Interactive connotative dictionary system

[0002] This invention is related to commonly-assigned U.S. patent application Ser. No. 09/372,377 filed on the same day, of W. Chase for “System for Identifying Connotative Meaning;” commonly-assigned U.S. patent application Ser. No. 09/372,549 filed on the same day, of W. Chase for “System for Quantifying Intensity of Connotative Meaning;” commonly-assigned U.S. patent application Ser. No. 09/372,244 filed on the same day, of W. Chase for “Interactive Connotative Thesaurus System;” commonly-assigned U.S. patent application Ser. No. 09/372,737 filed on the same day, of W. Chase for “System for Connotative Analysis of Discourse.” The content of all such applications are incorporated herein by reference and made a part hereof.

[0003] This invention relates to a computerized interactive language reference tool, and more particularly to a system for accessing the connotative meanings of words and phrases.

[0004] Language is an abstract, rule-governed system of arbitrary symbols that can be combined in countless ways to communicate information. All languages include a system of phonology (i.e., set of sounds), semantics (i.e., word, phrase and sentence meanings), morphology (i.e., rules for combining smallest meaningful units to form or alter words), syntax (i.e., ways in which words are organized into phrases and sentences) and pragmatics (i.e., rules governing a conversation and social use of language).

[0005] The use of language enables humankind to develop advanced cognitive abilities. Cognitive development relates to the changes in a person's memory, thinking, use of language and other mental skills as they develop from infants to adults. Humans develop a certain degree of cognitive competence. In addition to such cognitive competence, humans also display and experience feelings, emotions and moods. In particular, our emotional state or the emotional state we desire to elicit can influence our choice of words. Every human language enables people to communicate both intellectually and emotionally because words and phrases convey both cognitive and affective meaning. ‘Affective’ means to be influenced by or result from emotions.

[0006] Linguistics is the scientific study of language. Semantics is the branch of linguistics that deals with the study of the relationship between words or phrases and their meanings. Of particular significance here are the contrasting linguistic terms, ID denotation and connotation. ‘Denotation’ is a particular meaning of a symbol. ‘Connotation’ is an idea or meaning suggested by or associated with a word or phrase. Thus, ‘denote’ describes the relation between a word or phrase and the thing it conventionally names, whereas ‘connote’ describes the relation between the word or phrase and the images or associations it evokes. As used herein a denotation is an objective, cognitive meaning which refers to the direct relationship between a term and the object, idea or action it designates. As used herein, a connotation is a subjective, affective meaning which refers to the emotive and associative aspect of a term.

[0007] The denotative meanings of words have been systematically codified into definitions and collected together to form dictionaries, thesauruses and related denotative language references. However, the codification of connotative meanings has not been achieved. Consider, for example, a dictionary which provides the following denotative meaning for the word ‘pub’: “a building providing alcoholic drinks for consumption on the premises” (Oxford Dictionary). However, the word ‘pub’ simultaneously conveys a host of emotional connotations, such as merriment, pleasure, cheerfulness, perhaps some sadness, and so on. Similarly, words such as ‘summer’, ‘love’, and ‘melody’ have a variety of positive emotional connotative associations for most people, while words such as ‘cancer’, ‘rape’, and ‘homeless’ have negative emotional connotations for most people. In all cases, the associated connotations are not systematically accessible using any known language reference resource or tool.

[0008] The reason for the absence of codification of connotative meaning is that, while words readily evoke emotional connotations, the converse is not true: emotional connotations are not easily codified using words. Unlike denotative meaning, affective meaning does not naturally lend itself to systematic word-symbol description. Emotions are felt, not thought, so the relationship between a word and its associated connotative content, while real, is not codifiable using the relatively straightforward methods employed by lexicographers in fashioning denotative definitions. Accordingly, there is a need for a connotation language reference tool and a system for codifying the connotative content of such a tool.

[0009] Not only is it desirable to identify connotative meaning, it also is desirable to quantify the intensity associated with a connotation. Some words or phrases evoke stronger responses than others. Some words or phrases are more activity oriented. In the 1950s, Charles Osgood, an American psychologist developed a method of constructing bipolar scales based on semantic opposites, such as “good-bad”, “soft-hard”, “fast-slow,” “clean-dirty,” “valuable-worthless,” “fair-unfair,” and so on. Osgood called these scales “semantic differential” scales because they differentiated attitudinal intensity based on a person's subjective understanding of the connotative meanings of words. Osgood et al. explored large amounts of data provided by students who evaluated numerous words and phrases on numerous semantic differential scales. The outcome of Osgood's investigations was a description of the existence of “semantic space,” three measurable underlying attitudinal dimensions that proved in subsequent research to be robustly identifiable across other languages and cultures. Osgood named these dimensions Evaluation, Power, and Activity (EPA). Experimentation by many investigators around the world confirmed the reality of semantic space and its cross-cultural validity (Japan, Scandinavia, Germany, Ireland etc.).

[0010] The semantic differential is a method for measuring the meaning of an object to an individual. It may also be thought of as a series of attitude scales. A subject is asked to rate a given concept (for example, ‘Irish’, ‘Republican’, ‘wife’, ‘me as I am’) on a series of seven-point bipolar rating scales. Any concept—whether it is a political issue, a person, an institution, a work of art—can be rated. Subgroups of the scales can be summed up to yield scores that are interpreted as indicating the individual's position on three underlying dimensions of attitude toward the object being rated. These dimensions have been identified by using factor-analytic procedures in examining the responses of many individuals concerning many concepts or objects. It has been found that three subgroups measure the following three dimensions of attitude: (1) the individual's evaluation of the object or concept being rated, corresponding to the favorable-unfavorable dimension in more traditional attitude scales; (2) the individual's perception of the potency or power of the object or concept; and (3) the individual's perception of the activity of the object or concept. (See Kidder, L. H., “Research Methods in Social Relations;” 1981).

