Patent Application: US-39872806-A

Abstract:
a method uses linguistic units of analysis to identify the authorship of a document . the method is useful to determine authorship of brief documents , and in situations where there are less than ten documents per known author , i . e . when there is scarcity of text . the method analyzes parameters such as the syntax , punctuation , and , optionally the average word and paragraph length , and when the parameters are analyzed using statistical methods , obtains a high degree of reliability . the method can be applicable to numerous languages other than english because the variables selected are characteristic of most languages . the reliability of the method is verified when subjected to a cross - validation statistical analysis .

Description:
the present invention comprises a system and a method for determining the authorship of a textual work , such as a letter , a note , a book , manuscript , or other document . because embodiments of the present invention can be used with short works , the system and method of the present invention has applications in the forensic setting , such as , for example only and not intended as a limitation , identifying a known criminal , a kidnapper , a hostage - taker , or person ( s ) involved in various terrorist activities . an embodiment of the present invention can be utilized with a computing system 10 , such as an exemplary system illustrated in fig2 . the computing system 10 illustrated is only a representative computing system , and is not intended to be limiting factor in either the scope of the use , of the functionality of the present invention , except as described in the claims . consequently , embodiments of the present invention are operational with a variety of other general purpose or special purpose computing system configurations or environments . some examples , and again , these are examples only and not intended to be any limitations on the present invention , are personal computers , computer servers , laptop computers and devices , hand - held computers and devices , multiprocessor systems , microprocessor - based systems , programmable consumer electronics , network personal computers , minicomputers , mainframe computers , devices referred to as set - top boxes , distributed computing environments that include any of the systems or devices described herein , and the like . embodiments of the present invention may be utilized in the general context of computer - readable directions , such as software programs that can be effected by a computer . examples of such programs include , but are not limited to , programs , routines , components , data structures , objects and the like that carry out specific functions or implement specific abstract data types . embodiments of the present invention may also be carried out where the tasks are performed by one or more remote processing devices that are linked through a communications network , such as in a distributed computing environment . in a distributed computing environment , program modules may be situated both in local and remote computer storage media , including memory storage devices . the inventor has written a software application designated “ alias ” ( automated linguistic identification and assessment system , chaski (( 1997 , 2001 )) to perform the analysis which follows . use of this software involves a human interacting with the program to check the results of the automated tagging and for classifying syntactic phrases . each text is processed using alias , a software program for the purpose of databasing texts , lemmatizing , lexical frequency ranking , lexical , sentential and text lengths , punctuation - edge counting , pos - tagging , n - graph and n - gram sorting , and markedness subcategorizing . alias is thus able to provide a large number of linguistic variables . without intending to be a limitation , this program is designed to be used by computers running the windows and macintosh operating systems . the computer can be a stand - alone computer , or connected to one or more computers using a network , such as a wired or wireless network , the world wide web , the internet , or the like . the text in question is entered into the computer using any one of a variety of input means , such as by scanning , facsimile , direct entry using a keyboard , photography , file transfer , copying from a magnetic , optical or video disk , or other appropriate means of inputting information into a computer system . thus , a representative computing environment comprises a computer 12 equipped with a microprocessor 14 , random access memory 16 , read - only memory 18 , a mass - storage device 20 such as a hard disk , and a communications means 22 to enable the computer 12 to communicate with output devices such as a printer 24 . the communications means 22 may comprise a printer interface , which may be either a parallel , serial , universal serial bus (“ usb ”) or ieee 1394 firewire , and appropriate cabling to the printer , or may be a wireless communications interface . a second communications means 22 a may comprise a modem and communications port , such as