## Claudia Giehl*

## Optimizing and Translating LSP texts

## 1. Introduction

LSP texts comprise texts that are as different as a textbook chapter written for a fi  rst semester medical student or the latest journal article written by a frontline-researcher. Yet the main purpose of an LSP text is always to convey factual information (Gläser 1995:153). Therefore, when it comes to writing or translating an LSP text, its readability should be of highest priority:

Die Fachsprache [hat] für den Wissenschaftler und Techniker die Aufgabe, Erkenntnisse festzuhalten und zu übermitteln. Ihre Bewertung als  Kommunikationsmittel erfolgt deshalb nach dem Verhältnis von Leistung  und  Energieverbrauch.  (Fluck 5 1996:34,  with  reference  to Wüster  3 1970)

In  order  to  write  a  readable  text,  one  has  to  know  what  characteristics infl  uence readability. For text translation, it is not only important to know what makes a text easy to understand, but also to take the different conventions and styles of the languages into account (Göpferich 1993; 1995; 1998; Nord 1998). Therefore, the translator has to aim at high readability but also meet the stylistic requirements of the target language. These two criteria may or may not be compatible. The better the translator's knowledge about criteria that infl  uence readability and their traditional distribution in source and target language LSP texts, the more he or she will be able to make the right translation decisions.

* Claudia Giehl Saarland University European Project Offi  ce c/o EURICE GmbH Science Park 1 Stuhlsatzenhausweg 69 D-66123  Saarbrücken

115

## 2. Readability of English and German LSP texts

English LSP texts are usually perceived to be much easier to comprehend  than  German  LSP  texts.  Signifi  cant  differences  have  been  described at the macrostructural level where English texts are considered to be more reader-oriented while German texts are more content-oriented (Kaplan 1966; Clyne 1984; 1987; 1996; House 1996; 1997; Gerzymisch-Arbogast  1993;  1997;  Buhl  1999). Yet  no  differences  are  described on the microstructural/lexical level. However, it is a widely felt impression that the lexis of German LSP texts is particularly diffi  cult and complicated. The present work addresses lexical parameters that might infl  uence the comprehensibility of English and German LSP.

The lexical inventory of an LSP text consists of the subject-specifi  c terminology and of 'other' words.

LSP-text = subject-specifi  c terminology + remaining text (RT)

In  cooperation  with  Jan Alexandersson  and  Paul  Buitelaar  from  the German Research Center for Artifi  cial Intelligence (DFKI) I analyzed written English and German medical texts, such as textbook texts and journal articles with regard to their proportion of medical terminology. It has been described that in these texts, the medical terminology makes up 20-25% of the texts in both languages (Beier 1980:40; Sieper 1980:3; also confi  rmed by own data, see below). Thus, the medical terminology cannot account for the difference in readability.

So far, the lexis in the LSP context has usually been reduced to subject-specifi  c terminology, strongly neglecting the remaining 75-80% of the  texts  (cf.  Roelke  1999:50).  It  is,  therefore,  conceivable  that  precisely this part of LSP texts causes the subjective differences in comprehensibility. To address this possibility, we focused our study on the 'remaining text'.

The remaining text consists of basic terms (BT) and non-basic-terms (non-BT).

```
RT = basic terms + non-basic terms
```

It is conceivable that basic terms, which comprise the most frequent and the most important terms (Langenscheidt 2000:VII), are easier to understand than non-basic terms.

## 3. Hypothesis

While it is certainly true for all LSP texts that they consist of subjectspecifi  c terminology, basic and non-basic terms, and the proportion of terminology does not differ in the languages under investigation, nothing is known about the distribution of basic- and non-basic terms in different languages.

It can therefore be hypothesized that English LSP texts have a higher proportion of basic terms in their RT than German LSP texts, and that this difference accounts for the subjective impression of higher comprehensibility of the English texts.

