## Popular vs. Professional Aspects of Economics Texts in English

## Abstract

This article takes up the important issue of distinguishing popular from professional texts using economic texts as the object of analysis. It reports on a limited corpus study of authentic texts produced by native English professionals and published for native English readers, but differing in the nature of intended audience (lay/expert). Economics/electronic money transactions constituted the textual domain/subdomain, and the dominant text type was expository. Following a basic quantitative check and a tentative readability ranking the text samples were scrutinized with respect to lexical profile,  frequency  band  penetration,  terminological  density  and  uniqueness.  A  detailed collocation study was then made of focal multi-word terms as well as personal pronouns. Standard corpus techniques were employed in exposing regularities and producing supporting documentation. At the same time possible reflections of communicative  situation,  pragmatic  purpose  and  semiotic  significance  were  noted.  An  attempt was then made to integrate such internal ('linguistic') and external ('situational') variables as revealed by a comprehensive textual analysis in a synopsis intended to bring out  a  possible  clustering  of  features.  The  general  question  to  which  an  answer  was sought was to what extent is each perspective necessary and/or sufficient in defining the textual genre?

## 1. Introduction

In terms of the conventional (Bühler-type) 'sender-content-recipient' triangle  languages  for  special  purposes  (LSP)  gravitate  towards  the content corner in being chiefly concerned with 'Darstellung' of factual information. The resulting texts tend to fall into a definable set of textual genres appropriate to the domain and the ultimate textual function. However, the nature of the intended recipient will obviously influence the style of representing that factual information; thus in the analysis  both  text-linguistic,  pragmatic  and  semiotic  features  can  be seen to reflect whether the text is pitched at a popular or professional audience.

* Magnar Brekke Norges Handelshøyskole

LSP comprehension has been shown to benefit considerably from readers' genre knowledge, i.e. from awareness of the repertory of conventional  text  forms  and  their  functions  in  the  domain  community. Without opening up the Pandora's box of genre definitions one might ask whether such independent genre knowledge is necessary or at least strongly conducive to LSP comprehension, or whether the observable text-linguistic, pragmatic and semiotic features of a given text provide sufficient  basis  for  readers  to  work  out  the  message  and  indeed  the genre for themselves.

The present paper reports an attempt to put this question to an empirical test. The preliminary conclusions are that while superficial statistical measures constitute helpful clues neither internal nor external criteria are in themselves sufficient. Nevertheless their clustering contributes substantially to an empirical basis for useful classification of professional  genres.  It  is  argued  that  such  preliminary  results  have implications  for  corpus  building,  corpus  studies,  translator  training and LSP teaching programs in general.

## 2. Presentation of project

## 2.1.  Material

A  pilot  study  was  undertaken  of  five  authentic  texts  produced  by native  English  writers  and  published  for  native  English  readers,  but differing  in  the  nature  of  intended  audience  (lay  or  expert).  All  five texts fall within the domain of Economics and more specifically under the  subdomain  of  'electronic  money  transactions',  as  determined through a cursory reading. The dominant text type is expository, and the length of each text varies from 874 to 3776 words, a total of 8.834 words, the average text being approx. 1600 words long. The texts are complete texts rather than fragments, but for reasons not relevant to this paper one text has been shortened (by deleting whole subsections) to about 1/3 of the original. Four of the texts were available on paper in  their  published  form,  one  was  available  in  electronic  form  only. Two of the texts have been taken from professional journals, one from a newspaper, one from a popular science magazine, and the electronic one has been downloaded over the WWW.

## 2.2. Method

The text samples were scrutinized by the use of a standard concordance package (System Quirk). The general statistical picture emerging is  summarized  in  table  1  below  and  then  further  elaborated.  At  the same time possible reflections of communicative situation, pragmatic purpose and semiotic significance were noted. Standard corpus techniques were employed in exposing regularities and producing supporting documentation.

