logging in or signing up Stylistics harl19lyn Download Post to : URL : Related Presentations : Let's Connect Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 467 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: June 17, 2011 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Stylistics and stylometry: Stylistics and stylometryWhat is “style”?: 2 /28 What is “style”? Term not much loved by linguists Too vague Has connotations in neighbouring fields (“style” = good style, ie 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 elseStyle as choice: 3 /28 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 50p; no other coin is acceptable 50p pieces only Propositional meaning is the same; difference in expression conveys something else (register etc)Style as choice: 4 /28 Style as choice Style is a choice, but often the “choice” is somewhat predetermined ie a choice between appropriate and inappropriate style So maybe “style” is just another word for register?Style and the norm: 5 /28 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/register? Note also that the norm shifts: eg Bible AV was written in the vernacular of its time Literary stylistics focuses on the exceptionalSlide 6: 6 /28 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 textStylistic analysis: 7 /28 Stylistic analysis Gulf between literary vs linguistic stylistics Lit crit focuses on effect on the reader, intended or otherwise, so largely intuitive and subjective Linguistic stylistics looking for characterisations of style (including literary style) in terms of linguistic phenomena at the various levels of linguistic descriptionStylistic analysis: 8 /28 Stylistic analysis Inventory of linguistic devices and their effect usually in a contrastive way: in contrast with other writers in a similar genre in contrast with other genres Linguistic devices described in terms of the usual linguistic levels of description: phonology, morphology, lexis, grammar, etc. Effects can be directly expressive, or indirectly, by association example: onomatopoeia vs alliteration as a phonological deviceStylistic analysis Crystal & Davy (1969) Investigating English Style: 9 /28 Stylistic analysis Crystal & Davy (1969) Investigating English Style 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 identifiedTypes of features: 10 /28 Types of features “Invariable” features due to the individual or the time – usually of little interest Discourse features medium (= Halliday’s mode), what features distinguish written language from spoken language participation: eg 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) eg message delivered as a letter, postcard, text message, email, etc Singularity: deliberate occasional idiosyncraciesMethod and function: 11 /28 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 (see later) partially automated (factor analysis)What to count: 12 /28 What to count Simple things may characterise 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 comparativelyWhat to count: 13 /28 What to count More complex analyses can give a more interesting picture specific syntactic structures degree of modification in NPs types of verbs (eg 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 eg factor analysis: identifies from a (big) range of variables which ones best identify/characterize differencesSlide 14: 14 /28 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 fluctuationSlide 15: 15 /28 How to count How to recognize paragraph breaks? How to recognize sentence breaks? Headlines don’t end in a fullstop Not all sentences end in a fullstop 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 cf. through vs idea counting syllables implies either a dictionary or an algorithmMore sophisticated counting: 16 /28 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)Quantifying register differences: 17 /28 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 computationsExample: 18 /28 Example From D. Biber, S. Conrad & R. Reppen, Corpus Linguistics: Investigating Language Structure and Use , Cambriufge University Press, 1998. Ch 5: the study of discourse characteristicsMultidimensional analysis: 19 /28 Multidimensional analysis Collect a huge range of measures of a wide variety some simple word counts syntactic features classes and subclasses of N,V,Adj,Avd Factor analysisSlide 20: 20 /28Slide 21: 21 /28 ~150 features in allFactor analysis: 22 /28 Factor analysis Statistical method to take large number of apparently random variables and group them together into “factors” Factors will be groups of (+ve and –ve) features Linguist might then try to characterize the factors in terms of some psycholinguistic featureSlide 23: 23 /28Example: 24 /28 Example Biber took two Google classifications of text types: “Home” and “Science” Harvested ~1500 webpages in each category (3.74m words) originally got ~2500 webpages, but some were not suitable http://jan.ucc.nau.edu/biber/Web text types.pptSlide 25: 25 /28Summary of analysis: 26 /28 Summary of analysisSlide 27: 27 /28Slide 28: 28 /28 You do not have the permission to view this presentation. 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