriate translations of terms in a textual object in a first language into a second language comprising the steps of:

(a) providing an electronic dictionary of claim 1 containing records comprising representations of terms in said first language and said second language;

(b) scanning a textual object in said first language to identify each occurrence of a term in said textual object in a record of said electronic dictionary;

(c) inserting each topical code associated with each of said records identified in step (b) into a data structure that provides for counting of the frequency of occurrence of each topical code; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, selecting the term associated with the most frequently occurring topical code.

3. The method of claim 2, wherein step (c) is performed by generating a table associating each topical code occurring in the textual object with its frequency of occurrence.

4. The method of claim 2, wherein step (c) is performed by the use of a map class.

5. The electronic dictionary of claim 1, each record further comprising a representation of the part of speech for said first term.

6. The electronic dictionary of claim 1, each record further comprising representations of: the language of said first term and the language of said second term.

7. The electronic dictionary of claim 1, wherein at least some of said records further comprise a representation of a third term, said third term being a translation of the first term into a third language in the topical area of said topical code.

8. The electronic dictionary of claim 1, wherein one of said languages is Japanese and another language is English.

9. The electronic dictionary of claim 1, wherein at least one of said terms is represented in Unicode.

10. A method using an electronic dictionary of claim 9 for selecting topic-appropriate translations of terms in a textual object in a first language into a second language comprising the steps of:

(a) providing an electronic dictionary of claim 9 containing records comprising representations of terms in said first language and said second language;

(b) scanning a textual object in said first language to identify each occurrence of a term in said textual object in a record of said electronic dictionary;

(c) inserting each topical code associated with each of said records identified in step (b) into a data structure that provides for counting of the frequency of occurrence of each topical code; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, selecting the term associated with the most frequently occurring topical code.

11. The electronic dictionary of claim 1, wherein said topical code is a member of a hierarchical topical coding system.

12. The electronic dictionary of claim 11, wherein said hierarchical topical coding system is the International Patent Classification system.

13. A method of using an electronic dictionary of claim 11 for selecting topic-appropriate translations of terms in a textual object in a first language into a second language comprising the steps of:

(a) providing an electronic dictionary of claim 11 containing records comprising representations of terms in said first language and said second language;

(b) scanning a textual object in said first language to identify each occurrence of a term in said textual object in a record of said electronic dictionary;

(c) inserting each topical code associated with each of said records identified in step (b) into a plurality of data structures that provide for counting of the frequency of occurrence of each hierarchical topical code at a plurality of levels of said hierarchy; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, counting the frequency of the topical codes associated with each of said plurality of terms, and selecting the term associated with the most frequently occurring topical code;

wherein step (d) is applied iteratively, first at a coarser level of the topical code hierarchy, then at successively more detailed levels of the topical code hierarchy until topical ambiguities are either completely resolved or resolved to the extent allowed by the most detailed level of the hierarchy.

14. The method of claim 13, wherein step (c) is performed by generating a plurality of tables associating at each of a plurality of selected levels of said hierarchy each topical code occurring in the textual object with its frequency of occurrence.

15. The method of claim 13, wherein step (c) is performed by the use of a plurality of map classes.

16. The electronic dictionary of claim 11, wherein at least one of said terms is represented in Unicode.

17. A method of using an electronic dictionary of claim 16 for selecting topic-appropriate translations of terms in a textual object in a first language into a second language comprising the steps of:

(a) providing an electronic dictionary of claim 16 containing records comprising representations of terms in said first language and said second language;

(b) scanning a textual object in said first language to identify each occurrence of a term in said textual object in a record of said electronic dictionary;

(c) inserting each topical code associated with each of said records identified in step (b) into a plurality of data structures that provide for counting of the frequency of occurrence of each hierarchical topical code at a plurality of levels of said hierarchy; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, counting the frequency of the topical codes associated with each of said plurality of terms, and selecting the term associated with the most frequently occurring topical code;

wherein step (d) is applied iteratively, first at a coarser level of the topical code hierarchy, then at successively more detailed levels of the topical code hierarchy until topical ambiguities are either completely resolved or resolved to the extent allowed by the most detailed level of the hierarchy.