## 2.3. Goal

The general question to which an answer was sought was: In view of the range of internal (primarily statistical and text-linguistic) and external  (pragmatic  and  semiotic)  variables  revealed  by  a  comprehensive textual analysis, to what extent is the respective type of criteria necessary and/or sufficient in defining the textual genre? More specifically: Given five short texts from the same subdomain representing the same general text type, is it possible to work 'bottom-up' through textual/linguistic features to arrive at a clear generic typology as regards the type of audience which the author(s) may have had in mind, or are we dependent on 'top-down' processing taking published form into account? The crucial test will be the electronic text, since it lacks the institutionalized publication form which normally answers such a question. Can we  identify  a  text's  genre  'in  the  nude',  so  to  speak,  unassisted  by clues from the dress it is wearing?

## 2.4. Overview of paper

After  a  brief  survey  of  basic  text  statistical  measures  and  an  ad  hoc readability  ranking  based  on  them  the  paper  examines  two  lexical aspects of the text samples. One concerns the penetration of high frequency  lexical  words  (appearing  in  all  five  texts)  into  the  higher frequency bands normally reserved for function words, while the other picks up lexical words at the lower end of the frequency list which are either unique to one text or have a clear 'tell-tale' function in (partially)  revealing  textual  identities.  It  then  focuses  on  important  collocational patterns found with selected terms and with specific pronouns. Supplementary elements from the pragmatic and semiotic dimensions are pointed out, and a synoptic view of text profiles and feature clustering is provided. This leads directly on to a matching of each feature cluster with its specific text, the identity of which has been kept from the reader up to this point. The relative success of this undertaking is claimed to have implications for corpus-related LSP research, teaching, and practice.

## 3. Findings: Internal variables

## 3.1. Texts statistics

Table 1 exhibits some important measures. Since the text samples are very small I make no claims on statistical significance or strict validity for these numbers, but they serve as a fairly concrete point of departure for uncovering aspects and dimensions which are worth pursuing.

Table 1: Basic measures of text samples

|    |   1: WRD |   2: CHAR |   3: AWL |   4: WPS |   5: type/token |
|----|----------|-----------|----------|----------|-----------------|
| A  |     1625 |      8661 |     5.39 |     21.1 |             .45 |
| B  |     1646 |      9146 |     5.60 |     25.9 |             .45 |
| C  |     3776 |     19962 |     5.28 |     24.9 |             .45 |
| D  |      913 |      4508 |     4.93 |     22.3 |             .46 |
| E  |      874 |      4587 |     5.24 |     24.3 |             .48 |

The texts are randomly assigned letters A through E (left-hand margin of table 1). Columns 1 and 2 give word and character count, respectively,  and  columns  3  and  4  the  average  length  of  words  and  sentences. Text B has the longest words (5.60 characters), text D the shortest (4.93 characters).  While  differences  in  sentence  length  are  relatively  small, text B also has the longest sentences (25.9 words) while text A has the shortest (21.1 words). The type/token ratios in column 5, calculated for the first 850 words of each text (cf. Biber 1988: 239) reveal no significant differences among the text samples except for E, which has less lexical repetition than the others.

At least two of the three quick and superficial measures exhibited in table  1  point  to  relative  differences  holding  among  our  five  samples which can be brought together under a composite rank number: assuming that a text characterized by short words, short sentences and a high degree of lexical repetition (i.e. redundancy) is easier to read than one with  longer  words  and  sentences  with  lower  degree  of  repetition,  I have allowed myself to assign 'readability points' from 1 to 5 according to their rank in these respects (see table 2). Thus on a scale from 3 to 13 text D would appear easiest, B most difficult to read.