serial port , scsi or scscii , or usb interface enabling the computer 12 to communicate with communications network 32 , such as a public telephone net , the internet , an extranet , the world wide web , or other wired or wireless communications system . such communications means 22 may also comprise a cable modem and a connection to a cable services , an isdn modem and isdn line , a digital subscriber line (“ dsl ”) modem and dsl line , t - 1 line , and the like , now known or to be developed in the future . the representative system further includes an input means , such as a keyboard 26 , a mouse 28 , or similar device such as a touch - pad or pointing device such as the accupoint ( registered trademark of toshiba america information systems , inc . for a cursor control device for computers , attached to the computer keyboard .) or scanner ( not shown ), and a visual display means 30 such as a cathode ray tube (“ crt ”) monitor , flat screen , liquid crystal display (“ lcd ”), plasma , dual - scan monitor , thin - film transistor (“ tft ”), active - matrix monitor , or the like , now known to be developed in the future . software 33 which is resident in the computer &# 39 ; s memory , or which may be stored in the memory of another computer such as a server in a computer network or other central computer includes the operating system necessary for operation of the computer . among the possible operating systems the macintosh ® operating system ( registered trademark of apple computer , cupertino , calif . ), windows , or the like . the various embodiments of the present invention are based on particular concepts of generative grammar which have been briefly described in the background section d . the inventor has created a method for analyzing punctuation to create three graphemic variable sets , the syntactic edge version ; the syntactic edge and emphasis version ; and the locality version . the graphemic analysis of the textual data counts the punctuation as it is attached to different levels of linguistic structure , from word to phrase to clause to sentence to discourse . the inventor has created a method for analyzing syntactic phrases to create three syntactic variable sets : a full version ; an xp version ; and a nom version . sometimes there is not enough data , especially in a forensic situation , to use the full version of the syntactic variables . therefore , the inventor has created two collapsed versions of the full version , called the xp version and the nom version . either the xp or nom variable sets can be used when the amount of textual data is not large enough to use the full version . the syntactic analysis of the textual data divides each syntactic function into its marked and unmarked versions . in an embodiment of the present invention , after the text has been entered into the system , the text is split into its constituent sentences , shown schematically in fig3 . each punctuation mark in each sentence is categorized by the type of syntactic edge to which it attaches and any punctuation marks with additional discursive functions are categorized as such , resulting in four variables . ( the detailed schema is described below in section 1 ). each word is labeled as to its part - of - speech (“ pos ”). ( the detailed schema for pos - tagging is described below in section 2 ). the combinations of words into phrases is then listed for each word . the phrases are then listed for each head ( for example , all of the noun phrases , all of the verb phrases , all of the adjective phrases , etc .). the phrases are then characterized by markedness . for each head there are two subtypes : marked and unmarked . the subtypes for each head are counted , which results in seventeen variables . ( the detailed schema is described below in section 2 .) the number of phrases can be reduced by summing by markedness , which results in two variables . ( the detailed schema is described below in section 2 .) the number of phrases can be reduced by nominal / predicative contrast , which results in four variables . ( the detailed schema is described below in section 2 .) these variables are combined with two stylometric variables . ( the detailed schema is described below in section 3 ). the above counts are then analyzed using a variety of statistical procedures . ( the detailed schema is described in section 4 ). section 2 explains the pos tagging scheme and the method for obtaining the syntactic variables . section 3 shows how these variables are combined with stylometric variables to create the variable sets which can be fed into the classification algorithms . section 4 describes the settings for some of the classification algorithms which have been used with the different variable sets , including the lodo cross - validation method for sentence - level data , and provides experimental results of authorship attribution on the forensically - feasible dataset shown in table 3 above . the graphemic analysis produces a set of variables which organize the punctuation marks in textual data in a novel way , i . e . in their relation to syntactic and discourse functions in the text . syntax is the study of the possible