18. The electronic dictionary of claim 11, wherein said hierarchical topical coding system is a technology classification system.

19. An electronic translator comprising:

a electronic dictionary comprising a memory that contains a data structure composed of a plurality of records, each record comprising representations of a first term, a second term provided in a second language, and a topical code for records having plural meanings within a scope defined by the topical code, said topical code indicating a topical area of text for translation of the second term from the first term, and

means for providing translation of a textual object composed of representations of terms in said first language into representations of terms in said second language, said means for providing translation comprising:

(a) means for scanning a textual object in said first language to identify each occurrence of a term in said textual object in a record of said data structure;

(b) means for inserting each topical code associated with each of said records identified in step (a) into a data structure that provides for counting of the frequency of occurrence of each topical code; and

(c) means for selecting the term associated with the most frequently occurring topical code whenever there occur a plurality of terms in the second language corresponding to a term in the first language.

20. A method of generating a record in a translation dictionary composed of a plurality of records, each record representing a first term, a second term, and a topical code, wherein said first term is in a first language, said second term is in a second language and said topical code indicates a topical area in which the second term is a translation of the first term by:

(a) providing a collection of textual objects in the first or second language, each textual object having been associated with one or more codes that identify a topic to which the textual object pertains, and

(b) entering into the record representations of a term from the textual object, a topical code associated with the textual object, and the corresponding term from a translation of the textual object into the other language.

Description

COPYRIGHT

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the PTO patent file or records, but otherwise reserves all copyright rights whatsoever. Copyright .COPYRGT. 1999 ISTA.

TECHNICAL FIELD

The present invention relates to multilingual electronic dictionaries that may be used for machine translation.

DEFINITIONS

"Multilingual" means pertaining to two or more languages.

Unless the context otherwise requires, the terms "subject", "topic" and "field" are virtually synonymous in this disclosure, as are the terms "dictionary", "glossary" and "lexicon."

BACKGROUND ART

The existence of field-dependent translations of terms has long been a problem for both ordinary human translation and for machine translation. A term in a source language, for example, Japanese, may have more than one translation in a target language, for example, English, depending on the subject, topic or field of the document being translated. For example, the word "soshiki" in Japanese would be translated to the English "tissue" in a medical document, to the English "weave" in the case of textiles, or to the English "microstructure" in the case of metallurgy.

Conventional machine translation programs, for example, Systran.RTM., contain topical dictionaries or glossaries. The user must manually select topical dictionaries appropriate for the document being translated. In this case, there is one dictionary per topic, for example, chemistry or medicine, rather than one topic per dictionary entry or record as in the current invention.

The machine translation program METAL contains three individual lexicons: a German monolingual lexicon, an English monolingual lexicon and a German-English bilingual lexicon (Katherine Koch, "Machine Translation and Terminology Database--Uneasy Bedfellows?" Lecture Notes in Artificial Intelligence 898, Machine Translation and the Lexicon, Petra Steffens, ed., Springer, Berlin, 1995, pp. 131-140.) Semantic information is disclosed only for the monolingual lexicons, not for the bilingual lexicon. Even for the monolingual lexicon, only 15 semantic types are disclosed, such as "abstract", "concrete", "human", "animal" and "process." These are quite different from the topical classifications that are the subject of the current invention.

Brigitte Blaser, in "TransLexis: An Integrated Environment for Lexicon and Terminology Management," Lecture Notes in Artificial Intelligence 898, Machine Translation and the Lexicon, Petra Steffens, ed., Springer, Berlin, 1995, pp. 158-173, discloses the incorporation of concepts, including broader concepts, narrower concepts and related concepts in a lexicon database management system for machine translation. However, this disclosure does not extend to the incorporation of subject codes, notably hierarchical subject codes, in a multilingual electronic dictionary not does it disclose the use of concepts or other subject area information for automatic topic discrimination in machine translation. Notably, these concepts are not subject areas; rather, they constitute the interlingua for interlingua-based machine translation.