[0011] The problem with the semantic differential technique is that it does not distinguish beyond a single evaluative continuum, with positive attitude at one end of the scale through negative attitude at the other end. That is, it does not actually identify any individual emotions. Moreover, although several short “semantic differential dictionaries” have been developed (known in the literature as semantic “atlases” because they are analogous to “maps” of semantic space), consisting of 500 to 1,500 words with EPA scores for each word, the technique of semantic differential is not associated with any system or method for codifying the words of any given language, even on a single affective variable. According there is a need for a system for codifying the connotative meaning of words.

[0012] According to the invention, a computerized interactive language reference tool is provided which associates one or more connotative meanings with a denotative context of a given word or phrase (i.e., referred to herein as a ‘term’).

[0013] According to one aspect of the invention, a data base is generated which stores the denotative context and connotative meanings of a plurality of terms. Connotative meaning, along with the intensity of such meaning, are identified using a statistical model of sampled responses from select panels of evaluators. In addition, areas of human interest also are associated with a given term and its denotative meaning. Further, scaled ratings of the power, activity and abstract/concrete qualities of the term also are maintained.

[0014] According to another aspect of the invention, the data base content is updated over time to accommodate changing and new connotations.

[0015] According to another aspect of the invention, connotative meanings for any given term are selected from a plurality of predefined categories. In one embodiment described below for the English language there are 8 categories. In the preferred embodiment there are four categories of positive emotions (e.g., affection/friendliness, enjoyment/elation, amusement/excitement and contentment/gratitude) and four categories of negative emotions (e.g., sadness/grief, anger/loathing, fear/uneasiness, and humiliation/shame). Within each category there are a predefined list of emotional descriptors. Thus, there are a plurality of categories of emotional descriptors. A term may have a connotative meaning in any or all of the emotional categories. Some terms may not have any connotative meaning. In some embodiments only one emotional descriptor is permitted to be assigned for a given emotional category for a given term. Thus, for an eight category embodiment, any term can have 0 to 8 emotional descriptors the emotional descriptors being from different emotional categories. In other embodiments a primary and a secondary emotional descriptor may be assigned for any given term. For such an embodiment, which is based on 8 emotional categories, any term can have 0-16 emotional descriptors—the emotional descriptors being in pairs, with the two emotional descriptors in a given pair being for a given emotional category. Different pairs include emotional descriptors for different emotional categories.

[0016] According to another aspect of the invention, for each emotional descriptor associated with a given term, there is an intensity rating of how strongly or intensely the term tends to promote or relate to the emotional descriptor.

[0017] According to another aspect of the invention, a user interface for the connotative language reference operates in ‘look up’ mode and ‘look for’ mode. In ‘look up’ mode a user enters a term and the connotative meanings are displayed. In ‘look for’ mode a user selects filters to narrow down the data base to a list of terms meeting the filtration criteria.

[0018] According to another aspect of the invention, a user interface enables a user to view a selected term and its connotations. In one embodiment the emotional categories are grouped into positive emotions and negative emotions. The connotative language reference displays the emotional descriptors, if any, associated with the selected term relating to the positive emotions along with their associated intensity level. Similarly, the connotative language reference displays the emotional descriptors, if any, associated with the selected term relating to the negative emotions along with their associated intensity level. In addition, the power rating, activity rating and abstract vs concrete ratings are displayed along with their corresponding scales.

[0019] According to another aspect of the invention, there are a plurality of filters for identifying terms from the database to be displayed. There are general filters, such as denotative filters (number of syllables, parts of speech), special diction filters (e.g., slang, euphemistic, interrogative) and non-emotional connotation filters (power, activity, abstract/concrete). There also are human interest filters. There are several categories of human interest filters, such as personal identity filters (e.g., male, female, organization), spiritual identity filters (e.g., Hinduism, Christianity, Myth/legend), physical identity filters (e.g., body, health), perception filters (e.g., taste, place event) and hon-human life filters (e.g., animal, plant). In ‘look for’ mode a user enters selections for one or more filters (e.g, nouns, at least 2 syllables, euphemistic, have a power rating of 5 to 8, relating to myth or legend, and relating to health). The terms meeting the criteria of the filter selections are displayed, along with the associated denotative data. A user then can select one of the terms and switch to ‘look up’ mode to view the specific connotations associated with such term.

[0020] According to another aspect of the invention, the connotative language reference, including the data base, user interface and processing filters, are stored on a computer readable storage media.

[0021] According to one advantage of the invention a large amount of connotative and denotative information is accessible to an end user in a manageable format. According to another advantage of the invention, the data is updated over time for changing connotations. In particular, for a distributed computing model of implementation, such as a global computer network, the content may be updated continually or at varying intervals.

[0022] These and other aspects and advantages of the invention will be better understood by reference to the following detailed description taken in conjunction with the accompanying drawings.

