Patent Abstract:
A Process for analyzing full-text is provided for identifying  often-repea, high user interest, word phrases in a database. Often-repeated, high user interest, word phrases are defined as pervasive theme areas (PTAs). The process also allows the relationship defined as connectivity among the various PTAs to be identified. In addition, phrases that are in proximity to the PTAs and which are strongly supportive of the PTAs are identified. Numerical indices, figure of merit, and user defined thresholds are used to quantify relations between PTAs and among PTAs and phrases.

Full Description:
FIELD OF THE INVENTION 
     The present invention relates to database searching and particularly to full text database search methods and their mechanization and implementation. 
     BACKGROUND OF THE INVENTION 
     Due to the rapid expansion of electronic media storage capabilities, research analysts now have available massive databases of information which can be analyzed for various purposes. A major problem in practice is how to extract the essential information from these databases in a form readily amenable to analysis and interpretation. In other words, how does the analyst extract the collective wisdom contained in these large databases in a concise, readily understandable form. 
     The user may directly interact with the process of the present invention to select the often repeated phrases of high user interest referred to herein as pervasive theme areas (PTAs). Or, at the start of the process provide criteria for selection of the PTAs to enable the process of the present invention to define and select the PTAs from the sorted list content. Until recently, the co-word methodologies used key or index or title words as input, rather than input from the text directly. This led to universal criticism that the method input was subject to sources of bias and error from the indexing process, and the validity of the final results were questioned. In addition, the text database tended to consist of published papers, which had the index and key words. This also limited the types of text available for analysis. Recently, some progress has been reported in computer-assisted indexing using only the words found in the published literature. 
     Modern quantitative techniques utilize computer technology extensively, usually supplemented by network analytic approaches, and attempt to integrate disparate fields of information. One class of techniques exploits the use of co-occurrence phenomena. In co-occurrence analysis, phenomena that occur together frequently in some domain are assumed to be related, and the strength of that relationship is assumed to be related to the co-occurrence frequency. Networks of these co-occurring phenomena are constructed, and then maps of evolving topic fields are generated using the link-node values of the networks. Using these maps of structure and evolution, the information analyst can develop a deeper understanding of the interrelationships among the different information fields and the impacts of external intervention, and can recommend new directions for more desirable information portfolios. 
     One approach to co-occurrence analysis is co-word analysis. The origins of Co-word phenomena can be traced back to the pioneering work in: 1) lexicography to account for co-occurrence knowledge, and 2) linguistics to describe how affinity of two language units correlates with their appearance in the language. 
     In early co-word studies, words were classified on the basis of their co-occurrence with other words as well as their meanings. It was, however, observed that the reasons for two words co-occurring in the same context are not always relevant to a general linguistic description of a given language. The well-formedness of sentences to their lexical levels; i.e., how sensitive the meaning of a sentence is to substitution for one member of co-occurrence pair has been studied. A recent study included collocations as part of a linguistic model, whose goal was to relate any given meaning and all the texts that express it. Information retrieval research has focused on designing more efficient indexing tools using pairwise lexical affinities instead of keywords. Methods have been developed for locating interesting collocational expressions in a large body of text. These methods were based principally on the distribution of types and tokens in the body of text and on the analysis of the statistical patterns of neighboring words. 
