[pre-print] The Multidimensional Analysis Tagger
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Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicThe Multidimensional Analysis TaggerAndrea NiniUniversity of ManchesterAbstractThis chapter introduces and describes the Multidimensional Analysis Tagger (MAT), acomputer program for the analysis of corpora or single texts using the multi-dimensionalmodel proposed by Biber (1988). The program uses the Stanford Tagger to generate an initialtagged version of the input, which is then used to find and count the original linguisticfeatures used in Biber (1988). The program then plots the text or corpus on to Biber’s (1988)dimensions and assigns it a text type as proposed by Biber (1989). Finally, MAT offers a toolto visualize the features of each dimension in the text. The software was tested for reliabilityby comparing the dimension scores produced by MAT for the LOB corpus against the onesobtained by Biber (1988) in his original analysis. This test shows that MAT can largelyreplicate Biber’s results. The software was also tested on the Brown corpus and the results notonly confirm the reliability of MAT in calculating the dimension scores, but also suggest thatBiber’s (1988) dimensions and text types can be generalized and applied to other data sets. Asa further example of a MAT analysis, a study of a corpus of threatening and abusive letters isreported. Although this corpus did not contain the balanced sample of registers required toperform a new multi-dimensional analysis, MAT allowed a text type analysis of the corpus tobe performed through a comparison with Biber’s (1988; 1989) model of English registervariation.1. IntroductionAbout thirty years ago, Biber's (1988) Variation across Speech and Writing revolutionizedour understanding of registers by introducing factor analysis for the extraction of latent1
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicdimensions of variation from patterns of co-occurrence of linguistic features, a methodologylater called multi-dimensional analysis. The use of this new methodology also led to asounder understanding of the most important linguistic and extra-linguistic factors thatinfluence register variation in English. Multi-dimensional analysis was adopted in a largenumber of other studies on the language used in various registers from academic language (e.g. Biber, 2003; Gray, 2013) to the most recent web registers (Grieve, Biber, and Friginal2011; Titak and Roberson 2013; Biber and Egbert 2016). The flexibility of multi-dimensionalanalysis for linguistic research is also demonstrated by its various other applications, such asthe study of author styles (Biber and Finegan 1994) , sociolects (Biber and Burges 2000) ,regional variation (Grieve 2014), or diachronic register variation (Biber and Finegan 1989).Beside the value of multi-dimensional analysis itself, Biber (1988) has also uncoveredsome very valuable insight on the patterns of variation across the registers of the Englishlanguage. By extracting the underlying dimensions of variation for a corpus balanced forregisters, Biber (1988) was able to propose a set of six dimensions that can account for thelinguistic variation in the most important registers of the English language. These sixdimensions represent patterns of co-variation of linguistic features and were functionallyinterpreted according to their constituting features and the registers that they characterized.These original six dimensions are summarized in Table 3.1.Table 3.1: Short descriptions and summary of the six dimensions of register variation forEnglish found by Biber (1988).2
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicDescriptionDimension 1Low scores on this DimensionInvolved production features:Involved vs. Informational Discourseindicate informationally denseprivate verbs, that-deletions,discourse, as in the case ofcontractions, present tenses,academic prose, whereas highsecond person pronouns, do asscores indicate that the text ispro-verb, analytic negations,affective and interactional, asdemonstrative pronouns,for conversations.emphatics, first person pronouns,pronoun it, be as main verb,causative subordinations,discourse particles, indefinitepronouns, hedges, amplifiers,sentence relatives, wh- questions,possibility modals, non-phrasalcoordinations, wh- clauses,stranded prepositions.Informational productionfeatures: nouns, average wordlength, prepositions, type/tokenratio, attributive adjectivesDimension 2The higher the score on thisNarrative concerns features: pastNarrative vs. Non-Narrative ConcernsDimension the higher thetenses, third person pronouns,narrative concern, as in theperfect aspects, public verbs,case of works of fiction.synthetic negations, presentparticipial clauses3