[0023] FIG. 1 is a block diagram of a host computer system;

[0024] FIG. 2 is a diagram of a connotative dictionary according to an embodiment of this invention;

[0025] FIG. 3 is a display sample of a user interface in ‘look up’ mode according to an embodiment of this invention;

[0026] FIG. 4 is another display sample of the user interface in ‘look up’ mode according to an embodiment of this invention;

[0027] FIG. 5 is a display sample of a user interface in ‘look for’ mode according to an embodiment of this invention;

[0028] FIG. 6 is another display sample of a user interface in ‘look for’ mode according to an embodiment of this invention;

[0029] FIG. 7 is yet another display sample of a user interface in ‘look for’ mode according to an embodiment of this invention; and

[0030] FIG. 8 is a diagram of a system for identifying connotative meanings according to an embodiment of this invention.

[0031] A system and method are described for identifying, codifying, storing, and retrieving the connotative meaning inherent in the words and phrases of any language. Throughout this description, a preferred embodiment and examples given should be considered as exemplars rather than limitations on the method and system of the present invention.

[0032] Many of the functions of the present inventions preferably are performed by or with the assistance of a programmed digital computer of the type which is well known in the art, an example of which is shown in FIG. 1. A computer system 20 has a display 22, a key entry device 24, a pointing/clicking device 26, a processor 28, and random access memory (RAM) 30. In addition there commonly is a communication or network interface 34 (e.g., modem; ethernet adapter), a non-volatile storage device such as a hard disk drive 32 and a transportable storage media drive 36 which reads transportable storage media 38. Other miscellaneous storage devices 40, such as a floppy disk drive, CD-ROM drive, zip drive, bernoulli drive or other magnetic, optical or other storage media, may be included. The various components interface and exchange data and commands through one or more busses 42. The computer system receives information by entry through the key entry device 24, pointing/clicking device 26, the network interface 34 or another input device or input port. The computer system 20 may be any of the types well known in the art, such as a mainframe computer, minicomputer, or microcomputer and may serve as a network server computer 12, remote network 16 computer or a client computer 14. The computer system 20 may even be configured as a workstation, personal computer, network server, or a reduced-feature network terminal device.

[0033] The connotative language reference serves as an interactive electronic connotative dictionary, thesaurus or other language reference. For a connotative dictionary embodiment, the connotative dictionary is distinct from a ‘classic’ dictionary in that it lists words with their connotative meanings. For a connotative thesaurus embodiment, the connotative thesaurus is distinct from a ‘classic’ thesaurus in that words are linked when they have the same or a similar emotional or related connotative meaning, but typically have a different denotative meaning. These linked words are referred to herein as “connonyms,” a coined word meaning connotative synonyms. The data bases forming a computer version of the connotative language references may be formed using custom-designed database software or database software commercially available from manufacturers such as Inprise, Oracle, Microsoft or another vendor of data base software services.

[0034] Following are sections which describe a connotative dictionary, methods for identifying connotative meanings, methods for quantifying intensity of connotative meanings, methods for maintaining data integrity, and methods for identifying human interest areas.

[0035] Connotative Dictionary

[0036] Referring to FIG. 2, a connotative language reference system 10 is formed by a data base 12, a user interface 14 and various filtering and retrieval processes 16. Preferably such data base 12, interface 14 and processes 16 are implemented in electronic format as one or more software programs executed on a host computer system or over a host computer network. For example, the reference system 10 may be stored on an optical disc (e.g., CD-ROM) or other storage media and installed onto the host computer system or network. Specifically, the data base 12, user interface software 14 and filtering and retrieval processes 16 may be loaded and installed locally onto the host computer system or network. In some implementations the software embodying the user interface 14 and processes 16 are installed to be resident on the host computer system or network, while the data base 12 is stored and accessed from a removable storage media, such as an optical disk. In other embodiments the data base 12 is centrally located among one or more network server computers, while the user interface software 14 and processes 16 are stored and executed from either a local end user computer system or remotely at the server computer. The implementations may vary from local copies for a given end user's access to one or more copies stored on a private network or even a global computer network which users log into to access and use the dictionary.

[0037] Data Base 12:

[0038] In a preferred embodiment of the data base 12, the data base 12 includes a set of denotative fields 44, a set of connotative fields 46, and a set of human interest fields 48. The denotative fields and human interest fields are not necessary, but provide additional resources for the user. In some embodiments the data base 12 includes only the set of connotative fields 46.

[0039] The set of denotative fields 44 includes at least two fields, and preferably three fields. In one implementation the denotative data is obtained from one or more electronic or print-based dictionaries in any language. Database records are created for each word or phrase of the language, which may be the English language or any other language. In some embodiments the connotative language reference system 10 is specialized for a given subject (e.g., medical/health contexts; science). In other embodiments a general language reference is implemented for a given language. Table 1 below lists the desired fields included among the denotative fields:

[0040] For each record in the data base 12, one denotative field is assigned to the word or phrase. A second denotative field is assigned to the denotative context (dictionary meaning) of the word or phrase. A third denotative field is assigned to the part of speech. Preferably, each context of each word is assigned a separate database record. Thus, if the dictionary definition of a single word has two meanings among a total of five contexts, then there are five records, one for each context. There may be multiple contexts for a given dictionary meaning when, for example, there are different parts of speech for the word/meaning.

[0041] Assigning an additional field to track the meanings that subsume the contexts is not necessary to practice the invention, but such a field may be assigned in an alternative embodiment. The total number of records required is equal to the sum of all of the denotative contexts of all of the words in the dictionary or other denotative data source or sources.

[0042] In a preferred embodiment the set of connotative fields 46 includes a block of one or more fields corresponding to each one of a plurality of emotional categories. In a best mode embodiment eight emotional categories have been identified, although the number of categories may vary to be more or less than eight. The number of fields comprising each block may vary. In one embodiment there is one field in each block that corresponds to a primary connotative emotional descriptor for the corresponding term. In some embodiments there is a field for storing a secondary connotative emotional descriptor for the corresponding term. Note that the primary and or secondary emotional descriptor can correspond to a designation of no connotative meaning stored in this emotional category for the given term.