     In the mid-1970s, a study was performed to examine relationships among themes in a novel using co-occurrence phenomena. An important term in the book was chosen, and a dictionary was constructed of all words in the book occurring in the same sentences as that word. A co-occurrence matrix which contained the co-occurrences among these related terms was constructed, and analyzed to eventually show the relations among all the associated terms in the mini-dictionary as they occurred in the original text. While the dictionary was restricted to single words, and the co-occurrence domain was restricted to sentences, the methodology did represent a major step forward in extracting word relations from text by their co-occurrences. 
     A recent update of this method employed frequency of co-occurrence to extract relatedness information from text. The study looked at co-occurrence using the sense-definition as the textual unit (entire definition of a sense of a word). The database used was the Longman Dictionary of Contemporary English (LDOCE) rather than free text. The method used single word frequencies only, and resulted in construction of networks of related words. It was concluded that co-occurrences of words in the LDOCE-controlled vocabulary in the definitions in LDOCE appeared to provide some useful information about the meanings of those words. Co-occurrences frequency correlated significantly with human judgements of relatedness, and the relatedness functions on co-occurrences yielded even higher correlations. 
     While the methods described above were useful for showing how relations among words and terms could be quantified and extracted from text, none were applied to extract relationships among topics of interest from large text databases. 
     It has been concluded in a recent study that co-word analysis may be satisfactorily performed on a set of documents by using either title words or keywords and that the main difference between results obtained is that keywords provided a much more detailed account of the subject studied for the case in point. 
     While there has been some progress in overcoming the dependency of co-word analysis on key or index words, limitations remain. From the above it is clear that the prior art has taken many varied approaches to search out related topic information from individual or multiple target databases. In light of the various problems and limitations of the foregoing approaches a simpler methodology less dependent on user bias and influences was imperative. 
     SUMMARY OF THE INVENTION 
     It is thus an object of the present invention to provide a database search methodology that is less dependent upon or eliminates user input bias. 
     It is yet another object of the present invention to eliminate the need for user specified search criteria such as keyword requirements. 
     It is further another object of the present invention to provide a search methodology capable of performing upon full-text database elements. 
     It is still another object of the present invention to provide a data-base search methodology employing co-word relationships. 
     The present invention is a system and method of performing full-text data base analyses without the requirement for any key or index words. The database may include any form of combination of forms of text, be it published article, report, or memo. The invention is the most direct means of extracting messages from large textual databases without relying on interpreting intermediate abstractions of text such as citations, key or index words, or titles. 
     The invention provides a revolutionary approach for identifying often-repeated phrases (a phrase in the present context is from one to &#34;N&#34; words, e.g., [METAL, METAL MATRIX, OR METAL MATRIX COMPOSITES], etc.) in any size document or combination of documents (defined as the database). Those often-repeated phrases which also have high interest to the user(s) of the invention can be defined as pervasive theme areas (PTA). The invention allows the relationships (defined as connectivity) among these PTA to be identified. The invention also allows phrases which are in close physical proximity to the PTA, and which are strongly supportive of the PTA, to be identified. The PTA can be research areas of emphasis, organizations, journals, people, patents, etc. A major unique feature of this invention is that the database consists of full text (any combination of journal papers, reports, memos, written speeches, etc.), not abstractions of text such as key words or index words, as previous methods have used. The full text method displays the richness of the fine structure relations in the text, and provides orders of magnitude more detail and useful information than the methods which rely on key or index words. 
    