combinations of word units into grammatical phrases . discourse is the study of how sentential units are combined and how communicative effect is conveyed ( e . g . how we recognize irony , agreement and other rhetorical effects ). grammatical combinations ( also known as constituent structures ) have beginnings and endings . when we combine the words [ the ] and [ dog ] into the phrase [ the dog ], we have created a constituent structure known as a noun phrase which begins with [ t ] and ends with [ g ]. constituent structures can be large ; for example , a sentence is created by combining noun phrase and verb phrase [ noun phrase “ a sentence ” plus verb phrase “ is created by combining a noun phrase and a verb phrase ”]. a smaller unit is the phrase ; for example a prepositional phrase is created by combining a preposition with a noun phrase [ preposition “ with ” plus a noun phrase “ a noun phrase ”]. even smaller units of combination are possible when the internal , minimal , meaning - bearing units of words ( called morphemes ) are combined to create words ; for example , a noun combined with a possessive morpheme creates a possessive noun [ a noun “ jim ” plus possessive morpheme creates “ jim &# 39 ; s ”]. since these combinations have beginnings and endings as constituent structures , these beginnings and endings are called edges . each punctuation mark is classified by what type of syntactic edge it is marking . chaski ( 2001 ) showed that syntactically - classified punctuation had a better performance than using simple punctuation marks for discriminating authors while preserving intra - author classification . authors may share the same array of punctuation marks , but the placement of the punctuation marks appears to be what matters . this approach to using punctuation as an authorial identifier is different from the approaches advocated by questioned document examination ( hilton , 1993 ), forensic stylistics ( mcmenamin 2003 ), or the computational studies discussed in the background section . the inventive classification procedure is illustrated in examples below . there are four syntactic edges which are considered : edge of sentence (“ eos ”); edge of clause (“ eoc ”); edge of phrase (“ eop ”) and edge of morpheme ( or word - internal ) (“ eom ”) and one discursive function (“ epmh ”). this gives five variables : eos , eoc , eop , eom and emphatics ( emph ). any punctuation mark which marks the edge of a sentence is counted as an eos . example a shows a sentence marked off by an exclamation point and quotation mark for 2 eos . ( the leftmost quotation mark is counted as one edge - of - phrase marker ). any punctuation mark which marks the edge of a clause within a sentence is counted as an eoc . example b shows a clause marked off by a comma for 1 eoc . example b : after he joined the army , he saw how precious life can be . any punctuation mark which marks the edges of a phrase is counted as an eop . the phrase can be marked at both the beginning and ending of the phrase or only at the ending of the phrase . example c below shows 2 eop , while example d shows 1 eop . example c : he went , for a while , to the college in town . example d : for a while , he went to the college in town . any punctuation mark which marks the edge of a morpheme is counted as an eom . example e shows word - internal punctuation of 1 eom each . example f shows phrase - internal punctuation for 3 eom . any punctuation mark which indicates discursive emphasis such as multiple punctuation marks , underlining , bolding , italics , capitalization , emoticons is counted as an emph ( for emphatics ). example g shows 2 emph , one of which is lexical - phrasal ( the you ) and the other of which is clausal ( the ????). the punctuation relating only to syntactic structure of the sentence are included in this variable set . : eos , eoc , eop and eom . due to the data requirements of different classification algorithms , eos and eoc can be summed together , for three variables in this version ( eos + eoc , eop and eom ) or eos can be excluded completely ( as it is usually not normally distributed ) for the following three variables in this version ( eoc , eop , eom ). the syntactic edge variables are combined with the discursively - based variable emph . this variable set thus includes eoc ( optionally summed with eos ), eop , eom and emph . an important concept within syntactic analysis is locality , which refers to the syntactic structure within which a relation can occur and beyond which the relation cannot occur . in syntax , one localizing or bounding node is the beginning of a clause ; there are syntactic dependencies which can occur within a clause which cannot go beyond the clause ( like subject - verb agreement ). in the locality version of the punctuation variable set , the variables are subcategorized by whether they occur at the above - clause or below - clause levels . the variables eom and eop are summed together for the lexical - phrasal level . the variables eos and eoc are summed together for the clausal level . emph is divided into lexical - phrasal ( such as the you in example g ) and clausal ( such as the ???? in example