Masterson disclosed a means of automatic sense disambiguation for the machine translation of Latin to English in the article "The thesaurus in syntax and semantics," Mechanical Translation, Vol. 4, pp. 1-2, 1957. As described by Wilks, Slator, and Guthrie in Electric Words: Dictionaries, Computers and Meanings, MIT Press, Cambridge, Mass., 1996, pp. 88-89, Masterson disclosed a nonstatistical method using the headings in Roget's Thesaurus.

In this predecessor to interlingua-based machine translation, Masterson disclosed a concept thesaurus for the words in a Latin passage from Virgil's Georgics. Each word stem from the Latin passage was associated with a set of head numbers from Roget's International Thesaurus by translating the word stems into English and selecting the head numbers for the corresponding English words. For example, the three Latin noun stems, "agricola", "terram" and "aratro" have the following heads (where the head words are shown instead of the head numbers):

AGRICOLA: Region, Agriculture

TERRAM: Region, Land, Furrow

ARATRO: Agriculture, Furrow, Convolution

In the case of the text, "Agricola incurvo in terram dimovit aratro", the heads that occur more than once are selected into a concept set. In the above example, this yields the following sets:

AGRICOLA: Region, Agriculture

TERRAM: Region, Furrow

ARATRO: Agriculture, Furrow

Finally, the English words listed under each head in Roget's Thesaurus are intersected to leave the appropriate translation candidates. In the current example, this yields the following sets:

AGRICOLA: farmer, ploughman

TERRAM: soil, ground

ARATRO: plough, ploughman, rustic

Masterson does not disclose a multilingual dictionary, nor does she disclose use of topical codes in a multilingual dictionary for disambiguation.

Kenneth W. Church, William A. Gale and David E. Yarowsky, in U.S. Pat. No. 5,541,836, also disclosed the use of the categories from Roget's Thesaurus in automatically disambiguating word/sense pairs and the use of bilingual bodies of text to train word/sense probability tables. Church et al do not disclose a multilingual dictionary nor do they disclose the use of topical codes in a multilingual dictionary for sense disambiguation.

JuneJei Kuo, in U.S. Pat. No. 5,285,386, "Machine Translation Apparatus Having Means for Translating Polysemous Words Using Dominated Codes", discloses interlingua-based machine translation using semantic codes in the role of the interlingua. While Kuo discloses transfer dictionaries, these are not multilingual transfer dictionaries. Rather they are transfer dictionaries between the semantic codes, the interlingua in this case, and words in the target language.

The requirement of manually selecting a topical dictionary is a barrier to the automated translation of documents such as patent documents that cover many topical areas. Also, the semantic methods of the interlingua-based approaches do not provide for automatically determining the topic of the document being translated. There is a need for a means for automatically determining the most appropriate target definition depending on the topic of the document. Such a means is referred to as "automatic topic disambiguation" in the text below.

Elizabeth Liddy, Woojin Palk and Edmund Szi-li Wu, in U.S. Pat. No. 5,873,056, "Natural Language Processing System for Semantic Vector Representation Which Accounts for Lexical Ambiguity", disclose a monolingual lexical database that contains nonhierarchical subject codes assigned to each word in the database. To avoid unnecessary reiteration of prior teachings, the disclosure of each reference cited herein is hereby incorporated by reference.

SUMMARY OF THE INVENTION

This invention provides a multilingual electronic dictionary comprising a memory that contains a data structure composed of a plurality of records, each record comprising representations of the following: a first term (in a first language), a second term (in a second language), and a topical code. The topical code indicates a topical area in which the second term is a translation of the first term.

Such an electronic dictionary allows for selecting topic-appropriate translations of terms in a textual object in a first language into a second language. This is accomplished by:

(a) providing an electronic dictionary containing records comprising representations of terms in the first language and the second language;

(b) scanning a textual object in the first language to identify each occurrence of a term in the textual object in a record of the electronic dictionary;

(c) inserting each topical code associated with each of the records identified in step (b) into a data structure that provides for counting of the frequency of occurrence of each topical code; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, selecting the term associated with the most frequently occurring topical code.