    
     DETAILED DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a functional block diagram of the method of the present invention. 
     FIG. 2 is a functional block diagram expanding on Block 18 of FIG. 1. 
     FIG. 3 is a functional block diagram expanding on Block 22 of FIG. 1. 
     FIG. 4 is a functional block diagram expanding on Block 26 of FIG. 1. 
     FIG. 5 is a functional block diagram expanding on Block 30 of FIG. 1. 
    
    
     DETAILED DESCRIPTION 
     The present invention is a system and method as shown in FIG. 1. The system employs a scanner for digitizing document information content or other means for creating a data base in digital form. A computer program, for example as shown in Appendix A, can be used to make possible digital execution of the methodology of the present invention by means of a digital computer. A number of functions of the methodology of the present invention may be performed by human interaction. In the alternative a fully automated form of the system can perform all functions without human interaction where it is desirable to have the computer controlling the method of the invention by developing thresholds, numerical indices, figures of merit and other means of quantitative measurement directly from the database content itself. To those skilled in the art, manual data entry, optical scanning, and verbal input are means for information or data entry to a database of the type envisioned for application by the invention. Likewise any number of means for performing the functions of the invention including various types of digital computers and means for presenting or displaying the output of the inventions methodology may be used. 
     Referring to block 14 in FIG. 1 the invention 10 as depicted requires as a first step the assembly of information of interest to the user in a full-text database. Journals, papers, memos, reports, patents, and police records are just a few examples of the unlimited variety of documents that may be introduced into a database. The database may, of course, contain one or a combination of two or more documents. In the preferred embodiment of the invention the documents of interest are stored, regardless of the means of entry, on electronic media such as hard or floppy disk. CD-ROM, optical, and other high-density, rapid access storage media is also preferred for the system. Each individual or combination of documents may occupy a separate computer file in the system. In a preferred embodiment storage of digitally introduced documents has been in ASCII files. Other types of files will be dictated by the types of computer processing and the programs employed. 
     In the discussion that follows a phrase is defined as one or more words, where a word is used in the usual sense such as &#34;the printed word&#34;. More specifically a phrase is one word, adjacent double words, adjacent triple words . . . to adjacent m*words. Thus a single word phrase might be &#34;metal&#34;; an adjacent double word phrase &#34;metal matrix&#34;, and an adjacent triple word phrase &#34;metal matrix composite&#34;. 
     The user is required to prepare a list of trivial phrases, e.g. it, to, if, and, but, etc., that the user wishes to have excluded from the database as it is subjected to the invention&#39;s methodology. In the preferred embodiment the number of trivial phrases that can be selected is unlimited except for limitations imposed by computer memory size. The system can exclude trivial phrases at any time, however, in a preferred embodiment trivial phrases are excluded immediately prior to or during processing to identify pervasive theme areas discussed hereinafter. 
     The next step in the method of the present invention is the identification of pervasive theme areas (PTAs) as depicted in block 18 of FIG. 1 and in FIG. 2. Thus the system and methodology are required to use the entire full-text database to create lists of phrases as at 40 in FIG. 2. More specifically the system and methodology require that separate lists of all one (1) word phrases, adjacent two (2) word phrases, adjacent three (3) word phrases . . . and adjacent n*word phrases be prepared. The phrases in each list are then counted as in 42 and then sorted in order in each list by frequency of occurrence as shown in 44 and in Table 1. The user, of course, can as an option sort the phrases alphabetically by consecutive word order in the phrase, i.e. e.g., first word, or second word, or through to the last word. The sorted phrases are then displayed as at 46 by any of various means known to those skilled in the art such as print-out list or graphs or video means to permit user interaction as at 48. 
     At 48 FIG. 2 the user selects pervasive theme areas (PTAs) which are often repeating phrases in the data base of high user interest. See Table 2. 
     
                                           TABLE 1__________________________________________________________________________HIGH FREQUENCY SINGLE WORDS FROM FASACFREQ     WORD   FREQ    WORD   FREQ    WORD__________________________________________________________________________4170     SYSTEMS           2611    PHYSICS                          2351    OPTICAL4139     INSTITUTE           2587    WAVES  2314    TIME2883     COMPUTER           2564    MOSCOW 2268    MATERIALS2764     DATA   2466    CONTROL                          2248    SURFACEHIGH FREQUENCY DOUBLE WORDS FROM FASACFREQ     WORD         FREQ     WORD__________________________________________________________________________2982     SOVIET UNION 0246     AKADEMII NAUK0634     SHOCK WAVES  0243     MAGNETIC FIELD0503     INTERNAL WAVES                 0216     CONTROL AVTOMATIKA0461     QUANTUM ELECTRON                 0207     RADIO WAVES0425     PHASE CONJUGATION                 0193     FREQUENCY STANDARDS0375     REMOTE SENSING                 0176     CATALYSIS KINETIKA0374     IMAGE PROCESSING                 0171     PARTICLE ACCELERATORSHIGH FREQUENCY TRIPLE WORDS FROM FASACFREQ    WORD                  FREQ                         WORD__________________________________________________________________________0371    EXPLOS SHOCK WAVES    0112                         STIMULATED BRILLOUIN0159    CHARGED PARTICLE ACCELERATORS                         SCATTERING0134    VYCHISLITEL NAYA TEKHNIKA                     0095                         ATOMIC ENERGY0127    SPATIAL LIGHT MODULATORS  INSTITUTE0127    IZVESTIYA AKADEMII NAUK                     0073                         OPTICAL PHASE CONJUGATION0115    IMAGE PATTERN RECOGNITION                     0071                         FUELS AND OILS                     0068                         SPACE RESEARCH INSTITUTE                     0064                         SYNTHETIC APERTURE RADAR                     0060                         VYCHISLITEL NOY MATEMATIKI                     0054                         SOVIET ASTRONOMY LETTERS__________________________________________________________________________ 
    