[0043] In addition to the fields for the primary and optional secondary emotional descriptors, there also are fields associated with each block relating to the method for identifying connotative meaning. Although the method for defining connotative meaning may vary, in one embodiment the data is collected from multiple sources. In a given implementation the sources are judges or panels of judges. In some embodiments there is a field in each block for each judge's selection of the term's connotative meaning. In an example, where 24 connotative judges are used, each of the eight blocks of fields 46 includes 24 individual fields used in deriving the primary and/or secondary emotional descriptor for the term—a respective field to hold each judge's emotional descriptor data for each term in each context. Several additional fields are reserved to hold calculated data based on the connotative judges' emotional descriptor data. Fewer or more judges may be used, adjusting the number of fields within each block accordingly.

[0044] Table 2 lists eight emotional categories corresponding to the eight blocks of fields 46 for a best mode embodiment. In the exemplary embodiment the categories are classified into categories for positive emotions and categories for negative emotions. The general emotional classification “Positive Emotions” subsumes four emotional categories, and the general emotional classification “Negative Emotions” subsumes the other four emotional categories, as practiced in a preferred embodiment of the invention. Each emotional category subsumes a list of specific emotional descriptors (e.g., in this embodiment there are 12 to 37 emotional descriptors per category), each of which is associated with a two-digit identifying code number. The specific code number may vary. Further, the manner in identifying the distinct descriptors also may vary. The total number of emotional descriptors in this example is 164. In various embodiments one or more emotional descriptors may be removed from this list, entire emotional categories may be removed from the list, or one or more emotional categories and descriptors may be added to this list. In a preferred embodiment it is the code numbers which are stored in the records of the data base 12. In other embodiments the entire emotional descriptor term may be stored individually for each connotative data field of a record.

[0045] The connotative emotional descriptors that appear in Table 2 are English language emotional descriptors for one embodiment of a general connotative dictionary. The specific words that make up the emotional connotative descriptors may vary. Of course such descriptors will vary from language to language.

[0046] In a preferred embodiment each record also includes a set of human interest fields 48 which relate the corresponding term and its denotative context to a human interest category. The purpose of incorporating the human interest fields is to permit the end user to easily retrieve special connotative content from the database by first selecting one or more human interest filters before querying the database. The human interest fields 48 employed in an exemplary embodiment of the invention are listed in Table 3. There are nine groupings of the human interest categories in such embodiment. Each record includes a set of nine human interest fields—one field for each human interest category. Each field stores a human interest descriptor word. Of course, the field also may store a designation that there is no human interest context for the term as used in the associated denotative context of a given record. The human interest categories and descriptors may vary from embodiment to embodiment.

[0047] In a preferred embodiment of the invention, the assignment of the fields and records as described above effectively links each traditional dictionary definition of each term in each context with more than 200 connotative and human interest variables. The data base may be organized into one or more tables, files or other units of organizing data. Regardless of the number of tables or files, there are entries from each table or file which correspond to a term (i.e., a word or phrase among the denotative fields). The data associated with any given term is referred to herein and in the claims as a ‘record’, whether or not coming from a single table or file, or from multiple tables or files. Each record includes a field which identifies a term and another field which identifies the denotative context for the term. The record also includes a field which identifies the connotative meaning. Thus, each record includes at least three fields allocated among 1 to 3 tables, files or other units of organization of the data base. In a preferred embodiment there are a plurality of connotative meaning fields, at least one for each of a plurality of emotional categories. The connotative meaning fields identify an emotional descriptor for a corresponding emotional category. Such identification may be an identification of a specific emotional descriptor within the corresponding category or an indication that there is no emotional descriptor for such category. An indication of no connotative meaning is still considered to be connotative meaning information.

[0048] In varying embodiments additional fields are included for any one or more of the following types of information: parts of speech, intensity of connotative meaning, power rating, activity-rating, abstract/concrete rating, human interest areas. When an intensity field is included the intensity is an intensity of a connotative meaning and is associated with a corresponding emotional category and the identified emotional descriptor for such category.

[0049] User Interface 14:

[0050] In a preferred embodiment a graphical user interface is implemented, which provides an end user with the capability of retrieving data from the data base 12. Although there are many ways in which a user interface may be implemented, in one embodiment a system with menus and windows is used.

[0051] In one embodiment the user interface 14 is operated in different modes—‘look up’ mode or ‘look for’ mode. During ‘look up’ mode a user enters a word or phrase and data is retrieved from the database 12 and displayed to the user. In ‘look for’ mode a user enters parameters into various filtering processes which are implemented to retrieve words which meet the parameters.

[0052] FIG. 3 shows an embodiment of the graphical user interface 14 in ‘look up’ mode. The mode is indicated on a button 50 in the upper right corner of the interface window 52. When in ‘look up’ mode, the user may type a word or phrase into a ‘look up’ box 54 in the upper left corner of the interface window 52. The connotative dictionary responds to the user's typed input by retrieving denotative information from the database 12 relating to the word or phrase that has been typed into the ‘look up’ box 54. This denotative information is displayed alphabetically in an area 55 (e.g., column) on the left-hand side of the user interface, and is formatted in much the same manner as the same denotative information is displayed and formatted in any conventional electronic dictionary.

[0053] Simultaneously, the dictionary 10 retrieves from the database 12 and displays on display 22 a range of connotative information relating to the same word or phrase that the user has typed in the ‘look up’ box 54. This connotative information is displayed in an area 57 on the right-hand side of the user interface window 52.

