Patent Application: US-87094801-A

Abstract:
a linguistic assistant for domain analysis methodology to help a user define object models from documents such as requirements documents and validate object models against such documents . the approach is domain - independent and language - independent , mainly relying on widely available linguistic resources for the text analysis .

Description:
as indicated above , the lida methodology can be applied to any object - oriented modeling language that distinguishes classes ( or entities ), as well as associations between the classes ( or relationships between the entities ). the lida methodology was reduced to practice in the lida tool using uml . the following detailed description of the invention is thus presented in uml terminology . the document analysis component identifies word base forms and noun phrases contained in a document ; determines their parts of speech and frequencies ; records collocations between pairs of word base forms and frequencies of these collocations , and identifies all textual contexts of a particular word base form or noun phrase in a document . this information is stored in a structure called analyzed textual document that is used by the other components . the document - model comparison component automatically compares labels of model elements with word base forms and noun phrases in an analyzed textual document , taking into account their frequencies , and generates warnings if there are certain discrepancies . the model paraphrase component automatically creates descriptions of models in natural language from the representation of models in uml . the text analysis environment supports the user in the identification of the candidate model elements via a convenient graphical interface . the model description environment supports the user during model creation , evolution and validation via a convenient graphical interface . the lida methodology of iteratively deriving object models from documents includes the following three phases : in the model element identification phase , the user works within the text analysis environment . the user identifies the model elements candidates ( classes , attributes and roles in associations ) using linguistic information contained in the analyzed textual document ( word base forms , noun phrases , collocations , word frequencies , and textual contexts ) produced by the document analysis component . the identified model element candidates are automatically recorded by the model description environment . in the model association phase the user works within the model description environment and defines relationships between model element candidates , i . e . declares associations between classes and assigns attributes to classes . in doing so , the user takes into account the textual contexts of word base forms and noun phrases and their collocations in these contexts , relying on information which is contained in the analyzed textual document . the defined associations are recorded by the model description environment . in the model validation phase the user validates a particular model against a particular document , using the document - model comparison component , as well as the model paraphrase component . the text below first describes the components of the lida tool in more detail . this is followed by a detailed description of the three phases of the lida methodology , which use the output of the linguistic processing components of the lida tool and are supported by its model description environment . the input to the document analysis component ( 7 ) consists of a document such as a requirements document ( 13 ). the output of the document analysis component ( 7 ) is an analyzed textualdocument ( 13 ) consisting of ( i ) lists of the word base forms and noun phrases contained in a document ; ( ii ) part of speech and frequency for each listed word form or phrase ; ( iii ) collocations between pairs of word base forms and frequencies of these collocations ; and ( iv ) all textual contexts of a particular word base form or noun phrase in a document . to illustrate how the document analysis component ( 7 ) works , let us consider the following extract from a document ( 13 ): the document analysis component ( 7 ) begins with the morphological analysis of each sentence of the document in order to determine the part of speech and the base form of each word contained in the sentence . with each sentence is associated the list of word base form / part - of - speech pair it contains , excluding stop words that are considered irrelevant for the identification of model elements . the stop words include articles , prepositions , pronouns , conjunctions , punctuation marks , adverbs , and the two verbs be and have . as a result of this processing , a list of stemmed sentences is produced , which is the list of sentences contained in the document with their associated list of stemmed nouns , verbs and adjectives . table 2 shows the resulting list of stemmed sentences for the document extract in table 1 . further , the document analysis component ( 7 ) creates a list of the word base form / part - of - speech pairs and a list of all noun phrases contained in the document . it associates with each item on these lists the following information : ( ii ) a list of all sentences containing occurrences of the item in the document ; ( iii ) the noun , verb , and adjective base forms and noun phrases that collocate with the item in the same sentence or in the preceding or following sentences , with frequencies for each collocation . the resulting information is combined in a data structure called the analyzed textualdocument ( 14 ) used in all phases of the lida methodology . the analyzed textualdocument ( 14 ) for the document ( 13 ) extract in table 1 is shown in table 3 . the column “ location of occurrences in text ( sentences )” gives just the numbers of sentences due to lack of space ; in the lida tool , however , the user can see these sentences arranged in a concordance display , which is a proven effective display method in linguistic processing . the concordance display of sentences for the noun word base ‘ course ’ in the document ( 13 ) extract in table 1 is shown in table 4 . [ 0069 ] table 4 professors teach courses which students take courses have a number and a name and a maximum enrollment each course is taught by one professor , sometimes two students must take at least one course and each professor teaches exactly one course each professor teaches exactly one course the text analysis environment ( 5 ) is an interface component for the identification of candidate model elements . a sample screen shot of the text analysis environment ( 5 ) is shown as fig6 . the main features of the text analyzing environment ( 5 ) include : display of selected information from the analyzed textualdocument ( 14 ); capability for the user to identify candidate model elements by highlighting the corresponding words , word base forms and noun phrases in different colors , each color corresponding to a particular model element type . display of words , word base forms and noun phrases in the text using distinct colors depending on the element types ( class , attribute , role , etc .) that they denote in the associated model . the text analysis environment component ( 5 ) is tightly integrated with the model description environment ( 6 ) described below so that any change in the identification of model elements directly propagates to the model description environment ( 6 ). the model description environment ( 6 ), illustrated in fig7 is an interface for building a model from the candidate model elements . the main functions of the model description environment component ( 6 ) include : displaying lists ( vocabularies ) of candidate model elements , either identified in the text analyzing environment ( 5 ) or added directly in the model description environment ( 6 ). in fig7 the candidate model elements are displayed on the left side of the window . any changes to the candidate vocabularies propagate to the text analysis environment ( 5 ). this bidirectional propagation of information between the text analysis environment ( 5 ) and the model description environment ( 6 ) enables a developer to go back and forth between the text analysis process and the model building process . the resulting interleaving of these processes is a crucial part of the lida methodology offering operations for combining model elements into a class diagram corresponding to the object model ( 16 ). displaying textual contexts such as the one illustrated in table 4 , which are used in the process of model building and validation displaying textual paraphrases of model elements produced by the model paraphrase component ( 9 ), which are used to validate or document the model . displaying warnings produced by the document - model comparison component ( 8 ), which are used to validate the model ( 16 ). the input to the document - model comparison component ( 8 ) consists of the following information : ( i ) an analyzed textualdocument ( 14 ) produced by the document analysis component ( 7 ) for a given document ( 13 ); ( ii ) the current model ( 16 ) in the model description environment ( 6 ). the document - model comparison component ( 15 ) produces a list of warning messages resulting from the comparison of these inputs . absent model element with high word base form frequency : a warning is generated when there is a noun , adjective or verb base form , or a noun group with high frequency in the document ( 13 ) that is not found among the labels of the model elements . this can indicate either that a model element needs to be added to the model or that an existing model element is labeled with a conceptual synonym of a word or phrase used in the document ( 13 ). the component records conceptual synonyms ( including acronyms ) of document terms which the user identifies among the model element labels . upon subsequent use of the component any usage of user - provided synonyms is flagged by the component without producing a warning message . existing model element with low word base form frequency ; a warning is generated when there is a label in the model for which a corresponding noun , adjective or verb base form , or a noun group , either does not appear or has very low frequency in a large document ( 13 ). this can indicate that an element with this label either is not relevant for a given document ( 13 ) or that a conceptual synonym was used for the label ( see above ). unassociated model elements with collocated word base forms ; a warning is generated when there are model elements corresponding to word base forms or noun phrases that often collocate in the documents ( 13 ) but that are not associated in the model . this can indicate a missing association between two classes or between a class and an attribute . as the model paraphrase component ( 9 ), lida integrates modelexplainer ( lavoie et al ., 1996 ), a tool that automatically generates fluent english hypertext descriptions for uml object models . the screen in fig8 illustrates a description of the classes student and course based on the model shown in fig7 . the descriptions are generated from customizable text plans ( lavoie et al ., 1997 ) set in the above example to include the following class information : super - classes , class attributes , subclasses , and associations with other classes . hyperlinks generated with the descriptions allow the user to obtain additional descriptions and browse the model in text . providing textual support to a lida user during validation of the model with domain experts who may not be familiar with the uml graphical notation used in modeling . allowing a user to compare the generated text with the original document for validation . [ 0100 ] fig3 shows a flowchart with a decomposition of the model element identification phase ( 1 ). the model identification phase ( 1 ) is performed in the text analysis environment ( 5 ) using linguistic information in the analyzed textualdocument ( 14 ). using functionality provided in the text analysis environment (( 5 ); section 1 . 2 ), the user identifies basic model element candidates ( e . g ., uml classes , attributes and roles in associations ). the identified elements are automatically recorded by the model description environment ( 6 ). as a result of the model element identification phase ( 1 ), the user produces a model vocabulary : a list of classes , attributes and roles . the model vocabulary is automatically stored in the model description environment ( 6 ) and displayed via its graphical interface . during the model element identification phase ( 1 ) the user follows a set of guidelines which involve three main steps , that can be performed in any order : in step ( 1 . 1 ) the user considers and possibly declares as class candidates the most frequent noun base forms or noun phrases in the analyzed textualdocument . for example , in the analyzed textualdocument in table 3 , the noun base forms ‘ course ’, ‘ professor ’, ‘ employee ’ and ‘ student ’ have the highest number of occurrences ( 5 , 4 , 3 and 3 respectively ) and can be declared as candidate classes course , professor , employee , and student . in step ( 1 . 2 ) the user considers and possibly declares as attribute candidates the most frequent noun or adjective base forms that collocate with noun base forms or noun phrases already identified as candidate classes . for instance , the noun base form ‘ number ’ from the analyzed textualdocument in table 3 can be declared an attribute candidate number because it frequently collocates with ‘ course ’, which has been already declared a class candidate . in step ( 1 . 