In one embodiment of the invention, step (c) is performed by generating a table associating each topical code occurring in the textual object with its frequency of occurrence.

In another embodiment, as illustrated in the Examples below, step (c) is performed by the use of a map class.

In preferred embodiments of the apparatus and methods of this invention, one or more of the terms are represented in Unicode.

In the electronic dictionary of the present invention, each record may optionally include a representation of the part of speech for the first term, or for each term. However this is not a necessary field in the record.

Also, each record may optionally include a representation of the language of the first term and of the language of said second term. Alternatively a specific representation of the language (e.g. its name or a code such as JP indicating Japanese) may be omitted where an indication of the language is inherent in the structure of the record. (e.g. first field is always Japanese; second is always English).

The dictionary of the present invention is not limited to bilingual records, but may be generated with records that accommodate a third language, or any number of languages.

In preferred embodiments of the invention, the topical coding system is a hierarchical one, e.g. the International Patent Classification system.

Such embodiments are desirably used for selecting topic-appropriate translations of terms in a textual object in a first language into a second language by doing the following:

(a) providing an electronic dictionary containing records comprising representations of terms in the first language and the second language along with a topical code from a hierarchical system;

(b) scanning a textual object in the first language to identify each occurrence of a term in the textual object in a record of said electronic dictionary;

(c) inserting each topical code associated with each of the records identified in step (b) into a plurality of data structures that provide for counting of the frequency of occurrence of each topical code at a code at a plurality of levels of the hierarchy; and

(d) whenever there occur a plurality of terms in the second language corresponding to a term in the first language, selecting the term associated with the most frequently occurring topical code;

wherein steps (c) and (d) are applied iteratively, first at a coarser level of the topical code hierarchy, then at successively more detailed levels of the topical code hierarchy until topical ambiguities are either completely resolved or resolved to the extent allowed by the most detailed level of the hierarchy.

DISCLOSURE OF INVENTION

The multilingual electronic dictionary of this invention provides for automatic topic disambiguation by including one or more topic codes in definitions contained the dictionary.

A dictionary record according to this invention is part of a data structure contained in amachine-accessible memory. This record contains at least one topic code comprising the following items (with an example of a record for the Japanese term "soshiki" as shown in Table 1):

            TABLE 1
            Term in Language 1 (Japanese)    "soshiki"
            Part of Speech                   Noun
            Term in Language 2 (English)     tissue
            Part of Speech                   Noun
            Topic Code                       A61K 47/38

            TABLE 2
            Term in Language 1 (Japanese)    "soshiki"
            Part of Speech                   Noun
            Term in Language 2 (English)     tissue
            Part of Speech                   Noun
            Term in Language 3 (German)      Gewebe
            Part of Speech                   Noun
            Term in Language 4 (French)      tissu
            Part of Speech                   Noun
            Topic Code                       A61K 47/38

            TABLE 3
            Term in Language 1 (Japanese)    "maku"
            Part of Speech                   Noun
            Term in Language 2 (English)     membrane
            Part of Speech                   Noun
            Topic Code 1                     A61K 47/38
            Topic Code 2                     B01D 61/36

                TABLE 4
                LEVEL                    Example
                FAMILY                   A
                CLASS                    A61
                SUBCLASS                 A61K
                MAIN GROUP               A61K 47
                SUBGROUP                 A61K 47/38