     
                                           TABLE 2__________________________________________________________________________FASAC TECHNICAL THEMES__________________________________________________________________________INTERNAL WAVE    SEA SURFACESHOCK WAVE       COMPOSITE MATERIALQUANTUM ELECTRON CROSS SECTIONPHASE CONJUGATION            INTEGRAL EQUATIONREMOTE SENSING   SOLID FUELIMAGE PROCESSING BOUNDARY LAYERPATTERN RECOGNITION            PLASMA PHYSICSOCEANIC PHYSICS  COMPUTER SOFTWARERADIO WAVE       LIQUID CRYSTALMAGNETIC FIELD   DATA PROCESSINGCOMPUTER SCIENCE NEUTRAL BEAMHYDROGEN MASER   DIGITAL COMPUTERREMOTE CONTROL   ELECTRIC FIELDFREQUENCY STANDARD            ELECTROMAGNETIC WAVESIGNAL PROCESSING            LOW OBSERVABLEARTIFICIAL INTELLIGENCE            PARALLEL PROCESSINGLIGHT MODULATOR  AUTOMATIC CONTROLSURFACE WAVE     ATOMIC ENERGYRADIO ENGINEERING            WAVE PROPAGATIONCONTROL SYSTEM   IONOSPHERIC MODIFICATIONCHARGED PARTICLE FRACTURE MECHANICSDIFFERENTIAL EQUATION            CHEMICAL PHYSICSOPTICAL PROCESSING            HIGH-POWER MICROWAVEPARTICLE ACCELERATOR            EXPLOS SHOCK WAVETHIN FILM        KINETICS AND CATALYSISPROGRAMMING LANGUAGE            EXPLOSION AND SHOCKSTRENGTH MATER   SPATIAL LIGHT MODULATORCOMPUTER VISION  CHARGED PARTICLE ACCELERATORELECTRON BEAM    ATMOS OCEANIC PHYSDATA BASE        MOLECULAR ELECTRONIC__________________________________________________________________________ 
    
     The user may directly interact with the process of the present invention to select the often repeated phrases of high user interest (PTAs). Or, at the start of the process provide criteria for selection of the PTAs to enable the process of the present invention to define and select the PTAs from sorted list content. Many applications of the invention to date have focused on identifying PTA in databases which describe sponsored research programs. In these cases, phrases of high user interest are words which have high technical content, e.g., REMOTE SENSING, SIGNAL PROCESSING, HIGH TEMPERATURE SUPERCONDUCTOR. Thus, the selection of PTAs in some database analyses performed by the invention have used, for example, the 60 most often repeated phrases of high user interest as PTA. More or less than 60 phrases per PTA can be used, and, as stated above this can be determined by user interaction or pre-processing user supplied criteria for system definition of PTA composition. 
     The invention next identifies all phrases related to each PTA as depicted in 22 of FIG. 1 and FIG. 3. As shown at 50 the user identifies a range R of interest of plus or minus a number of single words about each PTA for the purpose of extracting phrases from the full-text database which occur physically close to the PTA. The invention has been found to produce good results using a set range of 150 single words of the PTA. It should be understood that selection of the size and range about the PTA is at the discretion of the user and may depend on the characteristics of the database. Next, for each PTA count all the phrases within the range of the PTA as depicted at 52 in FIG. 3. Sort by frequency of occurrence all phrases found for each PTA as depicted at 54. See Table 3. 
     