Table 2: Readability ranking

|     |   1: WPS |   2: AWL |   3: type/token |   Composite |
|-----|----------|----------|-----------------|-------------|
| A 1 |        4 |        3 |                 |           8 |
| B   |        5 |        5 |               3 |          13 |
| C   |        4 |        3 |               3 |          10 |
| D 2 |          |        1 |               2 |           5 |
| E   |        3 |        2 |               1 |           6 |

## 3.2. Lexical aspects

Table 3 gives frequency ranks 1-10 (lefthand margin) of lexical words occurring  most  frequently  in  the  five  texts  (A-E  horizontally).  The juxtaposition of these frequency lists creates a clear impression of subdomain relatedness as far as shared lexical content (boldface items) is concerned, although the degree of relatedness varies.

Table 3: Top ten lexical word frequency tables

<!-- image -->

|   Rank | A            | B           | C           | D           | E           |
|--------|--------------|-------------|-------------|-------------|-------------|
|      1 | bank         | financial   | electronic  | card        | cash        |
|      2 | card         | service     | card        | Mr.         | electronic  |
|      3 | signature    | software    | issue-      | electronic  | card        |
|      4 | key          | customer    | money       | security    | smart       |
|      5 | information  | bank        | cash        | transaction | system      |
|      6 | digital      | industry    | bank        | business    | transaction |
|      7 | record       | institution | value       | product     | information |
|      8 | organisation | on-line     | transaction | world       | bank        |
|      9 | electronic   | value       | policy      | years       | marketing   |
|     10 | privacy      | player      | scheme      | currency    | money       |

Taking the full spread of all words, lexical as well as function words, and then identifying the frequency bands where the eight most frequent lexical words turn up within the first percentile (1-100), gives a somewhat different perspective, see table 4.

Table 4: Penetration of top lexical items into general frequency chart

|    | card   | elec- tronic   | bank   | cash   | trans- action   | money   | secur- ity   | infor- mation   |   TOTAL FRQ |
|----|--------|----------------|--------|--------|-----------------|---------|--------------|-----------------|-------------|
| A  | 13/20  | 8/40           | 15/20  | 6/50   | 3/100           |         | 6/50         | 10/20           |          61 |
| B  |        |                | 13/20  |        |                 |         |              | 7/50            |          20 |
| C  | 62/10  | 66/10          | 27/30  | 28/20  | 16/40           | 60/20   | 11/60        |                 |         270 |
| D  | 20/10  | 6/30           |        | 4/50   | 6/30            | 2/80    | 6/30         |                 |          44 |
| E  | 12/10  | 22/10          | 5/30   | 22/10  | 8/20            | 4/40    |              | 5/30            |          78 |

Table 4 is to be understood as follows: the eight most frequent lexical words  are  listed  horizontally  across  the  table.  For  a  given  cell,  the number to the left of the slash (/) gives the frequency of the relevant item in each text (lefthand column), while the number to the right of the slash gives the frequency band (1-10, 11-20, 21-30 and so on) in which the item appears. The lexical unit card thus occurs 13 times in text A and is found in the 11-20 frequency band. It will appear that the first  five  words  ( card,  electronic,  bank,  cash and transaction) occur frequently in four of the texts; the next three words ( money, security, and information )  occur  frequently in three of them. The lexical unit transaction is thus barely within the first percentile in text A but rises to the 31-40 frequency band in text C, the 21-30 band in D, and the 11-20 band in E.

This use of frequency bands constitutes a way of indicating how far a  specific  lexical  word  has  'penetrated'  into  the  higher  frequency bands. Under normal distribution in a very large corpus the first percentile would be more or less 'reserved' for function words. In the 28 million word Longman/Lancaster Corpus, for instance, only the words man and little reach the first percentile; i.e. of the first hundred most frequent words, 98 are function words (Brekke, Myking  and Ahmad 1996). In an LSP corpus, on the other hand, words semantically related to the textual domain (i.e. terms) will be found much higher in the frequency charts.