        <record> 0xe000      // start of record
        </record> 0xe001      // end of record
        <lang1> 0xe011      // start of first language term field
        </lang1> 0xe021      // end of first language term field
        <lang2> 0xe013      // start of second language term field
        </lang2> 0xe023      // end of second language term field
        <topic> 0xe0a0      // start of topical code field
        </topic> 0xe0a1      // end of topical code field

    class Node
    {
        DWORD         m_ThisNode; // number of this node
        DWORD         m_NumberOfElements;
                      // number of node elements currently in node
        DWORD         m_MaxNumberOfElements;
        DWORD         m_ParentNode;
        DWORD         m_ParentElement;
                      // number of parent element in parent node
        DWORD         m_FirstElement;
        DWORD         m_bBottomNode; // true if this is a bottom node
        DWORD         m_LastElement; // last element in linked list
        NodeElement   m_Elements[127];
    };

    class NodeElement
    {
        wchar_t     key[10];    // hold up to 10 characters from
                                record
        DWORD       childNode;  // for keys less than this key
        DWORD       next;       // next element in node
        DWORD       record;     // number of record in dictionary
    };

    class Record
    {
        wstring wsL1Term;   // term from first language
        wstring wsL2Term;   // term from second language
        wstring wsTopic;    // topical code
    }

              TABLE 5
              paMaster[0]                  NULL
              (intervening paMasters)      (all NULL)
              paMaster[m]                  NULL
              paMaster[m + 1]              NULL
              (intervening paMasters)      (all NULL)
              paMaster[lenL1S-1]           NULL

          TABLE 6
          paMaster[0]              *paRecords (0.sup.th)
          (intervening paMasters)  (may include NULL)
          paMaster[m]              *paRecords (m.sup.th)
          paMaster[m + 1]          NULL
          (intervening paMasters)  (may include NULL)
          paMaster[lenL1S-1]       *paRecords ( (lenL1S-1).sup.th )
                                   (may be NULL)

              TABLE 7
              paRecord[0]        Pointer to first record
              paRecord[1]        Pointer to second record
              PaRecord[2]        Pointer to third record

        <record> 0xe000      //start of record
        </record> 0xe001      //end of record
        <lang1> 0xe010      //start of first language term field
        </lang1> 0xe011      //end of first language term field
        <lang2> 0xe020      //start of second language term field
        </lang2> 0xe021      //end of second language term field
        <lang3> 0xe030      //start of third language term field
        </lang3> 0xe031      //end of third language term field
        <lang4> 0xe040      //start of fourth language term field
        </lang4> 0xe041      //end of fourth language term field
        <topic> 0xe0a0      //start of topical code field
        </topic> 0xe0a1      //end of topical code field

    class Node
    {
      DWORD           m_ThisNode: //number of this node
      DWORD           m_NumberOfElements; //number of node elements
                      currently in node
      DWORD           m_MaxNumberOfElements;
      DWORD           m_ParentNode;
      DWORD           m_ParentElement; //number of parent element in
                      parent node
      DWORD           m_FirstElement;
      DWORD           m_bBottomNode; //true if this is a bottom node
      DWORD           m_LastElement; //last element in linked list
      NodeElement     m_Elements[127];
    };

    class NodeElement
    {
      wchar_t         key[10];    //hold up to 10 characters from record
      DWORD           childNode;  //for keys less than this key
      DWORD           next;       //next element in node
      DWORD           record;     //number of record in dictionary
    };

        class Record
        }
          wstring     wsL1Term;       //term from first language
          wstring     wsL2Term;       //term from second language
          wstring     wsL3Term;       //term from third language
          wstring     wsL4Term;       //term from fourth language
          wstring     wsTopic;        //topical code
        }

              TABLE 8
              paMaster[0]                  NULL
              (intervening paMasters)      (all NULL)
              paMaster[m]                  NULL
              paMaster[m + 1]              NULL
              (intervening paMasters)      (all NULL)
              paMaster[lenL1S-1]           NULL

              TABLE 9
              paRecord[0]        Pointer to first record
              paRecord[1]        Pointer to second record
              paRecord[2]        Pointer to third record