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicDimension 3Low scores on this DimensionContext-dependent discourseContext-Independent Discourse vs.indicate dependence on thefeatures: time adverbials, placeContext-Dependent Discoursecontext as in the case of a sportadverbials, general adverbs.broadcast, whereas high scoresContext-independent discourseindicate independence fromfeatures: wh- relative clauses oncontext, as for example inobject position, pied-pipingacademic prose.relatives, wh- relative clauses onsubject position, phrasalcoordinations, nominalizationsDimension 4The higher the score on thisOvert expression of persuasionOvert Expression of PersuasionDimension indicate the morefeatures: infinitives, predictionthe text explicitly marks themodals, suasive verbs, conditionalauthor’s point of view as wellsubordinations, necessity modals,as their assessment ofsplit auxiliarieslikelihood and/or certainty, asfor example in professionalletters.Dimension 5The higher the score on thisAbstract information features:Abstract vs. Non-AbstractDimension the higher theconjuncts, agentless passives, pastInformationdegree of technical and abstractparticipial clauses, by passives,information, as for example inpast participial WHIZ deletionscientific discourse.relatives, other adverbialsubordinatorsDimension 6High scores on this DimensionOn-line informationalOn-Line Informational Elaborationindicate that the informationelaboration features: that clausesexpressed is produced underas verb complements,certain time constraints, as fordemonstratives, that relativeexample in speeches.clauses on object position, thatclauses as adjective complements4
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicThe value of these dimensions transcends their role in the English language as subsequentresearch has also demonstrated a striking cross-linguistic validity for Dimension 1 andDimension 2 across several languages from different families (Biber 1995; Biber 2014).In addition to the discovery of the six dimensions, Biber (1989) later introduced theuse of cluster analysis to find out the characteristic text types of the English language, that is,clusters of texts that are linguistically similar in terms of the six dimensions. Using clusteranalysis, this study found out that the same corpus used in Biber (1988) could be divided intoeight text types, a summary of which is presented in Table 3.2.Table 3.2: Short description and summary of the eight text types for English found by Biber(1989).Text typeCharacterizing registersDimension profileDescriptionIntimate interpersonaltelephone conversationshigh score on D1, lowText type that usuallyinteractionbetween personal friendsscore on D3, low scoreincludes interactions thaton D5, unmarked scoreshave an interpersonalfor the other Dimensionsconcern between closeacquaintancesInformational interactionScientific expositionface-to-face interactions,high score on D1, lowText type that usuallytelephone conversations,score on D3, low scoreincludes personal spokenspontaneous speeches,on D5, unmarked scoresinteractions focused onpersonal lettersfor the other Dimensionsinformational concernsacademic prose, officiallow score on D1, highText type that usuallydocumentsscore on D3, high scoreincludes informationalon D5, unmarked scoresexpositions focused onfor the other Dimensionsconveying technical5
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicinformationLearned expositionImaginative narrativeofficial documents, presslow score on D1, highText type that usuallyreviews, academic prosescore on D3, high scoreincludes informationalon D5, unmarked scoresexpositions focused onfor the other Dimensionsconveying informationromance fiction, generalhigh score on D2, lowText type that usuallyfiction, preparedscore on D3,includes texts with anspeechesunmarked scores for theextreme narrativeother DimensionsconcernGeneral narrativepress reportage, presslow score on D1, highText type that usuallyExpositioneditorials, biographies,score on D2,includes texts that usenon-sports broadcasts,unmarked scores for thenarration to conveyscience fictionother Dimensionsinformationsport broadcastslow score on D3, lowText type that usuallyscore on D4, unmarkedincludes on-linescores for the othercommentaries of eventsDimensionsthat are in progressspontaneous speeches,high score on D4,Text type that usuallyprofessional letters,unmarked scores for theincludes persuasiveinterviewsother Dimensionsand/or argumentativeSituated reportageInvolved persuasiondiscourseBesides the pioneering of factor analysis and cluster analysis for the analysis ofregisters, another achievement of the findings of the two studies above is the elaboration of amodel of register variation for the English language that is predictive. Using the results of themulti-dimensional analysis it is possible to determine how a text, corpus, or even registerbehaves linguistically in comparison to other registers of English. In essence, the multidimensional model represents a base-rate knowledge of English that allows the description or6
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicevaluation of other texts or registers.Despite this potential, the majority of the research on the multi-dimensional analysisof register variation has focused on using factor analysis and cluster analysis on new data sets.Relatively speaking, few studies have used previous multi-dimensional models or the originalmodel itself to describe or evaluate new data. Among these studies, the original multidimensional model has been used especially to study the registers of television programs(Quaglio, 2009; Al-Surmi 2012; Berber Sardinha 2014; Berber Sardinha and Veirano Pinto2017) and written or spoken academic registers (Conrad 1996; Conrad, 2001; Biber et al.,2002) .These studies are evidence that the model can be useful in many applications thatinvolve the comparison of new data to a base-rate knowledge of English registers. Forexample, the