What we are discussing  here  is  a  form  of  terminological  density, and in order to arrive at an intuitive measure of this I have for each text  calculated  the  percentage  which  these  eight  technical  terms (which  most  texts  share)  constitute  of  the  total  word  mass.  Table  5 repeats  (from  table  1)  the  numbers  for  text  lengths  in  column  1  and

(from table 4) the total frequencies of the eight most frequent lexical words in column 2. Column 3 expresses the percentages that the eight most frequent technical terms (given across top row of table 4) represent out of the total word mass for the respective texts.

Table 5: Terminological density

|    |   1: WRD Text mass |   2: TOT.FRQ 8 most frq. |   3: % 2:1 |
|----|--------------------|--------------------------|------------|
| A  |               1625 |                       61 |       3.75 |
| B  |               1646 |                       20 |       1.21 |
| C  |               3776 |                      270 |       7.15 |
| D  |                913 |                       44 |       4.82 |
| E  |                874 |                       78 |       8.92 |

Not  unexpectedly  (given  table  4)  the  total  occurrence  of  the  eight shared high frequency lexical words in text B makes up only 1.21% of the total word mass, counting all word forms, whereas for text E the corresponding  percentage  is  close  to  9.  We  interpret  this  to  indicate that text B is not strongly focused on the shared domain topic while text  E  is.  On  this  score  text  C  is  also  strongly  focused  on  common subject matter, while A and D are intermediate.

Equally interesting, of course, are the unique occurrences at the tail end of the frequency scale, lexical words occurring in only one of the documents  and/  or  with  very  low  frequency;  see  table  6,  which  in addition  displays  'tell-tale'  items  giving  strong  clues  as  to  textual genre.

Table 6: 'Tell-tale', unique or low frequency items

|    | Lexical words/terms                                                           |
|----|-------------------------------------------------------------------------------|
| A  | digital/blind signature, untraceable, tamperproof, representatives, observers |
| B  | on-line personal financial services, incumbent                                |
| C  | issue, issuer, issued, issues, schemes, central bank                          |
| D  | says, explains, believes, points out, denies, cryptography, algorithm         |
| E  | says, systems integrator, decipher, applets, prototyping, encrypted           |

The 'tell-tale', unique or low frequency items given in table 6 appear to be just as revealing as to the genre identity of the respective text as the  high  frequency items in table 3. Text B in table 6 highlights the well-known limitation of simple frequency analysis, which counts only  single  words,  i.e.  character  strings  separated  by  spaces,  and  thus misses  multi-word  terms  or  expressions,  by  far  the  most  significant lexical  units  in  LSP  texts.  These  can  easily  be  brought  out  by  producing a concordance for the relevant items. Thus the word online occurs in two of the texts under investigation, but only in text B does it form part of the expression online personal financial services. We will now examine some of the characteristic combinations which our selected terminological units enter into, and then look at pronoun occurrences.

## 3.3. Focal terminology

Figure 1 is a graphic integration of concordance information for each word in the phrase online personal financial services occurring uniquely i text B .

Boldface entries co-occur with any listed word bordering on their territories; other entries co-occur with listed words on their corresponding level. It will appear that the boldface expression is fairly rich in its collocational patterns. Some of these undoubtedly qualify for term status, but that is a different undertaking.

Figure 1: Key collocations, text B: online personal financial services

<!-- image -->

The expression displayed in figure 1 was unique to text B. At the opposite extreme we find the phrase electronic bank notes/cash/money , which occur in all texts except B. Figure 2 displays the concordance information  for  this  phrase  while  figure  3  does  the  same  for card/ cards .

Figure 2: Key collocations, texts A & C-E: electronic bank notes

<!-- image -->

| director of Internet-based smartcardbased denominal untraceable   | e- encrypted   | commerce forms cash purse coins value money          | consultancy systems offers transactions products systems chip laundering schemes services account   |
|-------------------------------------------------------------------|----------------|------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
| the first real                                                    | e- encrypted   | bank                                                 | notes branch networks                                                                               |
| to authenticate achieving                                         | e- encrypted   | credentials documents privacy replacement signatures |                                                                                                     |

Figure 3: Key collocations, texts A & C-E: card(s)

<!-- image -->

## 3.4. Pronouns

Table 7 displays the relative incidence of all pronouns (column 1) in the five texts and further specifies percentages of individual items; it also lists those that are absent or prominent (first and second column from the right, respectively) in a given text.