                       TABLE 3______________________________________150(C.sub.j) REMOTE SENSING PTA - CLOSELYRELATED PHRASESC.sub.ij  C.sub.i______________________________________139    2764      DATA022    0036      THERMAL INFRARED120    0879      REMOTE056    0323      ICE079    2593      LITERATURE070    0522      SATELLITE041    0228      OCEANOGRAPHIC012    0020      ATMOSPHERIC CORRECTIONS065    2287      UNTIED109    1707      SPACE012    0024      AEROSOL OPTICAL012    0025      IMAGING SYSTEMS006    0007      MICROWAVE SENSORS062    2239      UNITED STATES074    1072      RADAR012    0037      VEGETATION______________________________________ CODE: C.sub.ij IS COOCCURRENCE FREQUENCY, OR NUMBER OF TIMES PHRASE APPEARS WITHIN +/- 50 WORDS OF PTA IN TOTAL TEXT; C.sub.i IS ABSOLUTE OCCURRENCE FREQUENCY OF PHRASE; C.sub.j IS ABSOLUTE OCCURRENCE FREQUENCY OF PTA. 
    
     Next, list each PTA and its related sorted list of phrases as at 56. See Table 3. 
     The invention next quantifies the strength of relationship between extracted phrases and each pervasive theme area (PTA) as depicted at 26 in FIG. 1 and 62 in FIG. 4. The user must first define, develop, and provide numerical indices and figures of merit for each of the extracted phrases. Examples of indices used in a preferred embodiment include the absolute frequency of occurrence of the PTA designated C j , the absolute frequency of occurrence of the extracted phrases, C i , and the extracted phrases within a set range of single words of the PTA designated C ij . 
     Examples of figures of merit used include the following ratios of the frequencies of occurrence described above: C ij  /C i , C ij  /C j , and (C ij   2)/(C i  *C j ) where  2 denotes squared and * denotes multiplication by. Other numerical indices and figures of merit may be used instead of or in addition to those described above. Table 3 shows the results of the quantification function of the invention. The user is thus in a position at 64 in FIG. 4 to select phrases that have a relationship to each PTA having determined relationships strengths based on quantifications obtained by applying the above numerical indices and figures of merit. 
     Pervasive theme areas (PTAs) which are closely related are next identified by the invention as depicted at 30 in FIG. 1 and in FIG. 5. The user is required to have defined and provided threshold values above some predetermined number of the figures of merit for selecting phrases of high user interest. In the presently embodied invention the selection of threshold values for the identification of closely related PTAS is at the discretion of the user. The system can, however, be implemented to select threshold values based on an automated analysis of the full-text database being operated upon. The user or system can then select phrases of high interest whose figures of merit are above the threshold. See Table 4. 
     
                       TABLE 4______________________________________150(C.sub.j) REMOTE SENSING PTA - CLOSELYRELATED PHRASES       I.sub.i  E.sub.ijC.sub.ijC.sub.i       C.sub.ij C.sub.ij  2/C.sub.i C.sub.j                        PTA MEMBER______________________________________022  0036   0.611    0.0359  THERMAL INFRARED056  0323   0.173    0.0259  ICE070  0522   0.134    0.0250  SATELLITE041  0228   0.180    0.0197  OCEANOGRAPHIC012  0020   0.600    0.0192  ATMOSPHERIC                        CORRECTIONS109  1707   0.064    0.0186  SPACE012  0024   0.500    0.0160  AEROSOL OPTICAL006  0007   0.857    0.0137  MICROWAVE SENSORS074  1072   0.069    0.0136  RADAR012  0037   0.324    0.0104  VEGETATION______________________________________ CODE: C.sub.ij IS COOCCURRENCE FREQUENCY, OR NUMBER OR TIMES PHRASE APPEARS WITHIN +/- 50 WORDS OF PTA IN TOTAL TEXT; C.sub.i IS ABSOLUTE OCCURRENCE FREQUENCY OF PHRASE; C.sub.j IS ABSOLUTE OCCURRENCE FREQUENCY OF PTA PHRASE; I.sub.i, THE INCLUSION INDEX BASED ON PHRASE, IS RATIO OF C.sub.ij TO C.sub.i ; AND E.sub.ij, THE EQUIVALENCE INDEX, IS PRODUCT OF INCLUSION INDEX BASED ON PHRASE I.sub.i (C.sub.ij /C.sub.i) AND INCLUSION INDEX BASED ON PTA I.sub.j (C.sub.ij /C.sub.j). 
    