evaluation of similarity of a particular academic text written by a learner ofEnglish to the norm for academic registers of English is such an application. Similarly,register variation researchers can use the same model to compare a register to the otherregisters of English considered in the 1988/1989 model. As opposed to finding newdimensions, which is an endeavor that brings insight in the internal structure of registers,contrasting a data set to a general model of English can be another way to bring to light itsregister identity.The application of Biber's original dimensions and text types can also be useful forthose interested in looking at variation within small or unstructured corpora. The first multidimensional analysis was successful in producing a model that describes English registersbecause the corpus was carefully sampled by registers and large enough to carry out astatistical analysis. These two pre-requisites are essential to obtain dimensions that canadequately capture register variation. However, depending on the data that one wants toanalyze, it might turn out to be impossible to collect a large enough corpus or one that is7
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicinternally stratified enough to produce meaningful register dimensions. In such cases, plottingthe input corpus onto Biber's model of English can be a reasonable approximation to runninga new multi-dimensional analysis. Instead of extracting new dimensions for the register, onecan assess how this new register is different or similar to other registers of the Englishlanguage and in doing so finding out its register identity.The application of Biber's model is however dependent firstly on an empiricalvalidation of its generalization to new texts and secondly on the development of a tool thatcan easily allow other researchers to find the location of a new data set in the English multidimensional space. The present chapter presents research that assesses both points. Firstly, thechapter introduces the Multidimensional Analysis Tagger (or MAT, freely accessible gger/), a computer program that facilitates theprocess of applying the original 1988 model to a new data set. After describing its architectureand validation process, an analysis of the Brown corpus using MAT will be reported todescribe to what extent the model can be applied to new texts. Finally, the chapter concludeswith a demonstration of the applications of MAT for register analysis.2. The MAT2. 1 The architecture of MATMAT is a computer program that replicates Biber's (1988) tagger, calculates thedimension scores for each of the dimensions, and then plots the input data onto the multidimensional space while also assigning each text to one of the eight text types identified byBiber (1989). This whole process is achievable due to the detailed descriptions of the taggingrules for the linguistic features presented in the appendix of Biber (1988).After the user has provided an input, MAT returns a tagged version of it using the same67 linguistic features of Biber (1988). However, MAT does not use the original Biber tagger,8
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicwhich is not publicly available, and instead uses the Stanford Tagger (Toutanova et al. 2003) for the preliminary tagging of basic parts of speech, such as nouns, adjectives, verbs, oradverbs, followed by Biber's (1988) rules for more complex features, such as sentencerelatives, that as a demonstrative as opposed to a complementizer, and so forth. Although theoriginal Biber tagger prompted the user with ambiguous cases for certain complex features,MAT does not implement any manual intervention from the user. However, manualintervention on the tagged texts can be performed by a user, if he/she wishes, before thestatistical analysis takes place.Although the tagging rules used by MAT are the same as the original Biber tagger,since the tagging of basic parts of speech is performed by the Stanford Tagger, the taggedfiles returned by MAT are bound to contain some inconsistencies with the original tagger.While some differences are unavoidable, basic parts of speech attribution generally does notvary greatly across taggers, and thus results should be compatible to the 1988 results. Indeed,the reliability of MAT has been tested and the results are reported in the next section below.After the input has been tagged, in order to calculate the dimension scores, firstly theoccurrences of a feature are counted (with the exception of average word length andtype/token ratio), and then their relative frequency per hundred words is calculated. Finally,the standardized scores, or z-scores, for each feature are calculated using the standard formulareported below, where x is the relative frequency of a feature in the user’s input, zx is theresulting z-score of the feature in consideration, µB is the mean frequency for that feature inBiber’s (1988) corpus, and σB is the standard deviation of that feature in Biber’s (1988)corpus:𝑧" 𝑥 𝜇'𝜎'MAT applies these formulas and outputs two files, one with the frequencies (per hundredwords) and one with the z-scores. As described in Biber (1988), the final dimension scores for9