Table 7: Pronoun incidence

|    | % of all words   | % of all pronouns   |   % of all pronouns | % of all pronouns   | % of all pronouns   | % of all pronouns   | % of all pronouns   | Prominent other than i t   | Absent       |
|----|------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|----------------------------|--------------|
|    | pronouns         | 1st                 |                   2 | 3m                  | 3f                  | 3it                 | 3pl                 |                            |              |
| A  | 3.80             | 3.2                 |                  13 | 1.6                 | 11                  | 32                  | 39                  | you she                    |              |
| B  | 2.24             | 0                   |                   0 | 0                   | 0                   | 46                  | 54                  | they                       | I you he she |
| C  | 1.56             | 0                   |                   0 | 3                   | 4                   | 57                  | 36                  | they                       | I you        |
| D  | 4.81             | 44                  |                   7 | 12                  | 0                   | 30                  | 7                   | I he                       |              |
| E  | 2.28             | 25                  |                   5 | 5                   | 0                   | 60                  | 5                   | I he                       |              |

It will appear that of the total word mass in each text, D has the highest proportion of pronouns (4.81% of text mass is pronouns, chiefly I and he ), whereas in C only 1.56% of all words are pronouns, all third person). B appears to be an impersonal text with only it and they . We will now take a closer look at the characteristic verb phrases taking these pronouns as subject. Figures 4, 5

and 6 exhibit the collocations of first, second and third person personal/possessive pronouns or names, respectively.

Evidence from pronoun collocation (as displayed in figures 45-6) and similar patterns has immediate communicative implications, since first person pronouns reflect subject or agent orientation,  while  second  person  pronouns  reflect  object  orientation. First  person  pronouns  as  well  as  third  person  pronouns  and names are often associated with statements in a reporting context such as news, which is beginning to suggest itself as a likely genre for text D. However, we still need more clues before definite  genre  identifications  can  be  made,  for  which  we  will  now turn to external situational variables manifested in the five texts.

Figure 4: First person collocations

<!-- image -->

| Text   | Pronoun     | Verb phrases                                                                                                                                                                          |
|--------|-------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| A      | First pers. | amassociated call a computer (have)developed/demonstrated/proposed/shown                                                                                                              |
| D      | First pers. | amdelighted/delirious decided expect said think started leaping about                                                                                                                 |
| E      | First pers. | can then go to a are doing as well/better are very serious/driving Mondex asked ourselves found no business have cracked the problem/a powerful start/spent a million/ to account for |

Figure 5: Second person collocations

| Text   |                            | Pronoun               | Verb phrases                                                                 |
|--------|----------------------------|-----------------------|------------------------------------------------------------------------------|
| A      | what whom every time where | Second pers./ (poss.) | buy communicate with make a telephone call travel life/medical history/taxes |
| D      |                            |                       | can't trust have to account for wouldn't worry shan't                        |

Figure 6: Third person collocations

| Text   | Verb phrases             | Pronoun name                                | Apposition                            | Verb phrases                               |
|--------|--------------------------|---------------------------------------------|---------------------------------------|--------------------------------------------|
| D      | explains says points out | Mr. Jones                                   |                                       | hadlearned recognized believes denies      |
|        |                          | he                                          |                                       | had spent time has signed up says suggests |
| E      | points out says comments | Mr./David Birch R. Nuttall Harvey Mike Nash | Director of Manager of Vice President |                                            |

## 4. Findings: External variables

The relative incidence of pronouns has already given some hints as to the pragmatic context of some of the texts and inferences one might draw  from  the  occurrence  of I/you .  In  this  section  we  will  briefly expand this perspective to include other aspects of the situational and semiotic  elements  manifested  in  the  text  samples;  we  will  take  the texts one by one.