## 4. Corpus analysis

What is common to many of the studies in the fi  eld of LSP research is that they usually remain at the qualitative level (Vihla 1998:74). In our study, we aimed at combining qualitative and quantitative aspects by testing our hypothesis on a large computer corpus. We wanted to show whether or not there is a difference in the relative number of basic terms in the RT of German and English medical LSP texts. Our study was conducted in three steps:

- Manual analysis of a small control corpus
- automated analysis of the same control corpus & comparison of results
- automated analysis of a large corpus (book corpus and paper corpus)

## 4.1.  Corpus

When designing the corpus, we chose the following criteria for the texts we wanted to include:

- Size: we aimed at several hundred thousand words
- Extracts from books were accepted
- Research articles were to be included in full
- Subject: texts from textbooks for medical students and medical journals
- Texts had to be written by native speakers
- Publication date: 1990 and later

The small control corpus for the manual and automated control analysis  contains  7  English  texts  and  8  German  texts  randomly  selected from different textbooks for medical students, each approximately 400 words long. The large book corpus for the automated analysis contains excerpts from 15 different authors for each language. The excerpts are between 240 and 46,606 words long and were randomly chosen by the publishing houses that provided them. 1  The paper corpus for automated analysis contains full text articles of fi  ve and 13 different native authors, respectively.

Tab. 4.1: Corpora. For further information on corpus design and compilation see Bowker & Pearson (2002: 45ff.)

|                 |   Control corpus |   Book corpus |   Paper corpus |
|-----------------|------------------|---------------|----------------|
| Size [words]    |                  |               |                |
| Eng             |            2.479 |       123.149 |         52.617 |
| Ger             |            2.512 |       108.622 |          8.632 |
| Total           |            4.991 |       231.771 |         61.249 |
| Number of texts |                  |               |                |
| Eng             |                7 |            15 |             13 |
| Ger             |                8 |            15 |              5 |
| Total           |               15 |            30 |             18 |

## 4.2.  Analysis

A manual and an automated analysis was conducted: The manual analysis was performed to initially test our hypothesis on the small control corpus. This control corpus was subsequently analyzed by the FURORE Software developed by Jan Alexandersson especially for this purpose, and the performance of the program was evaluated. Following this, the same automated analysis was performed evaluating the large (book and paper) corpus.

## Manual analysis

Texts were analyzed with respect to their total number of words, average  sentence  length  and  number  and  proportion  of  medical  terms

1 German texts were provided by the Springer Verlag, Abteilung Medizinische Lehrbücher; English texts were provided by Garland Science Publishing.

(medical terms were identifi  ed by specialists). In the 'remaining text', basic terms 2  were counted and their proportion was calculated.

## Automated analysis

First, the documents were annotated with multiple layers of linguistic information, including part-of-speech tagging, morphological analysis, and the identifi  cation of medical terms using the MeSH (Medical Subject Heading) subset of the Metathesaurus of the Unifi  ed Medical Language System UMLS 3 . Annotations were performed by Paul Buitelaar. The FURORE software determined the total number of words, average sentence length and number and proportion of medical terms. Medical  terms  were  subsequently  deleted  to  prevent  an  overlap  in word counts. In the remaining text, basic terms were counted and their proportion was calculated:

2 We combined the information from four dictionaries of basic terminology to create a list of 5000 basic terms for each language.

3 For linguistic processing we used ShProT (Buitelaar 2003) a shallow processing tool that consists of four integrated components of which we used the fi  rst three: the SPPC tokenizer, TnT for part-of-speech tagging, and Mmorph for morphological analysis. ShProT is used in the scope of the MUCHMORE project at the DFKI.

Fig.  4.2:  Example  of  a  result  fi  le  produced  by  the  FURORE  software:  File 'ceb3.xml' is read and processed by the program, basic vocabulary such as 'for', 'example', 'be', 'very', is followed by medical terms such as 'spatial', 'discrimination', 'retina', 'sensory', 'receptor', followed by non-basic words such as 'intensity', 'determine', 'amplitude', or 'historic' (sample words marked in bold). At the end of the fi  le, the program calculates the data.

<!-- image -->

manual analysis of control corpus

40%

35% +

30% -

25%

20%

15% ·

10%

5%

0%

medical terminology in

total text

@ English

German

non-basic terms in

remaining text

## 5. Results

## 5.1.  Manual analysis

The manual analysis of the small control corpus confi  rmed the hypothesis:

Fig. 5.1: Manual analysis of control corpus?