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academiceach dimension are calculated by summing or subtracting the z-scores of the dimensionfeatures following the features' polarities within a dimension. For example, for Dimension 1,the final score is calculated with the formula:𝐷1 𝑧,-./012/2-34 𝑧160172821.9:4 𝑧;9:1-0;1.9:4 𝑧:9 :4 𝑧?9-782:@16 𝑧,-2,94.1.9:4 In the calculation of dimension scores MAT implements a slight alteration as it includes ineach formula only those variables with a mean higher than 1 in Biber (1988: 77). This changehas been implemented as the features with a mean lower than 1 are rare features of English―the frequency of which can be highly dependent on sample size. For this reason, if by chancealone one of these rare features is even slightly more common in the user's input than in theoriginal corpus, then the z-scores of these features would be abnormal and thus inflate thedimension scores. The loss of this information does not greatly influence the dimension scoresas, given their rarity, these features contribute very little to the dimensions. In addition to thischange, MAT also offers the possibility to apply a z-score correction, that is, a reduction ofall the z-scores of magnitude higher than 5 to 5, in order to avoid unlikely inflated z-scoresand dimension scores.Finally, MAT plots the input data in the multi-dimensional space and assigns a texttype to each input text. A graph similar to the graphs displayed in Biber (1988: 172) usingmeans and ranges is produced for the dimensions selected by the user. Using this graph, theuser can compare their data against a selection of registers. The program will also print outwhich register is the most similar to the input data. Another plot is also produced for the texttypes mirroring Biber's (1989) visualisation. This plot displays the dimensions horizontallyand the location of each text type as well as the input data on each dimension vertically. Inthis way, the user can compare their data to the other text types and assess which text type isthe most similar to the input. The most similar text type is assigned using Euclidean distance10
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicfrom the centroids of the clusters reported in Biber (1989).After carrying out the analysis, the user can also use MAT to visualize of one or moredimension features in the text. MAT can produce a color-coded file with the selecteddimension features, allowing for the qualitative exploration and interpretation of suchfeatures.Figure X: Screenshot of MAT interface for Windows.Figure X shows the interface of MAT for Windows. The third button from the left,Tag and Analyze, will take as input one text or a folder of texts, tag it, and then return theinput’s location in the multi-dimensional model. The two processes can be done in twoseparate steps, for example if the user wants to manually check the quality of the tagging, byusing the first button Tag and then the second button Analyze. Finally, the final button is theInspect functionality to visualize the dimension features. More information about how to useMAT can be found in the manual, which is also freely downloadable gger/.Although great differences are not expected between MAT and the 1988 Biber tagger,the question of whether and to what extent MAT does indeed replicate the original analysiscan only be tested if the same data set is analyzed and similar results are found. The11
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academicdescription of such analysis is reported in the section below.2.2 Testing the reliability of MATIn order to test whether MAT is reliable a MAT analysis of the original data set used by Biber(1988; 1989) was carried out. The original data set consisted of the LOB corpus (Johansson,Leech, and Goodluck 1978) for the published written material, the London-Lund corpus(Svartvik 1990) for the spoken data, and a small corpus of personal and professional letterscollected by Biber.Despite some efforts, only the LOB corpus could be retrieved, of which only thirteenout of its fifteen registers were found: Press Reportage, Press Editorial, Press Reviews,Religion, Hobbies, Popular Lore, Academic Prose, General Fiction, Mystery Fiction, ScienceFiction, Adventure Fiction, Romantic Fiction, and Humour. The test was therefore carried outon this data set.After running MAT on the thirteen available registers of LOB some differences wereobserved, but overall Biber's analyses were successfully replicated. The results of the analysisare displayed in Table 3.3, where the first column identifies a register and the followingcolumns list the dimension scores obtained by Biber and the ones returned by MAT. The lastcolumn lists and contrasts the distribution of text types for the register using percentages,from the most common to the least common.Table 3.3: Comparison of dimension scores and distribution of text types between Biber's(1988; 1989) analysis of the LOB corpus and a MAT analysis of the same corpus.RegistersPress 72Text types59% Generalnarrativeexposition; 39%Learnedexposition; 2%12