          TABLE 10
          paMaster[0]              *paRecords (0.sup.th)
          (intervening paMasters)  (may include NULL)
          paMaster[m]              *paRecords (m.sup.th)
          paMaster[m + 1]          NULL
          (intervening paMasters)  (may include NULL)
          paMaster[lenL1S-1]       *paRecords ( (lenL1S-1).sup.th)
                                   (may be NULL)

    for(int i = 0; i < lenL1S; i++)
    {
      if( paMaster[i] ! = NULL )
      {
        int nRecords = paMaster[i].GetLength( ); //number of dictionary
        records copied
        CPtrArray* pRecords = paMaster [I];
        for( int j = 0; j < nRecords; j++)
        {
          Record* pRecord = pRecords[j];
          pair < wstring, int > newPair; //pair for insertion into
          smapClassCode
          newPair.first = pRecord- > wsTopic.left(3);
          newPair.second = 1;
          smapClassCode.insert( newPair );
        }
      }
    }

        int nMostFrequent = 0; //initialize to first record in paRecord
        int nMaxCount = 0; //for following the maximum count
        for( int i = 0; i < paRecord.GetLength( ); i++)
        {
          Record* pRecord = pRecords[i];
          wstring wsTest = pRecord- > wsTopic.left(3);
          int nCount = smapClassCode.count( wsTest );
          if( nCount > nMaxCount )
          {
            nMostFrequent = i;
            nMaxCount = nCount;
          }
        }

        <record> 0xe000      //start of record
        </record> 0xe001      //end of record
        <lang1> 0xe011      //start of first language term field
        </lang1> 0xe021      //end of first language term field
        <lang2> 0xe013      //start of second language term field
        </lang2> 0xe023      //end of second language term field
        <topic> 0xe0a0      //start of topical code field
        </topic> 0xe0a1      //end of topical code field

    class Node
    {
       DWORD           m_ThisNode; //number of this node
       DWORD           m_NumberOfElements; //number of node
                       elements currently in node
       DWORD           m_MaxNumberOfElements;
       DWORD           m_ParentNode;
       DWORD           m_ParentElement; //number of parent element in
                       parent node
       DWORD           m_FirstElement;
       DWORD           m_bBottomNode; //true if this is a bottom node
       DWORD           m_LastElement; //last element in linked list
       NodeElement     m_Elements[127];
    };

    class NodeElement
    {
       wchar_t        key[10];    //hold up to 10 characters from record
       DWORD          childNode;  //for keys less than this key
       DWORD          next;       //next element in node
       DWORD          record;     //number of record in dictionary
    };

    class Record
    {
       wstringwsL1Term;        //term from first language
       wstringwsL2Term;        //term from second language
       vector<wstring>   vecTopics;  //array of topical code
                               wstrings
    }

              TABLE 11
              paMaster[0]                  NULL
              (intervening paMasters)      (all NULL)
              paMaster[m]                  NULL
              paMaster[m + 1]              NULL
              (intervening paMasters)      (all NULL)
              paMaster[lenL1S-1]           NULL

          TABLE 12
          paMaster[0]                  *paRecords (0.sup.th)
          (intervening paMasters)      (may include NULL)
          paMaster[m]                  *paRecords (m.sup.th)
          paMaster[m + 1]              NULL
          (intervening paMasters)      (may include NULL)
          paMaster[lenL1S-1]           NULL

              TABLE 13
              paRecord[0]        Pointer to first record
              paRecord[1]        Pointer to second record
              paRecord[2]        Pointer to third record

    for( int i = 0; i < lenL1S; i++)
    {
      if( paMaster[i] ! = NULL)
      {
        int nRecords = paMaster[i].GetLength( ); //number of dictionary
        records copied
        CPtrArray* pRecords = paMaster[I];
        for( int j = 0; j <nRecords; j++)
        {
          Record* pRecord = pRecords[j];
          vector < wstring > & vecTopics = pRecord- > vecTopics;
          for(int k = 0; k < vecTopics.size( ) ; k++)
          {
            wstring& wsTopic = vecTopics[k];
            pair < wstring, int > newPair; //pair for insertion into
            smapClassCode
            newPair.first = pRecord- > wsTopic.left(3);
            newPair.second = 1;
            smapClassCode.insert( newPair );
          }
        }
      }
    }

        int nMostFrequent = 0; //initialize to first record in paRecord
        int nMaxCount = 0; //for following the maximum count
        for( int i = 0; i < paRecord.GetLength( ); i++)
        {
          Record* pRecord = pRecords[i];
          wstring wsTest = pRecord- > wsTopic.left(3);
          int nCount = smapClassCode.count( wsTest );
          if( nCount > nMaxCount)
          {
            nMostFrequent = i;
            nMaxCount = nCount;
          }
        }