<!-- image -->

The proportion of medical terminology was as described in the literature between 20 and 25% with no difference between the two languages. The sentence length (not shown), too, did not display any differences. However, we found a signifi  cant difference in the proportion of non-BT with 31% in German as compared to 14 % in English texts, supporting our hypothesis that understandability of the texts correlates with the portion of basic terms.

## 5.2.  Manual vs. automated analysis

In order to evaluate the correctness of the data obtained with FURORE, we next compared the manual analysis with the automated analysis performed by FURORE on the same corpus.

50%

45%

40%

35%

30%

25%

20%

15% -

10%

5%

0%

comparison manual vs. automated analysis

automated manual

medical terminology

in total text

& English

D German

Fig. 5.2: Manual vs. automated analysis

<!-- image -->

The automated analysis reveals a similar proportion of medical terms in English and German texts with a statistically not signifi  cant tendency to fewer medical terms in English (10 % vs. 17% respectively). The overall percentages, however, were lower in the automated than in the manual approach.

The analysis of non-BT revealed higher proportions in the automated approach (but similar tendencies in both approaches) with 43% in German vs. 31% in English texts. Further analysis reveals that, in the automated countings, the higher numbers of non-BT and the lower number of medical terms are a result of misgrouping of a small portion of the medical terms into the non-BT group. However, this misgrouping did not  affect  the  overall  tendencies  among  the  groups.  Finally,  the  sentence lengths display high agreement in both approaches (not shown). Thus, FURORE appears to be a reliable tool for performing an automated analysis on large corpora.

50%

45%-p

40%

35%.

30%-

20%

25%

15%

10%1

5%+

0%

Automated analysis book vs. paper

book paper

medical terminology

in total text

& English

· German

## 5.3.  Automated analysis: textbook vs. paper

The analysis of textbook texts and paper texts lead to the same results as the above manual analysis:

Fig. 5.3: Comparison book vs. paper

<!-- image -->

Both groups display slightly more medical terms in the English texts and much more non-BT were found in the German texts. This is an important fi  nding since it shows there is no connection between the use of basic terminology and information density. While authors are very free in their choice of form and style when writing books, the structure and length of a journal article is usually strictly regulated. As shown in Figure 5.3, book and journal texts are, however, almost identical with regard to the characteristic proportion of basic terminology in both languages.

## 6. Discussion

The present study reveals that the proportion of basic terms is higher in English than in German medical texts. This supports our hypothesis that there are language-specifi  c differences in the use of basic and non-ba- sic terms, and that the comprehensibility of medical texts increases with the proportion of frequently used, standard-language words. A special software tool, FURORE, was written to evaluate a large medical corpus, and we could show that this leads to similar results to a manual analysis, confi  rming its validity. This offers the possibility to subject large medical text corpora to automated lexical analysis that might reveal differences between English and German texts that have not been detected so far. We believe that these analyses will provide the basis for simple approaches on a lexical level to improve the comprehensibility of specialized texts.

## Differences between the manual and automated approaches

The analysis of medical terms reveals differences between the manual and automated approaches. This was expected since the Medical Terminology (MT)-reference list for automated analysis, as any lexical reference list, is incomplete, and it is even less comprehensive for German MT. Thus, some medical terms are not recognized by the program, whereas in a manual analysis, medical language specialists can identify all medical terminology. This explains the lower values for medical terms in the automated vs. manual analysis, but also the higher absolute values for the non-BT groups: Due to the evaluation scheme, unrecognized medical terminology accumulates in this group. This effect is of similar extent for both languages and thus does not signifi  cantly infl  uence the results.

## Infl  uence of the BT-proportion on text comprehensibility

In  the  German texts,  more than a third of the non-MT are not basic terms, i.e. complicated and unusual expressions that are more diffi  cult to recognize and to understand, but only 14% of the English non-MT belongs to this group. This may contribute to the subjective impression that English LSP texts are easier to understand than German texts.