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicPress reportageBiber 110.320.080.18Press editorialsMAT-8.4-0.284.383.31.50.33Press editorialsBiber 1.510.822.540.01-0.35Press reviews MATPress reviewsBiber (1988)DifferenceReligionMATReligionBiber (1988)DifferenceHobbiesMATInvolvedpersuasion;2% Scientificexposition73% Generalnarrativeexposition; 25%Learnedexposition; 2%Scientificexposition81% Generalnarrativeexposition; 7%Involvedpersuasion; 7%Scientificexposition;4% Learnedexposition86% Generalnarrativeexposition; 11%Involvedpersuasion; 4%Learnedexposition53% Generalnarrativeexposition; 47%Learnedexposition47% Learnedexposition; 47%Generalnarrativeexposition; 6%Scientificexposition65% Generalnarrativeexposition; 29%Involvedpersuasion; 6%Scientificexposition59% Generalnarrativeexposition; 18%Involvedpersuasion; 18%Learnedexposition;6% Imaginativenarrative34% Generalnarrativeexposition; 24%Learnedexposition; 24%13
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicHobbiesBiber 5Popular loreMAT-9.580.313.42-0.611.4-0.64Popular loreBiber 0.311.30.16Academic proseMAT-12.16-2.165.38-0.025.140.23Academic proseBiber 80.480.360.27General lvedpersuasion;18% Scientificexposition43% Generalnarrativeexposition; 21%Learnedexposition; 21%Involvedpersuasion;7% Scientificexposition; 7%Situatedreportage36% Learnedexposition; 32%Generalnarrativeexposition; 20%Involvedpersuasion;2% Imaginativenarrative; 9%Scientificexposition36% Learnedexposition; 36%Involvedpersuasion; 21%Generalnarrativeexposition;7% Imaginativenarrative56% Scientificexposition; 24%Learnedexposition; 14%Generalnarrativeexposition;6% Involvedpersuasion44% Scientificexposition; 31%Learnedexposition; 17%Generalnarrativeexposition;9% Involvedpersuasion55% Imaginativenarrative; 31%Generalnarrativeexposition; 10%Involvedpersuasion;3% Learned14
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicGeneral fictionBiber 0.892.050.85Mystery fictionMAT0.825.76-0.71.55-0.69-1.13Mystery fictionBiber 252.110.77Science fictionMAT-5.016.11.080.21-0.54-0.54Science fictionBiber 0.911.961.06Adventure fictionMAT-0.855.89-1.290.19-0.97-1.29Adventure fictionBiber 391.530.61Romantic fictionMAT3.556.71-0.882.35-1.26-1Romantic fictionBiber (1988)4.37.2-4.11.8-3.1-1.2exposition51% Imaginativenarrative; 41%Generalnarrativeexposition; 3%Informationalinteraction;3% Involvedpersuasion67% Imaginativenarrative; 29%Generalnarrativeexposition; 4%Involvedpersuasion70% Imaginativenarrative; 23%Generalnarrativeexposition; 8%Situatedreportage83% Generalnarrativeexposition; 17%Imaginativenarrative50% Generalnarrativeexposition; 33%Imaginativenarrative; 17%Situatedreportage69% Imaginativenarrative; 24%Generalnarrativeexposition; 3%Involvedpersuasion;3% Learnedexposition70% Imaginativenarrative; 31%Generalnarrativeexposition79% Imaginativenarrative; 17%Generalnarrativeexposition; 3%Involvedpersuasion92% Imaginativenarrative; 8%15
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury iber 20.731.050.94Mean differences1.240.512.270.721.240.51HumourMAT78% Generalnarrativeexposition; 11%Imaginativenarrative; 11%Involvedpersuasion89% Generalnarrativeexposition; 11%InvolvedpersuasionIn Dimension 1, Involved versus Informational Discourse, the most important of thedimensions in terms of variance explained and universality (Biber 1995), score differencesrange from 0.28 for Popular Lore to 2.74 for Religion (Mean: 1.24). These differences of theorder or one or two points do not affect the identification of the correct location of a newinput, as Dimension 1 scores in Biber's study range from roughly -20 to 50.In Dimension 2, Narrative versus Non-Narrative Concerns, again large differences arenot detected, with a range from 0.2 for Science Fiction to 0.87 for Religion (Mean: 0.51). Forthis dimension, as for all the other dimensions except for the first one, the range of scorespresented in Biber (1988) roughly ranges from -5 to 5, with a positive score indicating that atext or register is narrative. Besides the small differences, Table 3 highlights that for all theregisters except two, the sign of the scores is the same as in the original study.Contrary to the two dimensions above, the results for Dimension 3, ContextIndependent Discourse versus Context-Dependent Discourse, are not as accurate, withdifferences ranging from 0.99 for Religion to 3.22 for Romantic Fiction (Mean: 2.27). Suchdifferences are much higher in magnitude than the ones previously observed as the range of16
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), MultiDimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury AcademicDimension 3 from Biber (1988) spans from -5 to 5. An average difference of more than 2points can affect the reliable identification of the location of a text in this space.As opposed to Dimension 3, the scores for the remaining dimensions are again notlargely different from Biber's, ranging, respectively: from 0.19 to 2.25 for Dimension 4, OvertExpression of Persuasion (Mean: 0.72); from 0.08 to 2.11 for Dimension 5, Abstract versusNon-Abstract Information (Mean: 1.24), and from 0.01 to 1.06 for Dimension 6, On-LineInformational Elaboration (Mean: 0.51). Although it could be argued that some differences ofthe order of magnitude of 2 could be problematic, these are extreme values, as the moremodest mean differences reveal.In terms of the d
Nini, A. (2019). The Multi-Dimensional Analysis Tagger. In Berber Sardinha, T. & Veirano Pinto M. (eds), Multi-Dimensional Analysis: Research Methods and Current Issues, 67-94, London; New York: Bloomsbury Academic 2 dimensions of variation from patterns of co-occurrence of linguistic features, a methodology
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Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. 3 Crawford M., Marsh D. The driving force : food in human evolution and the future.