     The commonality, i.e. the degree of similarity or close relatedness, of extracted phrases among the different PTA is next computed to identify phrases in common between each PTA&#39;s related phrases as at 74 in FIG. 5. In the preferred embodiment of the present invention commonality was defined as the numbers of phrases in common among PTA, and in particular the number of extracted phrases in common between all the PTAs. Other definitions of commonality may, of course, be formulated. 
     PTA which are related based upon the number of phrases in common are next identified as at 76 in FIG. 5. Specifically groups of PTA are generated such that each PTA in a given group has extracted phrases in common with at least one other PTA in the group. See Table 5. 
     
                                           TABLE 5__________________________________________________________________________PTA GROUP 9 (PARTIAL)RADIO ENGINEERING PTA           ELECTROMAG. WAVES PTA                           CROSS SECTION PTA__________________________________________________________________________QUANTUM ELECTRONICS           QUANTUM ELECTRONICSDIFFRACTION     DIFFRACTIONMICROWAVE       MICROWAVETHIN PLASMA LAYER           THIN PLASMA LAYERRADAR           RADAR           RADAR           SCATTERING      SCATTERING           PLASMA          PLASMASPECTROSCOPYATOMIC ENERGYMOLECULAR BEAMMESON FACTORY           IMPEDANCE           RESONANCE           DIELECTRIC CONSTANT                           COATED BODIES                           FLAME TUBE                           PLANE WAVE                           PULSE                           ANTENNA__________________________________________________________________________ 
    
     In a preferred embodiment each PTA in a group contains extracted phrases in common beyond some predetermined threshold value with at least one other PTA in the group. Other inclusion or membership criteria may be used. 
     Finally, as shown in FIG. 1, the relationships among related PTA and among PTA and related phrases are graphically displayed. In a preferred embodiment groups of related PTAs with each PTA in the group are listed. A generic title is used for each PTA group. See Table 6. 
     TABLE 6 
     GROUPS OF CLOSELY RELATED PHRASES 
     GROUP 1. IONOSPHERIC HEATING/MODIFICATION: *RADIO WAVE; *WAVE PROPAGATION; *QUANTUM ELECTRON; *IONOSPHERIC MODIFICATION; *PHASE CONJUGATION. 
     GROUP 2. IMAGE/OPTICAL PROCESSING: *PARALLEL PROCESSING; *PATTERN RECOGNITION; *IMAGE PROCESSING; *COMPUTER VISION; *DIGITAL COMPUTER; *ARTIFICIAL INTELLIGENCE; *DATA PROCESSING; *COMPUTER SCIENCE; *OPTICAL PROCESSING; *SPATIAL LIGHT MODULATOR; *SIGNAL PROCESSING; *LIQUID CRYSTAL; *LIGHT MODULATOR; *PROGRAMMING LANGUAGES; *INTEGRAL EQUATIONS. 
     GROUP 3. AIR-SEA INTERFACE: *SURFACE WAVE; *OCEANIC PHYSICS; *INTERNAL WAVE; *SEA SURFACE; *BOUNDARY LAYER; *ATMOS OCEANIC PHYS; *REMOTE SENSING. 
     GROUP 4. LOW OBSERVABLE: *LOW OBSERVABLE; *THIN FILM. 
     GROUP 5. EXPLOSIVE COMBUSTION: *KINETICS AND CATALYSIS; *SOLID FUEL; *EXPLOSION AND SHOCK; *SHOCK WAVE; *CHEMICAL PHYSICS; *EXPLOS SHOCK WAVE; *STRENGTH MATER; *FRACTURE MECHANICS; *COMPOSITE MATERIALS. 
     GROUP 6. PARTICLE BEAMS: *NEUTRAL BEAM; *PARTICLE ACCELERATOR; *ATOMIC ENERGY; *PLASMA PHYSICS; *ELECTRON BEAM; *CHARGED PARTICLE ACCELERATOR; *CHARGED PARTICLE. 
     GROUP 7. AUTOMATIC/REMOTE CONTROL: *AUTOMATIC CONTROL; *REMOTE CONTROL 
     GROUP 8. FREQUENCY STANDARDS: *FREQUENCY STANDARD; *HYDROGEN MASER 
     GROUP 9. RADAR CROSS SECTION: *CROSS SECTION; *ELECTROMAGNETIC WAVE; *RADIO ENGINEERING. 
     Other relational display mechanisms such as Venn-type diagrams may be used in the alternative. Also in a preferred embodiment the graphical display of the relationship between each PTA and its extracted phrases present the extracted phrases assigned to quadrants based on the magnitudes of the two figures of merit (C ij  /C i ) and (C ij  /C j ). Each quadrant depicts a specific type of relationship between the PTA and the extracted phrases in that quadrant. See Table 7. 
     
