Corpora in TS
Corpus linguistics
A corpus is a large, more or less principled, possibly annotated, collection of natural texts
on computer. It is usually put together for a particular purpose and according to specific
design criteria in order to ensure that it is representative of a given area.
Notes
'large' may mean anything from 1m. to 200m. words
'principled': based on a principle, e.g. general vs. specialised (in terms of genre, register,
subject matter etc.), written vs. spoken, synchronic vs. diachronic (historical), dialect or
variant (BrE vs. AmE)
'texts' may be written or spoken (transcribed) or both
What are corpora good for?
Looking at actual use: how people actually speak, write, translate (performance)
Description rather than prescription
Natural language rather than formal representations of language
Escaping for reliance on native speaker intuition (which may be biased)
Variation of language features in relation to other (non-) linguistic features (genre, age,
gender)
Assessing (relative) frequency
Lexicography
Terminology – extracting from a representative corpus of authentic texts
Use tells us about meaning, e.g. big vs. large vs. great
Ensuring all variants of a term are covered
Demonstrating their linguistic behaviour and lexical patterns (e.g. collocations
from KWIC concordances (key words in context), inc. changes (diachronic)
Showing (changes in) frequency of terms
Sociolinguistics
Intuition on registers, situations etc. unreliable or unavailable
e.g. how do doctors speak to patients?
Teaching
Assessing relative frequency of patterns (and thus order of teaching)
e.g. Present Simple vs. Present Continuous
Search for translation 'universals' such as explicitation, simplification, levelling etc.
Limitations
Corpora are always incomplete
Selection procedures may bias the sample – what constitutes a 'representative' sample
hard to define (e.g. Slovene press – which papers, in what proportions, which parts of the
paper etc.)
Text fragments may paint a different picture from whole texts
Some texts may be unavailable for copyright or other reasons
Spoken language much harder to deal with
Some features not susceptible to investigation, being hard to detect e.g. ellipsis
Nothing can be inferred from non-occurrence in a corpus
Frequency and grammaticality are not the same thing (less/fewer people, he might of done
it) Occurrence in a corpus may not make an item standard/acceptable
Corpora provide facts, but not all facts are significant or interesting
A corpus is only a tool, not an end in itself (though it may involve a great deal of work)
Types of corpora for translation research
Parallel corpora (bilingual)
- Original SL texts in language A and their translations in language B
- Only as reliable as the translators (e.g. Evroterm) – particularly problematic
translations from Slo to Eng
- Tell us how translators actually tackle translation problems in practice
- Search for translation norms
Multilingual corpora
- Sets of two or more monolingual corpora using similar design criteria
- Study linguistic features/items in home environment (not translations)
- Offer insights into behaviour of 'equivalent' items and structures in different
languages: help develop teaching materials
- Cannot provide answers to theoretical questions in TS
- NB: based on the assumption that there is a natural way of saying anything in any
language and all we have to do is find it in languages A and B. But: "there are
some things that cannot be said naturally in some languages" (Headland 1981
cited in Baker 1995: 233) e.g. Dober tek or Gospa Maja in English, Good
afternoon in Slovene
- Rather than comparing language A and B, or ST and TT, makes more sense in TS
research to compare regular text production with texts produced by translation. >
Comparable corpora
- Two separate collections of texts in the same language (A):
o original texts in language A (monolingual corpus)
o translations into language A from a given SL (or SLs)
- Both should cover a similar domain, variety of language and time span, and be of
comparable length
- Helps us identify patterns specific to translated texts or with significantly higher
or lower frequency than in original texts (my impressions:…)
o e.g. namely over-represented in Slo-Eng translations (from namreč)
o e.g. on the one hand … on the other over-represented in Slo-Eng
translations
o e.g. therefore over-represented in Slo-Eng translations (because of zato)
o e.g. organising words like although, nevertheless, furthermore, moreover
under-represented in Slo-Eng translations
o e.g. phrasal verbs under-represnted in Slo-Eng translations
-
o Baker's e.g. They claimed/said (that) they would – probably that used
more in translations than in original texts
Also to support or refute hypotheses about the process of translation
Corpus analysis software such as WordSmith can be used to identify a range of textual
features that would be laborious or extremely difficult to identify manually:
Type/token ratio
(see notes on quantitative analysis)
Lexical density
(ditto)
Average word and sentence length
Frequency lists - provides statistical evidence for stylistic features such as key words
Concordance – a concordancer lists all occurrences of a word and a number of
preceding and following words
– very helpful when looking for collocations
Quantitative analysis
Possible lines of research using comparable corpora:
1. Type-token ratio (TTR)
To a computer, a sequence of letters with an orthographic space on either side is a word
or token. We can say there are x tokens of a particular word, e.g. say, in a given corpus.
The word-form say is a type, so we can say there are x tokens of the type say in a corpus
of y million words.
Corpora can be compared in these terms e.g. Krishnamurthy (1992) overall type-token
ration for BBC World Service broadcasts approx. 174 for The Times newspaper approx.
60 (i.e. each type occurs on average 174 times rather than 60). Thus the former more
lexically restricted than the latter – explainable by spoken-written difference?)
Comparing translations with non-translations we may find a higher type-token ratio and
thus evidence of lexical simplification.
type-token ratio n. In the study of texts, the ratio of the number of different words, called
types, to the total number of words, called tokens. For example, in a particular text, the
number of different words may be 1,000 and the total number of words 5,000, because
common words such as the may occur several times, and in this case the type-token ratio
would be 1/5 or 0.2.
Used for investigating e.g. child language acquisition
2. Lexical density
Lexical words have 'content' – nouns, adjectives and verbs
Grammatical words organise language – prepositions, determiners, pronouns
Lexical density = percentage of lexical vs. grammatical words in a corpus
Lexical density of written English higher than spoken: the former is edited, redrafted,
repetition and redundancy cut; the latter has more contextual clues, has to be interpreted
in real time.
Lexical words belong to open sets therefore less predictable
'Difficult' texts have higher lexical density and information load.
Is lexical density of translations lower than originals?