[0054] Although specific formats and locations are being described the specific format and location of information within the window 52 may vary. In one embodiment the connotative information is displayed in a color-coded graphical format, including horizontal bars. Preferably, the relative lengths of the horizontal bars represent data corresponding to connotative intensity (strength or weakness). For the power, activity and abstract/concrete information, the relative length represents a rating along a scale. In one example, the colors designate the following:

[0055] A set of two tabs 61, 63, labeled “Level 1” and “Level 2,” indicate the level of emotional classification and categorization of the connotative data represented in the graphical display area 57, 59 associated with the selected tab. In one embodiment these levels of classification and categorization are defined as follows:

[0056] Level 1: Four level 1 categories of “Positive Emotions” and four level 1 categories of “Negative Emotions” for the embodiment illustrated in FIG. 3.

[0057] Level 2: Each level 1 emotional category subsumes a list of 12 to 37 specific emotional descriptors (e.g., level 2 information), as listed in Table 2. Only the level 2 emotional descriptors associated with the word displayed in the ‘Look up’ box are displayed.

[0058] FIG. 4 shows the user interface 14 in ‘look up’ mode with the level 2 data being displayed in window area 59. Such window area 59 overlays area 57 when tab 63 is selected. In one embodiment the window area 59 includes a plurality of display placeholder variables 65. There is a placeholder variable for each one of the emotional categories. When a record is displayed the emotional descriptor for the corresponding category for the given record is displayed (when there is an emotional descriptor for such category for such record). Thus, rather than see a name of an emotional category in window area 59, the emotional descriptor corresponding to that category is viewed. Note that in the example illustrated, there is only one emotional descriptor (i.e., ‘excitement’) in FIG. 4 for the positive emotion categories (of FIG. 3). Accordingly, there was no connotation for the other three positive emotional categories. Also shown is a horizontal bar. The length of the bar corresponds to the intensity of such emotional descriptor for the connotation to the term looked up. A scale 67 for the intensity is included in the window area 57.

[0059] FIGS. 5-7 show the user interface 14 in ‘look for’ mode. The user enters ‘look for’ mode by toggling the ‘Look up/Look for’ toggle button 50. In a preferred embodiment of the invention, switching to “Look for” mode changes the window 52 format to display a set of three tabs 66, 68, 70 and corresponding to overlaying window areas 72, 74, 76. These tabs 66, 68, 70 and window areas 72, 74, 76 replace the tabs 61, 63 and window areas 57, 59 of ‘look up’ mode.

[0060] In ‘look for’ mode the user may retrieve connotative content from the database 12. First, the user selects criteria from one or more human interest filters or the non-emotional information (power, activity and abstract/concrete). Then the user initiates a search of the data base 12 for records matching the criteria. The human interest fields 48 employed in a preferred embodiment of the invention are listed in Table 3. These fields are displayed among the window areas 72, 74,76 and accessed by pressing on the corresponding tab 66, 68, or 70. The number of tabs 66, 68, 70 and the allocation of human interest fields 48 to the window areas 72, 74, 76 may vary.

[0061] In one embodiment the human interest fields associated with tab 66 are the special diction fields, the argot/vernacular fields and the non-emotional connotation fields of Table 3. In addition there are denotative filters 80 included in window area 72 for defining selection criteria. The user can search specific definitions, limit the words and phrases or parts of speech, limit the number of syllables or the accented syllable using the denotative filters 80. The user can select among slang, coarse, derogatory and other types of diction and vernacular under the heading of special diction filters 82. The non-emotional connotation filters 84 relate to scaled values based on a power, activity or abstract/concrete.

[0062] Referring to FIG. 6, window area 74, which is selected by pressing on tab 68, includes personal identity filters 86, spiritual identity filters 88 and physical identity filters 90. In the example illustrated, the user has specified a filtered search of the database for a random selection of famous or notorious female persons. The language reference system 10 retrieves the requested information and displays it in the window 55 on the left-hand side of the user interface, once the user finalizes the choices by clicking on the button labeled ‘OK.’

[0063] The user may select several human interest filters for a single search, in order to retrieve very particular customized lists of words and phrases. In a preferred embodiment of the invention, when the user has retrieved a customized list, the user may then switch back to “Look up” mode and retrieve all of the connotative information associated with any of the words and phrases in the customized list.

[0064] The human interest fields associated with the third tab in “Look for” mode are displayed in FIG. 7. In the example illustrated, the user has specified a filtered search of the database 12 for a random selection of noted places. The apparatus retrieves the requested information and displays it in the window on the left-hand side of the interface.

[0065] In a best mode embodiment the data base is stored on a computer readable storage media, such as embedded memory, RAM, ROM, a hard disk, an optical disk, a floppy disk, magneto-optical disk, electro-optical disk, or another known or to be implemented transportable or non-transportable computer readable storage media. The user interface, processing filters and other executable instruction code for maintaining and accessing the data base also is stored on the same or another computer readable storage media of the types listed above.

[0066] Under various computing models, the connotative language reference is installed at an end user computer or accessed remotely from an end user computer. For resident computing models, the executable instructions may be loaded onto the computer and the data base accessed from a transportable storage media. Alternatively the data base also may be installed locally. One skilled in the art will appreciate the many computing configurations that may be implemented. For non-resident computing models, the data base may be stored at a server computer on a public or private, local or wide area network, or even on a global computer network. The executable instructions may be run either at the end user computer or at a server computer with the data being displayed at the end user's display device.

