1. Stylistics and Stylometry
CSC Machine Translation
Dr. Tom Way

2. What is “style”? Term not much loved by linguists
Too vague
Has connotations in similar fields (“style” = good style, a value judgment)
Many books/articles make reference to etymology of the word (Lat. stilus = ‘pen’), so it follows that style is mainly about written language
Various definitions, some very close to things already seen (especially “register”)
Two main aspects widely supposed:
style is choice
style is described by reference to something else

3. Style as choice
For any intended meaning there are a range of alternative ways of expressing that meaning
Different choices express nuances
of meaning
of other things (style?) eg buy vs purchase
Example:
Visitors are respectfully informed that the coin required for the meter is a quarter; no other coin is acceptable
Quarters only
Propositional meaning is the same; difference in expression conveys something else

4. Style as choice (2)
Style is a choice, but often the “choice” is somewhat predetermined
For example: a choice between appropriate and inappropriate style
So perhaps style does not connote “good” or “bad” but merely the way in which the author expresses or conveys things

5. Style and the norm Some writers define style as
“individual characteristics of a text”
“total sum of deviations from a norm”
But what is the “norm”?
Is there some form of the language that is neutral as regards style?
Note also that the norm shifts: for example, many works are written in the vernacular of their time
Literary stylistics focuses on the exceptional

6. Style and the norm (2)
Even if there is no norm, we can describe style comparatively
Stylistics mainly involves comparing and contrasting texts
and associating linguistic variance with contextual explanation
Some authors see style as being what is added to the text

7. Stylistic analysis
Informally identify stylistic features felt to be significant
Devise a method of analysis which facilitates comparison between usages
Identify the stylistic function of the features so identified

8. Types of features
“Invariable” features due to the individual or the time – usually of little interest
Discourse features
medium, what features distinguish written language from spoken language
participation: e.g., monologue vs dialogue
Province (= field) lexis and syntax
Status (= tenor) features relating to relative social standing of writer/speaker and reader/listener
Modality (= text type) e.g., message delivered as a letter, postcard, text message, , etc.
Singularity: deliberate occasional idiosyncrasies

9. Method and function Methods and features determine each other
you can only measure features that you can extract
simple counting features are easy to extract
more complex features can be extracted thanks to NLP techniques of corpus annotation (tagging, parsing, etc)
Describing the function of observed differences
could be based on intuition
or using more advanced techniques (factor analysis)

10. What to count Simple things may characterize different styles
average sentence length
average word length
type:token ratio (vocabulary richness)
number of types = number of different words
number of tokens = total number of words
vocabulary growth (homogeneity of text)
number of new types in 1st, 2nd, …, nth 1000 words
in rich varied text, number will climb steadily
Especially when used comparatively

11. What to count (2)
More complex analyses can give a more interesting picture
specific syntactic structures
degree of modification in Noun Phrases (NPs)
types of verbs (e.g., verbs of persuasion, speech verbs, action verbs, descriptive verbs)
distribution of pronouns (1st/2nd/3rd person)
etc. (anything you can think of)
Quite sophisticated mathematical techniques can give an overall picture
e.g., factor analysis: identifies from a (big) range of variables which ones best identify/characterize differences

12. Normalization and significance
Always important to compare like with like
It is usual when counting things to “normalize” over the length of the text
If one text is longer than the other, of course you would expect higher frequencies of everything
Issue of statistical significance
Small differences may not really tell you anything
Various measures can confirm whether difference is statistically significant or due to random fluctuation

13. How to count How to recognize paragraph breaks?
How to recognize sentence breaks?
Headlines don’t end in a full stop
Not all sentences end in a full stop
Not all full stops are sentence ending (abbreviations)
How to count words
Hyphenated words, contractions e.g. don’t
How to measure word-length/complexity
length only roughly corresponds to complexity
number of characters vs. number of syllables
counting syllables implies either a dictionary or an algorithm

14. More sophisticated counting
Tagging and parsing allows you to look at grammatical and lexical issues
Use of particular POSs (conjunctions, pronouns, auxiliaries, modals)
Use of particular features (tenses, …)
Use of particular constructions (passives, interrogatives)

15. Quantifying register differences
Much work based on corpora trying to quantify and characterize register differences
Work pioneered by Douglas Biber
Simple counts like the ones suggested
Also, more complex computations

16. Example anaphoric noun - refers back to previous object
anaphoric pronoun - refers back to previous object
exophoric pronoun - refers to something outside text
From D. Biber, S. Conrad & R. Reppen, Corpus Linguistics: Investigating
Language Structure and Use, Cambriufge University Press, 1998.
Ch 5: the study of discourse characteristics

17. Features (1)

18. Features (2)
~150 features in all