## A multidisciplinary point of view

Whether a word is considered a 'basic term' is determined by two criteria: (1) The frequency with which it occurs in spoken and written language, and (2) how basic/fundamental it is (Langenscheidt 2000:VII). The latter corresponds with the age at which a word is learned. This so-called 'Age of Acquisition' and the 'Word Frequency' are the most wide- ly investigated variables in word recognition, processing and memory research in the fi  eld of cognitive science (Ghyselinck & Lewis & Brysbaert 2004, 43ff.). Studies in this fi  eld have revealed, for example, that frequently encountered words are processed more easily than less common words (Grainger 1990; Jescheniak & Levelt 1994). Semantic decisions about high-frequency words can be made faster (Chee & Westphal & Goh 2003) and with greater ease (Chee & Hon & Caplan 2002) than  decisions  related  to  low-frequency  words.  This  so  called  'Frequency effect' can be clearly distinguished from the 'Age-of-Acquisition (AoA) effect' (Fiebach & Friederici & Müller 2003; Ghyselinck & Lewis & Brysbaert 2004, 43 ff.) that states that early learned words are processed faster than those words learned at a later stage (Ghyselinck & Lewis & Brysbaert 2004). The AoA proved to be a signifi  cant variable for a wide range of word processing paradigms such as lexical decision (Brysbaert & Lange & van Wijnendaele 2000; Gerhand & Barry 1999b; Morrison & Ellis 1995; 2000), semantic categorization (Brysbaert & Lange & van Wijnendaele 2000), picture naming (Barry & Morrison & Ellis 1997; Bonin & Chalard & Méot 2002), speeded word naming (Gerhand & Barry 1999a), and auditory lexical decision (Turner & Valentine & Ellis 1998).

With these two factors, word frequency and AoA, having a large impact on the ease of several levels of word processing, it can be predicted that basic terms will most likely show similar effects, and that the relative amount of basic terms in a text strongly infl  uences its readability and comprehensibility.

From the fi  eld of translation theory, it has been argued that the attention of the reader is distracted from the text content if text type conventions  are  not  met  properly,  and  therefore  this  should  be  avoided in LSP translation (Reiß &Vermeer 1984:189; Biere 1989; Göpferich 1998:62f.). It would be important to verify whether this has a negative effect on text comprehensibility and would therefore counteract the above mentioned benefi  cial effects of AoA and word frequency.

## Concluding remarks

Increasing the comprehensibility of specialized text based on lexical parameters might prove to be useful for optimizing newly written medical texts as well as for their translation. The main purpose of a specialized text is to impart information. Thus, optimization of this process should be a major goal, and increasing the comprehensibility for the audience (target group) is surely one important aspect in this context. In contrast, the implementation of a scientifi  c style of writing as a convention for the generation and translation of specialized texts does, at least on the German side, not necessarily act in synergy with the specifi  cation of optimal information transfer. Also, studies in the fi  eld of cognitive science show that the ease of processing positively correlates with the simplicity of texts. We therefore propose that the criterion of comprehensibility should be given priority over scientifi  c style conventions.

To this end, our future research will focus on optimizing the corpus analysis tools, and collecting further evidence to show that specifi  c lexical criteria such as the proportion of BT-words are causal factors for the comprehensibility of a specialized text. In particular, we will expand the corpora to be analyzed and establish comprehensive English and German biomedical text corpora, improve the term recognition, investigate the AoA and frequency-proportions seperately, and extend the analysis to other levels such as syntax and concepts. Parameters revealed by these studies will then be used to test our hypothesis on human subjects. These studies may involve both behavioral memory tests and functional brain imaging. In this context, we will also evaluate whether trespassing classical conventions in the style of scientifi  c writing interferes with comprehensibility.

## 7. References

## 7.1.  General references

- Barry, Chris/Morrison, Catriona M./Ellis, Andrew W. 1997: Naming the Snodgrass and Vanderwart pictures: Effects of age of acquisition, frequency, and name agreement. In Quarterly Journal of Experimental Psychology: Human Experimental Psychology 50, 560-585.
- Beier, Rudolf 1980: Englische Fachsprache. Stuttgart u.a.: Kohlhammer.
- Biere, Bernd Ulrich 1989: Verständlich-Machen: hermeneutische Tradition - historische Praxis - sprachtheoretische Begründung . Tübingen: Niemeyer.
- Buhl,  Silke  1999:  Gestaltungsprinzipien  wissenschaftlicher  Texte  im  Sprachenpaarvergleich Deutsch - Englisch am Beispiel von Texten Einsteins und Russells zur Relativitätstheorie. In Gerzymisch-Arbogast, Heidrun/Gile, Daniel/House, Juliane/ Rothkegel, Annely mit Buhl, Silke (eds.), Wege der Übersetzungs- und Dolmetschforschung . Tübingen: Narr. (= Jahrbuch Übersetzen und Dolmetschen. 1), 117-142.