g ). this variable set thus includes : the syntactic analysis begins with part - of - speech (“ pos ”) tagging . each word in the textual data is tagged for its part - of - speech in the sentence in which it occurs . as mentioned in section d above , the inventive pos procedure works well with short texts which are typical of forensic authorship attribution . the pos tags used are shown in table 5 : the pos tags are the input to syntactic analysis . the syntactic analysis produces a set of variables which organize the syntactic structures in textual data in a novel way based on their markedness ( whether they are marked or unmarked ) or whether they are nominal or not ( nominal or predicative ). for syntactic structures , the unmarked contrast is the most common and often the most easily parsed because its informational content is unambiguous , while the marked contrast is typically less frequent , sometimes more difficult to parse because it can pose several different parsing attachments and its informational content is determined by different attachments . after each word is pos - tagged , the method classifies each instance of each grammatical category into marked ( m ) or unmarked ( u ) subcategories according to the following schemes : linguistic theory shorthand uses “ x ” to represent any syntactic category , such as an adjective , determiner , noun , modifier , preposition , etc . the term xp thus represents any type of syntactic phrase or function . the full version ( from section 2 . 1 ) is collapsed by summing the marked counts and unmarked into two variables : dp can also be added in optionally to both the mxp and uxp variables . 2 . 3 . syntactic analysis : nom version : marked and unmarked syntactic phrases collapsed by nominal - predicative contrast the full version ( from section 2 . 1 ) is collapsed by summing the marked and unmarked counts into four variables : the variables explained above are combined with each other and with two traditional stylometric variables to create different variable sets for forensic authorship attribution . the stylometric features are average word length (“ avgwl ”), and average paragraph length (“ avgpl ”). avgwl is calculated using all words in the sentence . avgpl is calculated by dividing the number of paragraphs in the document by the number of sentences in the document ; it is only used at the document level analysis . 1 . full version of marked / unmarked syntax with graphemic 1 with stylometric 1 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ eoc , eop , eom ]; graphemic avgwl stylometric 2 . xp version with graphemic 1 with stylometric 1 [ uxp , mxp ]; syntactic [ eoc , eop , eom ]; graphemic avgwl stylometric 3 . nom version plus graphemic 1 plus stylometric 1 [ nom , pred , comp , mod ]; syntactic [ eoc , eop , eom ]; graphemic avgwl stylometric 4 . xp version with graphemic 1 with stylometric 2 [ uxp , mxp ]; syntactic [ eoc , eop , eom ]; graphemic avgwl ; stylometric avgpl stylometric 5 . nom version plus graphemic 1 plus stylometric 2 [ nom , pred , comp , mod ]; syntactic [ eoc , eop , eom ]; graphemic avgwl ; stylometric avgpl stylometric 6 . xp version with graphemic 2 with stylometric 3 [ uxp , mxp ]; syntactic [ eoc , eop , eom , emph ]; graphemic avgwl stylometric 7 . nom version plus graphemic 2 plus stylometric 3 [ nom , pred , comp , mod ]; syntactic [ eoc , eop , eom , emph ]; graphemic avgwl stylometric 8 . xp version with graphemic 2 with stylometric 3 [ uxp , mxp ]; syntactic [ eoc , eop , eom , emph ]; graphemic avgwl ; stylometric avgpl stylometrid 9 . nom version plus graphemic 2 plus stylometric 3 [ nom , pred , comp , mod ]; syntactic [ eoc , eop , eom , emph ]; graphemic avgwl ; stylometric avgpl stylometric 10 . xp version with graphemic 3 with stylometric 1 [ uxp , mxp ]; syntactic [ clausal , subclausal ]; graphemic avgwl stylometric 11 . nom version plus graphemic 3 plus stylometric 1 [ nom , pred , comp , mod ]; syntactic [ clausal , subclausal ]; graphemic avgwl stylometric 12 . xp version with graphemic 3 with stylometric 2 [ uxp , mxp ]; syntactic [ clausal , subclausal ]; grapheic avgwl ; stylometric avgpl stylometric 13 . nom version plus graphemic 3 plus stylometric 2 [ nom , pred , comp , mod ]; syntactic [ clausal , subclausal ]; graphemic avgwl ; stylometric avgpl stylometric 14 . full version of marked / unmarked syntax with graphemic 1 with stylometric 2 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ eoc , eop , eom ]; graphemic avgwl ; stylometric avgpl stylometric 15 . full version of marked / unmarked syntax with graphemic 2 with stylometric 1 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ eoc , eop , eom , emph ]; graphemic avgwl stylometric 16 . full version of marked / unmarked syntax with graphemic 2 with stylometric 2 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ eoc , eop , eom , emph ]; graphemic avgwl ; stylometric avgpl stylometric 17 . full version of marked / unmarked syntax with graphemic 3 with stylometric 1 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ clausal , subclausal ]; graphemic avgwl stylometric 18 . full version of marked / unmarked syntax with graphemic 3 with