## 4.1. Text A

This  sample  contains  an  invented  story  illustrating  the  operation  of electronic signatures. The characters appearing in the (clearly didactic) story are Alice and Bob, prominence being given to she/her. Most of the you -forms appear in the initial 'attention-grabber'.

## 4.2. Text B

Interspersed in the running text are illustrations in the form of tables and  diagrams  as  well  as  enhanced  quotes  repeating  key  statements. Personal pronouns are totally absent.

## 4.3. Text C

This  text  also  has  pie-charts  and  diagrams  interrupting  the  flow  of text.

## 4.4. Text D

As noted above, this sample has the highest pronoun incidence of all, and half of them are I/you .  The  item Mr. appears half-a-dozen times, direct quotes abound. Verbs with first person subject are mostly emotive or indicating mental activity, those with third person subject often cognitive  (cf.  table  6  and  figures  4-5-6).  A  URL  is  given  below  the text.

## 4.5. Text E

Mr .  appears  once,  direct  quotes  abound,  text  is  sprinkled  with  selfevaluative  verbs,  company  names,  and  personal  names  with  titles  in apposition (cf. table 6 and figures 4-5-6). E-mail address is given.

## 5. Text profiles and clustering

We have now reached the point where some of the varied measures and scattered observations made above may be brought together synoptically in an attempt to address the general dichotomy posed in the title:  Are  there  aspects  of  the  text  samples  which  reflect  popular  or professional language use, which can be used in further identifying the genre of a given text? Table 8 integrates information given above in a synoptic view:

Table 8: Synopsis of features and observations

|    |   Read- ability |   Termin. density |   Pronouns: incidence | Pronouns: prominent   | Pronouns: absent   | Pragmatic & semiotic            |
|----|-----------------|-------------------|-----------------------|-----------------------|--------------------|---------------------------------|
| A  |               8 |              3.75 |                  3.80 | you she               |                    | Invented story                  |
| B  |              13 |              1.21 |                  2.24 | it they               | I you he she       | tables diagrams enh. quotes     |
| C  |              10 |              7.15 |                  1.56 | it they               | I you              | piecharts diagrams              |
| D  |               5 |              4.82 |                  4.81 | I he                  |                    | directquotes URL                |
| E  |               6 |              8.92 |                  2.28 | I he                  |                    | definitions directquotes e-mail |

The question facing us at this point is the following: Within the subdomain  of  'electronic  money  transactions',  are  there  consistent  lin- guistic criteria, supplemented by pragmatic and semiotic hints, which give us a clue to the textual genre being sampled?

Text A in this perspective has medium readability, medium terminological  density,  but  high  pronoun  incidence.  Notable  among  pronouns used are you (reflecting attempts to engage the reader) and she (arising from the embedded didactic story). This is clear evidence of a popular genre, and accords well with the overall impression created by articles published in Scientific American , which is the source of text A (Achieving Electronic Privacy [abbreviated], by David Chaum.

Text B marks something of a contrast to A in that it has very low readability (scoring all the 13 possible points, see table 2), very low terminological density, and medium pronoun incidence. Here you and she (and the remaining personal pronouns) are conspicuously absent, yielding the arena to the impersonal it and they .  The  running  text  is supplemented  by  tables  and  diagrams  and  punctuated  by  enhanced quotes.  Readers  familiar  with The  McKinsey  Quarterly will  perhaps recognize  the  journal's  style  of  presentation,  well  illustrated  and  yet formal  without  inundating  the  reader  in  technical  terminology.  The genre is thus that of a professional article ('Who will capture Value in On-line  Services?'  by  Tab  Bowers  and  Marc  Singer)  without  being heavily academic.