[0067] Identifying Connotative Meaning

[0068] According to a preferred embodiment of the invention the connotative meanings associated with the terms found in the connotative language reference system are derived by subjective responses from a plurality of evaluators. In a best mode embodiment the evaluators are a panel of persons having objective credentials or accepted expertise in connotative analysis. However, in some embodiments the evaluators may be selected at random. Such persons are referred to herein as connotative judges. In a preferred embodiment, the Internet is used as a recruitment medium to recruit 100 to 200 individuals who are not known to each other to act as independent connotative judges. In one embodiment, the connotative judges are screened for the following characteristics listed below in Table 4:

[0069] While the above qualifications are used in one embodiment, the invention may be practiced using any number of judges having any qualifications of one's choosing. For example, connotative judges may be only women, or only men, or only individuals of a defined age or ethnic group, or only people who reside in a certain geographical location. The nature and quality of data captured will of course vary with the demographic profile of connotative judges, as well as with the number of judges used when practicing the invention, their geographical locations, and the linguistic qualifications of the judges.

[0070] The connotative judges evaluate the meaning of given words and phrases for connotative content using a questionnaire. The questionnaires preferably are distributed as database software files, although they may also be distributed in paper document form. The responses of the connotative judges are processed using either custom-designed database software or database software commercially available from manufacturers such as Inprise, Oracle, and Microsoft. As the data are analyzed, a database of connotative meaning is constructed, which is linked with each context of each word in the connotative dictionary. Each questionnaire is, in effect, a small database table containing five data fields, as summarized in Table 5.

[0071] Field 4 is used for identifying connotational meaning. Field 5 is used for identifying the intensity of the connotation. The use of Field 5 is described below in the section, Quantifying Intensity of Connotative Meanings.

[0072] In one embodiment, a distributed computing model is employed, in which the connotative judges use their own computers in their own homes or offices to receive questionnaire tables over the Internet (via e-mail or from a World Wide Web site) that are extracted from the main database 12. The connotative judges complete their work on the questionnaire tables, and then return the data tables over the Internet.

[0073] Referring to FIG. 8, a pool 80 of connotative judges are recruited to evaluate records of the connotative data base 12 for connotative associations of corresponding words and phrases. A sample of judges from the pool 80 forms a panel 82 used to evaluate a set of records. The same or different panels are formed to evaluate other sets of records. The number of records in a set may vary. For purposes of illustration a panel of 24 judges is described which evaluates a set of 500 records. In a preferred embodiment, each judge typically receives a questionnaire table 84 covering approximately 500 records, each record consisting of the four fields identified in Table 4. The questionnaire laso includes instructions for selecting a code number to fill in the blank Field 4 for each record. Each connotative judge is also supplied with one or more of the eight category lists of code-numbered emotional descriptors identified in Table 2. In a preferred embodiment for a given questionnaire each judge is supplied with only one of the eight category lists of emotional descriptors found in Table 2. Thus, in a given questionnaire a judge evaluates the terms for connotative meaning in only a specific emotional category. To complete the questionnaire table, the connotative judge inputs one of three codes or code types for each record in Field 4, as listed in Table 6.

[0074] In a preferred method of practicing the invention, only one judgment is required for each record in a questionnaire table. However, in other embodiments more than one judgment may be allowed or required, if, for example, one wishes to capture the connotative judge's first choice and also the connotative judge's second choice of emotional descriptor. To effect such data capture, the number of connotative fields 46 (see FIG. 2) would need to be expanded accordingly, and the questionnaire table structure modified to include additional data capture fields.

[0075] The connotative judge repeats this procedure for all 500 records in the questionnaire, then returns the completed questionnaire table 84 via the Internet. Thereafter, the judge may receive another questionnaire table 86, or 88 to evaluate. The next questionnaire table received by the connotative judge may contain exactly the same set of records that was just evaluated, but accompanied by a different category list of emotional descriptors to be used for coding. Alternatively, the next questionnaire table may contain a completely different selection of records. The exchange of questionnaire tables continues iteratively for the duration of connotative data collection.

[0076] In a preferred embodiment of the invention, each block of 500 records is evaluated in this manner eight times (corresponding to the eight category lists of connotative descriptors listed in Table 2), each time by 24 different connotative judges selected at random from the pool of 100 to 200 available connotative judges, using a judge-selection technique that stratifies sampling to ensure equal representation according to the guidelines summarized in Table 4. Note that the number of judges selected, the size of the pool and the number of records processed in a given questionnaire may vary.

[0077] Typically a plurality of panels 82, 83 are formed to evaluate the database 12 records for connotative associations. Different panels 82, 83 receive either the same or different questionnaires 84-89. For the exemplary embodiment where 24 judges evaluate each of 500 records in a given questionnaire, the same 24 judges may or may not evaluate all eight categories of emotional connotations for such 500 records.

[0078] Quantifying Intensity of Connotative Meanings

[0079] As previously described each judge receives a questionnaire. In one embodiment the questionnaire is in table format. Each record in the table has multiple fields as listed above in Table 5. Fields 1, 2 and 3 are already complete and correspond to the term, a denotative context for such term, and a part of speech. Field 4 is filled in as described above to identify connotative meaning for the term. Field 5 is to be filled in to record the intensity associated with the connotative meaning provided in Field 4. When a judge indicates that there is no connotative meaning for the term or that the judge does not know the term or the specific denotative context, then there is no need to quantify an intensity in Field 5. Where a judge is permitted to provide two connotative meanings (e.g., a primary and a secondary connotative meaning) then additional fields are included for each record in the questionnaire (e.g., two fields corresponding to Field 4 and two fields corresponding to Field 5).