                                           TABLE 7__________________________________________________________________________ATMOS OCEANIC PHYS PTA - HIGH TECHNICAL CONTENT PHRASESHIGH I.sub.j  LOW I.sub.i      LOW I.sub.j  HIGH I.sub.i(LEFT QUADRANT)                (RIGHT QUADRANT)__________________________________________________________________________HIGH I.sub.j  HIGH I.sub.i(UPPER QUADRANT)SEA                            RADIOACOUSTIC SOUNDINGINTERNAL WAVE                  ACOUSTIC SOUNDINGACOUSTIC                       THEORY OF WINDSCATTERING                     MODELING OF SURFACERADAR                          WIND WAVES ATMOSSEA SURFACE                    INTRASOUND AND INTERNALATMOSPHERE                     THEORY OF WAVELOW I.sub.j  LOW I.sub.i(BOTTOM QUADRANT)WIND WAVES    SHEAR FLOW       PROCESSING OF RADARSOUND PROPAGATION         TURBULENT        WAVE PROPAGATIONOCEAN SURFACE SATELLITE        WIND VELOCITYGRAVITY WAVES INTERNAL GRAVITY WAVES                          POINT SOURCESTRATIFIED FLUID         SOUND WAVES__________________________________________________________________________ 
    
     Alternative means for displaying relationships would include, but not be limited to, two or three-dimensional plots of the relationship between each PTA and its extracted phrases using figures of merit of the extracted phrases as plotting variables. 
     The invention as described is easily mechanized using a variety of existing hardware elements such as optical scanners computers and displays. A program used by the system of the present invention to implement the full-text database process of the present invention appears in the Appendix to this specification. The code was written in Think Pascal™ on the Macintosh. The Macintosh was chosen for the user interface, speed, and memory management capabilities. An application on a Macintosh can access all of the free memory management capabilities of the machine without any limitations like the 640K &#34;barrier&#34; on IBM PC&#39;s. Think Pascal™ was chosen as the programming language because of its efficient compiler and powerful debugger. 
     The variable and procedure names indicate what they are used for. There are a few subtleties that should be understood. First, whenever a list is mentioned in the block diagrams of the process it is implemented using a balanced binary tree. The main advantages of a binary tree are the fast search time and dynamic memory allocation. Each time the binary tree is changed it is checked to see that all branches are close to the same length. By doing this it should be understood that to search for a given item in a tree containing n items will take at most log n  (n) comparisons. This is much better than n/2 for a sort list. The fact that the tree is made up of individual data structures linked together by pointers means that the user doesn&#39;t have to know how big the tree will get. The user simply has to know if there is enough memory to add the nest item to the tree. Second, there is no double counting of words. 
     It should be understood that there are numerous variations and modifications of the invention that will be readily apparent to those skilled in the art in light of the above teachings. It is, therefore, to be understood that, within the scope of the appended claims, the invention may be practiced other than as specifically described. ##SPC1##

Technology Classification (CPC): 8