stylometric 2 [ uap , map , uconj , mconj , udp , mdp , syntactic ump , mmp , unp , mnp , upp , mpp , uvp , mvp , subn , n - pro , intj ]; [ clausal , subclausal ]; graphemic avgwl ; stylometric avgpl stylometric not all of the variable sets can be used with every classification algorithm because of textual data restrictions or the distributions within the counts from the textual data . the classification algorithms allow for optional settings within commonly used statistical packages such as spss , sas , lnknet or dtreg . ( sas is a commercial software package distributed by sas institute , cary , n . c . ; dtreg is commercial software for statistical analysis ; lnknet is open source software for statistical analysis available from mit lincoln laboratory , cambridge , mass .). in background section c , experimental results using the invention were briefly summarized in table 4 , and which is repeated below for convenience . these experiments are now described in detail so that the operation of the method and particular classification algorithm settings can be understood . in this example , document analysis was done using analysis of sentences , but the prediction of authorship is on the document level variable set 1 includes 21 variables . seventeen of these variables are syntactic ( related to phrasal type ); three are graphemic ( related to punctuation ) and one is stylometric ( average word length ). the variable counts were extracted for each sentence for each author . the commercial statistical software spss was used to perform discriminant analysis . the statistical model for each author pair was built using sentence level data for the authors , with one document &# 39 ; s sentences left out from the model building . instead of predicting the membership of the holdout ( or left - out ) document based on its sentences , the means of each variable ( using all the sentences in the document ) are used , such that while the model is built at the sentence level , the prediction is on the document level . a pair - wise discriminant analysis was performed using the feature counts for each sentence of each document . let x i , j , k denote an m - vector containing the counts for m syntax features ( m = 1 , . . . , m ) in sentence k = 1 , . . . , k i , j of document j = 1 , . . . j i written by author i = 1 , . . . , n a . here k i , j denotes the number of sentences in document j by author i , j i denotes the number of documents by author i , and n a denotes the number of authors . the database contains a total of n a = σ i σ i k i , j sentences found in n d = σ i j i documents written by n a authors . in the pair - wise analysis , only the documents for a selected pair of authors are included in the analysis , the procedure is repeated for all possible author pairings . there are n p = n a ( n a − 1 )/ 2 author pairs to consider in the cross - validation analysis . for each pair of authors , data at the sentence level was used to estimate a linear function for discriminating between the two authors . then the prediction of authorship is made using document - level mean feature counts per sentence in the document with “ unknown ” authorship . this method provides a large sample size for estimating the discriminant functions by using feature counts at the sentence level . the larger sample size permits the use of a large number of feature variables for discriminating between authors . use of feature counts at the document level does not support the use of a large number of variables , unless a relatively large number of documents is available for each author . in our data ( see tables 2 and 3 ), as few as three documents were available for several authors , a situation that is very common in forensic applications of authorship attribution . the larger sample size also provides a better estimate of the discriminant function coefficients and the pooled variance - covariance matrix which is used in linear discriminant analysis to compute the mahalanobis distances between data points . these distances provide the basis for determining the probabilities of group membership , i . e . authorship attribution . the pair - wise cross - validation was conducted using a “ leave one document out ” (“ lodo ”) procedure . in this procedure , one document ( written by , for example , author r ) was selected from the set of documents for a pair of authors ( r and s ), and all sentences in the lodo document were removed from the data set for pair - wise analysis . the goal was to correctly predict the authorship of the lodo document by fitting a model using only the remaining ( j r − 1 ) documents known to be written by the author r and the j s written by the other author . the discriminant function for this pair was estimated using all the sentences in the j r + j s − 1 documents which remained after sentences in the lodo document were removed . after the discriminant function for this pair was estimated using spss , it was used to predict the authorship of the lodo document . the vector of mean feature counts for the lodo document was calculated as y = σ k x i , j , k / k i , j . these mean feature counts , averaged over all sentences in