- Bonin, Patrick/Chalard, Marylène/Méot, Alain/Fayol, Michel 2002: The determinants of spoken and written picture naming latencies. In British Journal of Psychology 93, 89-114.
- Bowker, Lynn/Pearson, Jennifer 2002: Working with specialized language. A practical guide to using corpora. London - New York: Routledge.
- Brysbaert, Marc/Lange, Marielle/van Wijnendaele, Ilse 2000: The effects of age-of-acquisition and frequency-of-occurrence in visual word recognition: Further evidence from the Dutch language. In European Journal of Cognitive Psychology 12, 65-85.
- Buitelaar, Paul/Vintar, Špela/Volk, Martin 2003: Semantic Relations in Concept-Based Cross-Language  Medical  Information  Retrieval. In  Proceedings  of  the  ECML/ PKDD Workshop on Adaptive Text Extraction and Mining (ATEM) . 2003.
- Chee, Michael W. L./Westphal, Christopher/Goh, Joshua/Graham, Steven/Song, Allan W. 2003: Word frequency and subsequent memory effects studied using event-related fMRI. In NeuroImage 20, 1042-1051.
- Chee, Michael W. L./Hon, Nicholas H. H./Caplan, David/Lee, Hwee Ling/Goh, Joshua 2002: Frequency of concrete words modulates prefrontal activation during semantic judgements. In NeuroImage 16, 259-268.
- Clyne,  Michael  1984:  Wissenschaftliche  Texte  Englisch-  und  Deutschsprachiger: Textstrukturelle Vergleiche. In Studium Linguistik 15, 92-97.
- Clyne, Michael 1987: Discourse Structures and Discourse Expectations: Implications for Anglo-German Academic Communication in English. In Smith, L. (eds.), Discourse across Cultures . New York, 73-83.
- Clyne, Michael  2 1996: Intercultural Communication at Work . Cambridge: University Press.
- Fiebach, Christian J./Friederici, Angela D./Müller, Karsten/von Cramon, D.Yves/Hernandez, Arturo E. 2003: Distinct brain representations for early and late learned words. In NeuroImage 19, 1627-1637.
- Fluck, Hans-Rüdiger.  5 1996: Fachsprachen: Einführung und Bibliographie. 5. Aufl  . Tübingen - Basel: Francke. (= UTB. 483).
- Gerhand, Simon/Barry, Christopher 1999a: Age-of-acquisition and frequency effects in speeded word naming. In Cognition 73, 27-36.
- Gerhand, Simon/Barry, Christopher 1999b: Age of acquisition, word frequency, and the role of phonology in the lexical decision task. In Memory/Cognition 27, 592-602.
- Gerzymisch-Arbogast, Heidrun 1993: Contrastive Scientifi  c and Technical Register as a Translation Problem. In Wright, Sue Ellen/Wright Jr., Leland D. (eds.), Scientifi  c and Technical Translation . New York: SUNY. (= American Translators Association Scholarly Monograph Series. IV), 21-51.
- Gerzymisch-Arbogast, Heidrun 1997: Der Leserbezug in Sigmund Freuds psychoanalytischen  Schriften  im  Spiegel  der  englischen  Übersetzungen.  In  Wotjak,  Gerdt/ Schmidt, Heide (eds.), Modelle der Translation / Models of Translation. Festschrift für Albrecht Neubert . Frankfurtam MaIn Vervuert, 213-233.

