Multi-dimensional approach to register variation

variation

Register analysis has profited immensely from the developments in compu- tational linguistics in the last few decades. It has also allowed researchers to gather, process, and analyze a greater number of genuine texts than in the past. One of the most prominent work, diachronic as well as synchronic, in register analysis of large numbers of texts is the research of Douglas Biber and his collaborators (e.g., Biber 1988, 1993b, 1995, 2006a,d; Biber & Conrad 2009; Biber & Finegan 1989, 1992, 1994). He proposes a com- prehensive analytical framework for quantitatively analyzing register and register variation. Based on Halliday’s definition of register (1978: 31; cf. p. 20), Biber argues that such analytical framework should provide tools for the identification, quantification and classification of the three typical components of register analysis: the situational, and the linguistics charac- teristics of register, and the functional associations between these two. Ac- cording to Biber (1994: 35), such analysis are inevitably quantitative since “register distinctions are based on differences in the relative distribution of linguistic features, which in turn reflect differences in their communica- tive purposes and situations”. As Halliday (1988: 162) points out, register can also be defined as “a cluster of associated features having a greater- than-random [. . . ] tendency to occur”. Based on the notion of linguistic co-occurrence Biber develops a multi-dimensional approach to register vari- ation, by which different patterns of co-occurrence of linguistic features are analyzed as underlying dimensions of functional variation. One of the ma- jor distinguishing aspect of Biber’s framework is that it considers “register variation as continuous rather than discrete” (Biber 1994: 36; emphasis added). Hence, the focus of his multi-dimensional approach is on the rela- tive distribution of common linguistic features, i.e., co-occurrence patterns of register markers, flowing across register variation. In the preliminary steps in the development of this approach, he identified 67 linguistic fea- tures6, i.e., register markers (e.g., lexical classes, grammatical categories, syntactic constructions), that may have a functional association in texts, through the quantitative analysis of these features over a large number of naturally occurring texts. These register markers were then organized into 16 major grammatical and functional categories, e.g., nominal forms, (e.g., nominalizations, gerunds and total other nouns), pronouns and pro-verbs, passives, lexical specificity (e.g., type/token ratio and mean word length), modals, etc. The co-occurrence patterns of these grammatical and func-

6_{A complete list of all register markers used in the multi-dimensional approach can}

2.4. Register analysis

tional categories were then grouped into seven factors. This has been done using a statistical technique known as factor analysis. Finally, these factors were interpreted as seven dimensions of variation used for register compar- ison. Thus, the first of the seven dimensions, which represent a continuum along which registers may differ, is called Involved vs Informational Pro- duction, by which high frequencies of occurrence of first- and second-person pronouns, wh-questions, amplifiers are interpreted as an indication of inter- personal interaction, i.e., a higher involved text production. Contrastively, high frequencies of nouns, prepositional phrases, type/token ratio, and at- tributive adjectives indicate a more informational focus in the text produc- tion. The second dimension in this approach is Narrative vs Non-narrative Discourse. Linguistic features contributing to the positive characterization of narrative registers, e.g., fiction prose, are past tense verbs, third-person pronouns, synthetic negation, and present participial clauses, among others. Non-narrative registers, such as academic discourse and news, have lower frequency of occurrence of such linguistic features. The third dimension is called Elaborated vs Situation-dependent Reference. Linguistic features, contributing to a more elaborated discourse, are, for instance, phrasal co- ordination, nominalizations, wh-relative clauses, which are highly frequent in, e.g., academic discourse. Time and place adverbials and adverbs in news registers are features with high frequency of occurence that indicate a more situation-dependent register. Overt Expression of Persuasion / Ar- gumentation is the name of the fourth dimension. Features contributing to a higher expression of persuasion / argumentation are modals, suasive verbs and infinitives, among others. These occur highly in registers such as professional letters and editorials. Contrastively, news registers are not overtly argumentative, showing lower frequency or even absence of these features. The fifth dimension is Abstract vs Non-abstract Style. Similarly to dimensions 2 and 4, it has only positive loadings, e.g., conjuncts, pas- sives, adverbial subordinators, etc. While academic discourse and official documents show a high frequency of these features, conversation and fic- tion show practically the absence of them. This confirms the expectation for academic discourse being much more abstract than other registers. The last two dimensions, dimension 6, On-line Informational Elaboration Mark- ing Stance, and dimension 7, Academic Hedging, are the most difficult ones to interpret (Conrad & Biber 2001: 39). These have few features with important loadings, and have been less used in register analysis research. Particullarly the interpretation of dimension 7 still needs to be rectified by further research. Typical features contributing positively for dimension 6 are that -complement and -relative clauses, whereas down-toners, adverbs, and attributive adjectives are important for dimension 7.

2.4. Register analysis

Biber’s multi-dimensional approach for register analysis is, therefore, a comparative perspective, where patterns of register variation are quantita- tively investigated. Moreover, it is not rooted in any specific theoretical framework. When a large quantitative unstructured feature set is statis- tically processed, it will allow English teachers to produce better learning materials.

In the field of ESP - English for Specific Purposes - researchers and practitioners seek to understand the linguistic characteristics of spe- cialized registers in English. One major goal of such research is to design the best possible materials and activities to help students comprehend and produce these registers appropriately.

(Biber et al. 1998: 157)

This approach requires no hypothesis formulation prior to the experi- ments and provides a substantial overview over register variation. One of the many advantages of this approach is precisely the fact that no hypoth- esis is required prior to the quantitative investigation of linguistic features because it allows linguists to gain insights into the variation of many dif- ferent registers at once. However, this approach has been criticized for relying strongly on statistical techniques, which are themselves not fault- less, as well as for its lack of rooting in a broader linguistic theory, e.g., one which considers language entirely as a social-semiotic system. Systemic Functional Linguistics is a linguistic theory that is used as the theoretical underpinnings of this research, and is discussed in the next section, Section