[0080] Typically a judge quantifies the intensity of the connotative meaning when selecting the connotative meaning itself. The intensity is a scaled value judgement of the judge. Table 7 lists the ratings scale for one embodiment.

[0081] In a preferred embodiment, a guiding set of anchor terms are included which are prerated for intensity. The judge's review of such guiding set improves consistency and accuracy among many judges completing the questionnaires. Specifically, the anchor terms are terms that are associated with scale numbers that represent the average intensity scores that other people have provided for various terms. Each connotative judge is expected to disagree with some of the scores represented by some of the anchor terms. For this reason, connotative judges are instructed to either, (i) highlight only those anchor terms with which they are comfortable, or (ii) alternatively, cross out those anchor terms with which they are uncomfortable. The connotative judge then compares the term he or she is scaling in the questionnaire table with his or her intensity of feeling associated with the anchored scales, choosing the scale number with the closest match.

[0082] The anchor terms are representative averages; they do not imply “correctness,” but rather provide the connotative judge with indicators of the relative strength of feeling that the connotative judge associates with the chosen emotional descriptor that is meant to be associated with each score number. In effect the anchor terms serve as a way for the judges to calibrate themselves to a scale of intensity. Table 8 presents a typical list of anchor terms associated with a group of emotional descriptors subsumed under the emotional category ‘Sadness,’ which is one of the eight emotional categories previously described.

[0083] A judge looks at each anchor term for a given rating in a given emotional category of the categories listed in Table 2. The judge selects one or more anchor terms under a given rating for a given emotional category which the judge feels most closely relates to the intensity rating subjectively felt by the judge.

[0084] In a preferred embodiment, the anchor terms are updated over time based upon many judges' response entries into Field 5 for each record. In particular the Field 4 and 5 entries are analyzed to identify terms which consistently are judged by many different judges to have the same connotative meaning and the same intensity. Such terms become reliable anchor terms. This is done on an ongoing basis in order to build up a large, reliable database of anchor terms.

[0085] As indicated above, an 8-point scale (including zero, indicating absence of the specified connotative feeling) is used to capture data for Field 5 in a preferred embodiment of the invention. However anchored scales of smaller or larger size, such a 3, 5, or 9 point scales may be used. Also, the number of anchor words or phrases may be greater than the 12 used in the preferred method, or fewer than 12. The number of anchor terms should be large enough to allow a choice permitting the connotative judge to select only those with which he or she is comfortable.

[0086] The connotative judge repeats the above steps to input data for Fields 4 and 5 for all records in the questionnaire, then returns the completed questionnaire table via the Internet, then receives another questionnaire table to evaluate. The next questionnaire table received by the connotative judge may contain exactly the same set of terms that was just evaluated, but accompanied by a different list of emotional descriptors to be used for coding Field 4, and a correspondingly different set of anchors for coding Field 5. Alternatively, the next questionnaire table may contain a completely different selection of terms, with a corresponding emotional descriptor list for Field 4 and an anchor term list for Field 5. The exchange of questionnaire tables continues iteratively for the duration of connotative data collection.

[0087] In a preferred embodiment of the invention, each block of 500 records is evaluated in this manner eight times, corresponding to eight emotional categories, each time by 24 different connotative judges. In this manner, a full-language dictionary database in any language, associating every context of every word with a very broad range of identified emotional connotations and their individual intensity levels, is constructed.

[0088] Data Integrity

[0089] Comparatively analyzing the connotative data associated with each block of records being processed serves to check for data integrity. Checking the data for integrity is part of an automated questionnaire processing function 90 (see FIG. 8) . An initial integrity processing step is to determine whether any of the 24 sets of data should be rejected as invalid because of anomalous data. This is accomplished by statistically comparing the score set of each individual judge with the combined score sets of the other 23 judges who evaluated the same set of words using the same lists of emotional descriptors. If the scores between any given judge's data and the aggregate data of the other judges in the panel are not statistically related, then the data set for the anomalous judge is rejected. Anomalous data may arise if, for example, a connotative judge is filling in random data to avoid the mental work involved in providing genuine connotative data, or if a judge is coding a large number of double zeros and ninety-nines, or if a judge's experience is so far out of the mainstream that his or her connotative associations are not representative of the larger population. In a preferred method of practicing the invention, a minimum correlation level of 0.6 is used as a data rejection threshold.

[0090] Further analysis includes determining how many valid non-zero scores remain after purging invalid scores and after accounting for 00 and 99 scores. A determination is then made to ascertain which emotional connotations the judges most often associate with each word or phrase. This is a function of four factors:

[0091] 1. The number of valid scores remaining after data purging;

[0092] 2. The number of emotional connotative descriptors in the list the judges had to choose from;

[0093] 3. The number of judges who selected the same emotional descriptor; and

[0094] 4. The probability that the same emotional descriptor was selected by more than one judge merely by chance.

[0095] The multinomial probability distribution below in equation (I) embodies the above factors: $\begin{array}{cc}P\left(y\right)=\frac{n!}{y!\left(n-y\right)!}*p^y{q}^{n-y}& \left(I\right)\end{array}$

[0096] where:

[0097] n is the total number of independent connotative judges evaluating the record;

[0098] y is the number of judges selecting a particular emotional descriptor;

[0099] p is the probability of the emotional descriptor being selected if the selection occurs by chance;

[0100] q is the probability of an emotional descriptor being excluded if the selection occurs by chance; and

[0101] P(y) is the probability of the emotional descriptor being selected by y judges if the selections occurred by chance.