Text  C  is  similarly  illustrated  with  charts  and  diagrams,  which  in itself would immediately raise a suspicion that we are looking at another professional journal article. The impersonal pronouns it and they are  absolutely  dominant, he marginal  and she only  used  to  achieve political  correctness  in  the  phrases he or she and his  or  her, while I and you are totally absent. Pronoun incidence is in fact lower in C than in any other sample, while its terminological density is second highest and readability second lowest. If there is an academic professional article among the samples, this has got to be the one - which appeared in the Bank of England Financial Stability Review as 'Electronic Money', written by Mark Robson.

Text D puts us squarely back in the popular genres again, with top score for readability, medium score for terminological density and top score  again  for  pronoun  incidence,  all  of  them,  in  fact,  except she . First person I/we dominate, it is a strong second. Text D is peppered with direct quotes in the running text, which accounts for the consis- tent first person focus and strongly suggests a reporting of statements. Text D is typical of feature articles appearing in the IT/Management section of the Financial Times, this one entitled 'Vision for a global multi-currency card' and contributed by Rod Newing.

That leaves us with Text E, which is really the crucial test of our approach, since it was downloaded from a WWW-site without the normal genre-characterizing publishing channel. Its readability is almost as high as D, while its terminological density is much higher, in fact the highest of all samples. Pronoun incidence is among the lowest, but here it accounts  for  2/3  of  all  pronouns, I for  1/4.  The  definitions occurring throughout the text are clearly provided as a didactic softening  of  the  impact  of  the  terminological  density  already  noted,  while the  direct  quotes  again  indicate  a  reporting  function.  The  text  was found by searching for key words (cf. table 3) on AltaVista, and it was only at a later  stage  that  the  present  writer  discovered  that  E  had  in fact been written by the same author as text D and indeed had appeared in the same newspaper three months earlier: 'Electronic cash', by Rod Newing, in the Financial Times .

## 6. Main claims and a conclusion

- Statistical  footwork  (provided  by  standard  word  processors  and concordancing packages) is useful in text-based genre studies.
- Frequency studies give useful hints  as  to  density  of  domain-focal lexical words (i.e. terms).
- Concordance studies reveal collocational patterns pointing to potential multi-word terms for the domain.
- Text-linguistic,  pragmatic  and  semiotic  features  provide  essential clues to function and purpose of text in domain community.

Conclusion: The pilot study of five short LSP texts reported here indicates  that  the  specific  question  posed  above  can  be  given  a  positive answer:  Yes,  it  is  possible  to  work  'bottom-up'  through  textual-linguistic features (supplemented by pragmatic/semiotic observations) to arrive at a generic typology of text forms. No feature in isolation but rather the clustering of features from various levels of analysis indicates significant convergences and contributes to placing genre taxonomies on a stronger empirical foundation.

The implication is that we should build, extend, and maintain LSP corpora  for  more  domains,  develop  tools  and  methods  for  studying them efficiently, and establish and cultivate professional networks linking the  expertise  of  text  linguists,  technical  communicators,  translators, LSP teachers and domain specialists.

## Bibliography

## 1. Text samples

- Bowers, Tab and Marc Singer (1996). Who will capture value in On-line Services? The McKinsey Quarterly , n°. 2, 78-83.
- Chaum,  David  (1992).  Achieving  Electronic  Privacy  [abbreviated].  In Scientific American , 96-101.
- Newing, Rod (1996). Electronic cash, WWW.
- Newing, Rod (1997). Vision for a global multi-currency card. In Financial Times ,  5 february.
- Robson,  Mark  (1996).  Electronic  Money.  In Bank  of  England  Financial  Stability Review , 23-30.

## 2. References

- Biber, D. (1988). Variation across speech and writing .  Cambridge: Cambridge University Press.
- Brekke, M, Myking, J. and Ahmad, K. (1996). Terminology management and lesserused living languages: A critique of the corpus-based approach. In TKE'96: Terminology and knowledge engineering . Frankfurt/M.: Indeks Verlag.