Using BNC-xml in language teaching and learning

1. Guy Aston guy@sslmit.unibo.it Using BNC-xml in language teaching and learning

2. Where do corpora fit in? • As a teaching aid in the classroom • Replace teacher intuition • Place native/non-native speaking teachers on equal terms • As a self-access learning aid • Find out about the language/the culture for yourself (data-driven learning) • Hypothesis testing • Hypothesis generation • Contradict your teacher

3. What the corpus can tell you • Sinclair’s (2003) regularity types / Hoey’s (2005) Lexical priming • Collocations - associations with other word forms/lemmas: immemorial with time • Colligations - associations with • grammatical categories: than with comparative adjectives; • structural positions: text-initial sixty • Semantic preferences - associations with semantic classes: ago with time nouns • Semantic prosodies - evaluative classes: a load of (rubbish)/loads of (money) • Contextual associations • text-type • register

4. Learners aren’t linguists • The aim is NOT to provide • a complete description of all the data • maximum generalisations/abstractions • The aim IS to take away • usable partial generalisations • memorable experiences • enthusiasm • Using BNC-xml with Xaira can provide these? • Examples focussing on Xaira improvements • From my experience with advanced learners of English

5. Example 1: Grammer • To + gerund • used to -ing, accustomed to -ing, look forward to -ing, object to -ing • Xaira AddKey Query allows you to look for any word with a specified POS value! • To + VVG|VBG|VDG|VHG

6. The AddKey query (any VVG) Or VBG, or VDG, or VHG (multiple selections)…

7. QueryBuilder: To NEXT VVG Or VBG, or VDG, or VHG …

8. Too many solutions

9. Random 30/14227 (sort 1L)look forward to / when it comes to / devoted to / well on the way to

10. To + V.G is written formal …

11. Collocates of to + V.G (1,0): by frequency

12. Learners should take something away which is • relevant • memorable • typical • not over-general • E.g. • The French are the meanest when it comes to sending Christmas cards • When it comes to buying houses, the British are keenest of all • I’m not exactly the archetypal Mills & Boon dark stranger when it comes to courting girls

13. Example 2: the verb tend • Missing from textbooks (Carter & McCarthy 1995) • Frequent (>100/M), widely distributed • How is it used?

15. Too many solutions? Try Collocation/Analysis

17. VERB collocates (0,3): by frequency

18. TEND to concentrate (30/96)

19. Colligates (0,2: lemmata) Just what nouns?

20. SUBST collocates (lemmata: 0,2)

21. Tend * SUBST collocates (25/352)

22. An odd list of nouns • You can tend: • gardens • cattle/sheep/flocks • fires • Things can tend: • to unity/infinity • to sort of VERB

23. Isn’t this all in the dictionary? • Perhaps, but the corpus gives • More frequency/distribution information • More examples • Access to wider contexts • Practice in working things out for yourself • Casual encounters – did you know tend to unity? • The corpus calls for an open mind – you regularly find the answer to a different question from the one you started off with … but you learn a lot in the process

24. Example 3: Hell for leather • How, when and where an idiom is used • Frequency? • Distribution? • Variants? • Grammatical roles? • Semantic roles? • Register/text-type association?

25. Hell for leather – with variants?

26. Invariant, adverbial (+ 1 adjectival)Semantic preference: go/drive/ride/head Rare: n = 10 All written

27. More hell • Word Query hell • hell-for-leather (5) • hell (random 100) • Sort left 2, right 2 • like hell

28. Like hell

29. Like hell • What do you do like hell? (152) • Combat/suffer/flee Fight / hurt / run • Denials/Contradictions: Like hell + pronoun + auxiliary • A much more interesting exercise on auxiliary verb use than generally proposed in textbooks

30. Example 4: Decide how (not) to start your next novel • The most frequent opening word is …?

31. Formulating the query Class: fiction and verse; No region catRef: Beginning sample|Whole text p (first paragraph)

32. The compleat query

34. The … + past VBD/VDD/VHD/VVD • The air hostess smiled. • The bodies were discovered at eight forty-five on the morning of Wednesday 18 September by Miss Emily Wharton, a 65-year-old spinster • The call had come at 6.12 precisely. • The castaways were lying together in the bilges of a cockboat when … • The cat had finished with its night hunt, and came padding silently back to its home territory. • The dawn was breaking as the cars rolled off the ferry at North Wall; … • The day was almost over before the young men made their move. • The gypsies arrived on Dartmoor early that year. • The house stood in a leafy street in the southern suburbs. • The injuries in the aftermath of the bomb explosion looked horrific. • The kitchen was full of the smells of baking. • The ladies of Tollemarche, Alberta, were always wonderfully clever at disposing of their menfolk; … • The lecture ended on a humorous note and, as the laughter and applause died away, Sophie Ferguson ..

35. and the runner up … It was • It was a pretty churchyard. • It was dark by the time he reached his destination. • It was dark. • It was dawn on the northern frontier of France; a border marked only by a shallow stream which ran between the stunted trunks of … • It was first love — there had been no time for earlier romance because Nicandra was only eight on April 8th 1904. • It was half-past midnight, and some time before sunrise Ebenezer Judge knew that he was going to die. • It was like grabbing a tiger by the tail! • It was the first dead body he had ever seen. • It was the pivotal teaching of Pluthero Quexos, the most celebrated dramatist of the Second Dominion, that in any fiction, no matter ... • It was the very first day of Mildred Hubble's second year at Miss Cackle's Academy for Witches.

36. And the learner task • Choose one opening, read the whole first chapter, and tell us what you think of it next time.