        <record> 0xe000      //start of record
        </record> 0xe001      //end of record
        <lang1> 0xe011      //start of first language term field
        </lang1> 0xe021      //end of first language term field
        <lang2> 0xe013      //start of second language term field
        </lang2> 0xe023      //end of second language term field
        <topic> 0xe0a0      //start of topical code field
        </topic> 0xe0a1      //end of topical code field

    class Node
    {
      DWORD m_ThisNode; //number of this node
      DWORD m_NumberOfElements; //number of node elements
      currently in node
      DWORD m_MacNumberOfElements;
      DWORD m_ParentNode;
      DWORD m_ParentElement; //number of parent element in parent
      node
      DWORD m_FirstElement;
      DWORD m_bBottomNode; //true if this is a bottom node
      DWORD m_LastElement; //last element in linked list
      NodeElement  m_Elements[127];
    };

        class NodeElement
        {
          wchar_t key[10]; //hold up to 10 characters from record
          DWORD childNode; //for keys less than this key
          DWORD next;  //next element in node
          DWORD record;  //number of record in dictionary
        };

        class Record
        {
          wstring wsL1Term;        //term from first language
          wstring wsL2Term;        //term from second language
          wstring wsTopic;         //topical code
        }

              TABLE 14
              paMaster[0]                  NULL
              (intervening paMasters)      (all NULL)
              paMaster[m]                  NULL
              paMaster[m + 1]              NULL
              (intervening paMasters)      (all NULL)
              paMaster[lenL1S-1]           NULL

          TABLE 15
          paMaster[0]                  *paRecords (0.sup.th)
          (intervening paMasters)      (may include NULL)
          paMaster[m]                  *paRecords (m.sup.th)
          paMaster[m + 1]              NULL
          (intervening paMasters)      (may include NULL)
          paMaster[lenL1S-1]           NULL

              TABLE 16
              paRecord[0]        Pointer to first record
              paRecord[1]        Pointer to second record
              paRecord[2]        Pointer to third record

    for( int i = 0; i < lenL1S; i++)
    {
      if( paMaster[i] ! = NULL )
      {
        int nRecords = paMaster[i].GetLength( ); //number of dictionary
        records copied
        CPtrArray* pRecords = paMaster[I];
        for( int j = 0; j < nRecords; j++)
        {
          Record* pRecord = pRecords[j];
          pair < wstring, int > newPair; //pair for insertion into
          smapCode
          newPair.first = pRecord- > wsTopic; //entire code because
          nonhierarchical
          newPair.second = 1;
          smapCode.insert( newPair );
        }
      }

        int nMostFrequent = 0; //initialize to first record in paRecord
        int nMaxCount = 0; //for following the maximum count
        for( int i = 0; i < paRecord.GetLength( ); i++)
        {
          Record* pRecord = pRecords[i];
          wstring wsTest = pRecord- > wsTopic; //entire code because
          nonhierarchical
          int nCount = smapCode.count( wsTest );
          if( nCount > nMaxCount )
          {
            nMostFrequent = i;
            nMaxCount = nCount;
          }
        }

• Inventors
  Engel, Alan;
• Assignee
  Paterra, Inc. (Villanova, PA)
• Published
  Dec-3-2002
• Current US Classes:
  704/10
  707/100
• Application #
  311925
• International Classes
  G06F 017/21
• Field of Search
  704/2-9 704/10 707/530 707/532 707/100 707/103
• Examiner
  Edouard; Patrick N.
• Agent
  Elman & Associates, Elman; Gerry J.
• US Patent References:
  4703425
  4775956
  5284386
  5311429
  5323311
  5357430
  5369763
  5541836
  5873056
  5963940
  6137911
  6269368