3 ) the user considers and possibly declares as role candidates the most frequent verbs in the table of occurrences . for instance , the verb base forms ‘ teach ’ and ‘ take ’ in the analyzed textualdocument in table 3 have the highest number of occurrences ( 3 and 2 respectively ) and can be declared as roles teach and take . a model vocabulary defined on the basis of the analyzed textualdocument ( 14 ) illustrated in table 3 is shown in table 5 . attributes are assigned to classes and associations are declared between classes during the model element association phase ( 2 ), which is described next . according to the lida methodology , these two phases can be interleaved at the user &# 39 ; s convenience . in particular , the user can declare a class and an attribute , then immediately proceed to the model element association phase ( 2 ) and associate these elements , then return to the model element identification phase ( 1 ) and declare more elements , and so on . such interleaving is fully supported by the model description environment ( 6 ) of the lida tool . as a result of the model element association phase ( 2 ) the user produces or develops a model in a language such as uml , assigning attributes to classes and defining associations between classes and their roles in these associations on the basis of information from the analyzed textualdocument . the work is performed via the graphical interface of the model description environment ( 6 ), and the resulting model is stored and graphically displayed there . during the model element association phase ( 2 ) the user follows a set of guidelines , which consist of two main steps that can be performed in any order : ( ii ) identification of associations between a class and its attributes ( 2 . 2 ). for each noun base form or a noun phrase n declared as a class candidate in the model vocabulary , identify all verb base forms vi declared as role candidates and noun base forms or noun phrases ni declared as class candidates where the verb base form vi collocates with n ( as indicated by the analyzed textualdocument ( 14 )) and where ni collocates with vi and occurs in the same sentence as n ( as indicated by the analyzed textualdocument ). this activity should produce a list of triples ( n , vi , ni ) indicating possible class associations . for example , for a class candidate course the analyzed textualdocument ( 14 ) indicates that the corresponding noun word base ‘ course ’ collocates with two verb base forms ‘ teach ’ and ‘ take ’ that were declared as roles teach and take and that these two verb base forms collocate with the noun base forms ‘ professor ’ and ‘ student ’, respectively . professor and student were also declared as class candidates . this information suggests two possible associations . the first is course ( one or more )— professor ( one or more ) with a role teach for professor , and a role taught by for course . the second is course ( one or more )— student ( one or more ) with a role taken by for course and a role take for student . the cardinality ( 1 :*, 0 :*, *:*, . . . ) of the association is established by analyzing the determiners and modifiers ( the , any , many , one or more , etc .) used with the nouns corresponding to classes in the document , as well as by observing whether these nouns are used in singular or plural . the user can conveniently get this information at a glance in the sentence concordance display for a class . for each noun base form or a noun phrase n declared as a class candidate in the model vocabulary , identify all noun or adjective base forms ai declared as attribute candidates that collocate with n , as indicated by the analyzed textualdocument ( 14 ). as a result of this activity , a list of tuples ( n , ai ) is produced establishing possible attribute association with classes . for example , for a class candidate course the analyzed textualdocument indicates that the corresponding noun base form ‘ course ’ collocates with the noun base form ‘ number ’. this corresponds to a possible association between an attribute and a class : number is an attribute for course . a uml model produced on the basis of the analyzed textualdocument ( 14 ) in table 2 is shown below in table 6 . this uml model is displayed graphically in the model description environment ( 6 ) according to the standard uml notation . the graphical representation of the model in table 6 is partially illustrated in fig7 . as indicated above , the lida methodology is not limited to modeling in uml , but is illustrated here using the uml terminology of the implemented lida tool . [ 0128 ] fig5 shows a flowchart of the model validation phase ( 3 ). during the model validation phase ( 3 ) the user concentrates on validating a particular model against a particular document , using the document - model comparison component ( 8 ), as well as the model paraphrase component ( 9 ). at the user &# 39 ; s request , the document - model comparison component ( 8 ) performs the comparison between the model ( 16 ) and the document represented in the analyzed textualdocument ( 14 ). if warning messages are produced , the user analyzes them and decides whether to take corrective action . in particular , if the warning absent model element with high word base form frequency is produced , the user can either add a missing model element to the model , or re - label some element , or record a note that a meaningful synonym was used ( leading to the discrepancy between the document and model vocabularies ). if the warning existing model element with low word base form frequency is produced , the user can either delete a potentially irrelevant element from the model , or , as above , record a note that a meaningful synonym was used . finally , if a warning unassociated model elements with collocated word baseforms is produced , the user can add to the model a missing association between two classes or between a class and an attribute . also at the user &# 39 ; s request , the model paraphrase component ( 9 ), integrated with a text generator such as modelexplainer ( lavoie et al ., 1996 ), generates fluent hypertext descriptions in a natural language such as english for the current object model ( 16 ) that can be used for the validation of the model ( 16 ). a sample description is illustrated in fig8 . object models often contain semantic errors when these models are developed by people ( including experienced analysts ) who are not familiar with the graphical notation . natural language paraphrases can help developers identify these semantic errors . for example , assigning the roles of an association in the incorrect order is a frequent mistake . in the model illustrated in fig7 this type of error would occur if one would reverse the roles taught by and teach between the class course and the class professor , and the roles taken by and take between the class course and the class student . the textual paraphrase of the resulting model would be grammatically correct but not semantically correct : “ a course teaches one or more professors . in addition , a course takes one or more students ”. burg , j . f . m . and van de riet , r . p . 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