the lodo document , are then inserted in the estimated discriminant function to assign authorship of the lodo document to one of the pair of authors . table 16 shows the accuracy results for this variable set and classification algorithm , reporting the results for all ten authors when each is compared only with authors of the opposite sex . table 17 shows the accuracy rates for this variable set and classification algorithm when each of the authors is compared with each other , including both same - sex and opposite - sex pairs . variable set 2 comprises six variables . two of these variables ( mxp , uxp ) are syntactic ( related to phrasal type ); three ( eoc , eop , eom ) are graphemic ( related to punctuation ) and one ( avgwl ) is stylometric ( average word length ). the variable counts were extracted for each sentence for each author , and then summed for each document for each author . linear discriminant function analysis was performed using spss . the statistical model for each author pair was built using document level data for the authors , with leave - one - out ( loo ) cross - validation . this kind of cross - validation , where the unit of analysis and the unit for cross - validation are the same ( both document - level data ), is a standard option in spss ; it is unlike lodo cross - validation which was described earlier . the following spss options were selected : stepwise entry of variables , default values of f to enter or remove variables , mahalanobis distance and prior probabilities computed based on group size . table 18 shows the accuracy results , with an overall accuracy rate of 95 %; the individual authors &# 39 ; accuracy rates range from 92 % to 98 %. one author pair had no variables qualify for the analysis under these parameters . variable set 3 includes seven variables : four ( nom , pred , comp , mod ) are syntactic ( related to phrasal type ); three ( nom , pred , comp , mod ) are graphemic ( related to punctuation ) and one ( avgwl ) is stylometric ( average word length ). this experiment was conducted using the variables of variable set 3 , and omitting the average word length variable . the variable counts were extracted for each sentence for each author , and then summed for each document for each author . linear dfa was performed using spss , with the statistical model for each author pair built using document level data for the authors , with leave - one - out ( loo ) cross - validation . the following spss options were selected : stepwise entry of variables , f set to 1 . 84 to enter and f set to 0 . 71 to remove variables , mahalanobis distance and prior probabilities computed based on group size . table 19 shows the overall accuracy rate at 90 . 6 % with the range from 83 % to 98 %. variable set 7 includes nine variables , of which four ( nom , pred , comp , mod ) are syntactic ( related to phrasal type ); four ( eoc , eop , eom , emph ) are graphemic ( related to punctuation ); and one ( avgwl ) is stylometric ( average word length ). the variable counts were extracted for each sentence for each author , and then summed for each document for each author . logistic regression analysis of the data was performed using spss . the statistical model for each author pair was built using document level data for the authors , but without cross - validation . the overall accuracy rate was 96 %, with the overall results being in line with cross - validated results ( although it is not standard procedure to use such small datasets without cross - validation ). as has been demonstrated , embodiments of the present invention deal with syntactic structures , an aspect of language that is not unique to any one language , but which is cross - linguistic , and thus , the parameters are applicable regardless of the language being used . thus , embodiments of the present invention can be modified to provide authorship attribution in numerous languages , for example only , and not intended as a limitation , arabic , farsi , fulani , spanish or turkish . further , since embodiments of the present invention require far less textual data than other authorship attribution methods ( as few as 100 sentences from any number of texts ) authorship attribution using embodiments of the present invention can proceed rapidly , such that a security or investigative agency could work with 100 sentences , rather than having to delay until a large quantity of text ( such as 100 texts ) are obtained . thus , embodiments of the present invention can aid in identifying a terrorist who , for example , has prepared a threatening statement , or who was overheard speaking by means of electronic surveillance and a transcript of the conversation generated . therefore , although this invention has been described with a certain degree of particularity , it is to be understood that the present disclosure has been made only by way of illustration , and that numerous changes in the details of construction and arrangement of components may be resorted to without departing from the spirit and scope of the invention . baayen , h ., van halteran , h ., neijt , a ., tweedie , f . 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