- Ghyselinck, Mandy/Lewis, Michael B./Brysbaert, Marc 2004: Age of acquisition and the cumulative-frequency hypothesis: A review of the literature and a new multitask investigation. In Acta Psychologica 115, 43-67.
- Gläser, Rosemarie 1995: Linguistic features and genre profi  les of scientifi  c  English. Frankfurt am Main u.a.: Lang.
- Göpferich, Susanne 1993: Die translatorische Behandlung von Textsortenkonventionen in technischen Texten. In Lebende Sprachen 2, 49-53.
- Göpferich, Susanne 1995: Textsorten in Naturwissenschaften und Technik. Pragmatische Typologie - Kontrastierung - Translation . Tübingen: Narr. (= Forum für Fachsprachen-Forschung. 27).
- Göpferich, Susanne 1998: Text, Textsorte, Texttyp. In In Snell-Hornby, Mary/Hönig, Hans G./Kußmaul, Paul/Schmitt, Peter A. (eds.), Handbuch Translation . Tübingen: Stauffenburg, 61-64.
- Grainger,  Jonathan  1990: Word  frequency  and  the  neighbourhood  frequency  effects in lexical decision and word naming. In Journal of Memory & Language 29, 228244.
- House, Juliane 1996: Contrastive Discourse Analysis and misunderstanding: The Case of  German  and  English.  In  Hellinger,  Marlis/Ammon,  Ulrich  (eds.), Contrastive Sociolinguistics . Berlin - New York: de Gruyter, 345-361.
- House,  Juliane  1997:  Interkulturelle  Pragmatik  und  Übersetzen.  In  Wotjak,  Gerd/ Schmidt, Heide (eds.), Modelle der Translation - Models of Translation. Festschrift für Albrecht Neubert. Frankfurt am MaIn Vervuert, 21-40.
- Jescheniak, Jörg D./Levelt, Willem J.M. 1994: Word frequency effects in speech production: retrieval of syntactic information and of phonological form. In Journal of Experimental Psychology: Learning, Memory, and Cognition 20, 687-715.
- Kaplan, Robert B. 1966: Cultural Thought Patterns in Inter-Cultural Education. In Language Learning 16 [1-2], 1-20.
- Morrison, Catriona M./Ellis, Andrew W. 1995: Roles of word frequency and age of acquisition in word naming and lexical decision. In Journal of Experimental Psychology: Learning, Memory, and Cognition 21, 116-133.
- Morrison, Catriona M./Ellis, Andrew W. 2000a: Real age of acquisition effects in word naming and lexical decision. In British Journal of Psychology 91, 176-180.
- Morrison, Catriona M./Ellis, Andrew W. 2000b: Age-of-acquisition of words is a signifi  cant variable in semantic tasks. In Acta Psychologica 104, 215-216.
- Nord,  Christiane  1998: Textlinguistik.  In  Snell-Hornby,  Mary/Hönig,  Hans  G./Kußmaul, Paul/Schmitt, Peter A. (eds.), Handbuch Translation . Tübingen: Stauffenburg, 59-61.
- Reiß, Katharina/Vermeer, Hans J. 1984: Grundlegung einer allgemeinen Translationstheorie . Tübingen: Niemeyer. (= Linguistische Arbeiten. 147).
- Roelke, Thorsten 1999: Fachsprachen . Berlin: Erich Schmidt. (= Grundlagen der Germanistik. 37).

- Sieper, Gerd 1980: Fachsprachliche Korpusanalyse und Wortschatzauswahl: Untersuchungen zur Lexik englischer Fachtexte der Chemie. Frankfurt: Lang.
- Turner, Judy E./Valentine, Tim/Ellis, Andrew W. 1998: Contrasting effects of age of acquisition and word frequency on auditory and lexical decision. In Memory and Cognition 26, 1282-1291.
- Vihla, Minna 1998: Medicor: A corpus of contemporary American medical texts. ICAME Journal 22, 73-80.
- Wüster, Eugen  3 1970: Internationale Sprachnormung in der Technik, besonders in der Elektrotechnik . 3. ergänzte Aufl  age. Bonn: Bouvier.  (1. Aufl  . BerlIn VDI 1931).

## 7.2.  Dictionaries

- Freese,  Holger  2000: Langenscheidts  Grundwortschatz  Englisch .  Berlin  u.a.:  Langenscheidt.
- Langenscheidt 2000: Basic German Vocabulary . Berlin u.a.: Langenscheidt.
- Lübke, Diethard 1998: Learning German Words. Wortschatz Deutsch . Ismaning: Verlag für Deutsch.
- Weis, Erich 1999: Grund- und Aufbauwortschatz Englisch .  Stuttgart  -  Düsseldorf  Leipzig: Klett.

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