[0102] Tables may be constructed of the probabilities P(y) of connotative judges independently selecting the same emotional descriptors by chance for various panel sizes (e.g., increasing incrementally up to 24, and/or additional panel sizes of 36, 72, 96, 120 or any other panel size), and emotional connotative descriptors available for selection (e.g., increasing incrementally up to 24, with additional category group sizes of 36, 72, 96, 120 or any other corresponding descriptor group size).

[0103] As an example, consider the following set of connotative judgments for one word evaluated by 24 connotative judges on the Amusement/Excitement emotional category, which subsumes 16 emotional descriptors. The total number of valid judgments after purging is 21 (Table 9).

[0104] The associated probabilities of chance selection of the same emotional descriptor by independent connotative judges, according to equation (I), are as follows:

[0105] In this example, only one emotional descriptor, “Glee,” has been selected by enough independent connotative judges (5 judges) to meet the test of statistical significance, and is retained in the main database 12 as a connotative association for the term being evaluated. For any given term, selection of emotional descriptors from one emotional category does not preclude selection of emotional descriptors from other emotional categories. Any given term is apt to evoke several kinds of emotional response simultaneously. Therefore, the same term is also evaluated in an identical manner on the other seven categories of emotional connotations listed in Table 2. Thus, the term may, or may not, finish with more connotative emotional descriptors added when the data collection procedure has been completed.

[0106] In a preferred embodiment of the invention, terms that receive no votes from the connotative judges on any of the connotative groupings, or too few votes on all eight connotative groupings to meet the test of statistical significance, are tagged as A“non-connotative,” so that such terms may be optionally excluded from further analysis or database querying.

[0107] As for connotative intensity, all 21 scores in the above example are valid, not just the 5 scores for the specific emotional descriptor “Glee,” because the 21 Field-5 scores represent the general emotional category, “Amusement/Excitement,” which subsumes the specific emotional descriptor, “Glee.”

[0108] By completion of data analysis, each of the eight emotional variables contains one mean (i.e., average) intensity score for each word or phrase. An unbiased estimate of the variance of the sample of 21 anchored intensity scores in Table 9 is calculated according to the following variance formula: $s^2=\frac{\sum {\left(X-\stackrel{\_}{X}\right)}^2}{\left(n-1\right)}$

[0109] from which the standard error of the mean for the sample is estimated in accordance with the following formula: $\frac{s}{x}=\frac{s}{\sqrt{n}}$

[0110] where:

[0111] In the present example, the average of the 21-score sample of Field-5 data presented in Table 9 is 4.4. The standard deviation of the 21-score sample is approximately 0.81, which, when divided by 21 yields a standard error of the mean of about 0.177, for a 95% confidence level about the mean of ±0.35. Further accuracy is obtained by programming the computer to identify and purge “outlier” scores. This is accomplished by comparing each score with the mean and purging scores that are higher or lower than a statistically specified distance from the mean.

[0112] In a preferred embodiment of the invention, terms that receive no Field-4 emotional descriptor votes from the connotative judges on any of the eight emotional categories (and therefore no Field-5 intensity scores) are tagged as “non-connotative,” so that, at the user's option, such terms may be excluded from further analysis or database querying.

[0113] Identifying Human Interest Area Relating to a Record

[0114] The Human Interest fields 48 may be defined in the same manner as the Field 4 data of Table 5. However, because the human interest fields are less subjective and relate more directly to denotative context, in a preferred embodiment assigned editors are used to define most of the human interest fields. However several variables on the Table 3 list of human interest fields, such as the miscellaneous fields for “Abstract-Concrete,” “Power,” and “Activity” are better left to evaluation by panels of connotative judges. These fields are defined as described above for the field 4 data and are subject to the same or similar data integrity procedures.

[0115] A preferred embodiment of the invention such as the one described herein is both human-judgment based and dynamic, reflecting the human and dynamic nature of language. Since the data provided by the connotative judges are key to the system and method, one may wish to establish a program of continuous update of the database, either at prescribed intervals or on an ongoing basis, such as through a World Wide Web site. In this way, connotative judges would be able to supply data continuously, with turnover of connotative judges easily managed, and the database, particularly the. connotative component, kept completely up to date allowing for new or changing connotative associations.

[0116] In one embodiment participating judges periodically or aperiodically receive a mini-database via e-mail or by logging onto a web site. The mini-database serves as the questionnaire allowing the judge to enter a code for the connotative association (see table 6) for a given emotional category (see table 2). The results are then processed as described above for data integrity (see questionnaire processing 90 of FIG. 8 and related description).

[0117] By practicing the above method and system of the present invention, a complete and accurate connotative language reference database 12 is constructed in any language, which then can be used to construct connotative equivalents of denotative language reference resources, such as connotative dictionaries, connotative thesauruses, and connotative text analysis tools. In addition, the anchored system of judgment elicitation may be applied in any field requiring accuracy in the elicitation of qualitative data where Likert-type scaling is applicable.

[0118] Meritorious and Advantageous Effects

[0119] One advantage of the system for identifying connotative meanings is that reliable associations, including connotative descriptions and intensities, are identified for given words and phrases in each of their denotative contexts. Another advantage is that the associations are maintained over time with changes in the vernacular or other changes/occurrences affecting connotative association.

[0120] Although a preferred embodiment of the invention has been illustrated and described, various alternatives, modifications and equivalents may be used. Therefore, the foregoing description should not be taken as limiting the scope of the inventions which are defined by the appended claims.