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The British National Corpus: where did we go wrong?

The British National Corpus: where did we go wrong?. Lou Burnard H UMANITIES C OMPUTING U NIT Oxford University Computing Services http://info.ox.ac.uk/bnc/ . What is the BNC?. 100 million words of modern British English

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The British National Corpus: where did we go wrong?

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  1. The British National Corpus:where did we go wrong? Lou Burnard HUMANITIES COMPUTING UNIT Oxford University Computing Services http://info.ox.ac.uk/bnc/

  2. What is the BNC? • 100 million words of modern British English • produced by a consortium of dictionary publishers and academic researchers • OUP, Longman, Chambers • Oxford, Lancaster, British Library • funded as pre-competitive resource by DTI/ SERC under JFIT 1990-1994

  3. Where did we go wrong? • (if we did) • or, The Benefit of Hindsight • or, If I'd known then what I know now... • or, Wisdom After the Event • And, Where Do We Go From Here?

  4. Production of the BNC • took three years (at least) • cost GBP 1.6 million (at least) • came about through an unusual coincidence of interests amongst: • Lexicographical publishers • Government (DTI) • Engineering and Science Research Council

  5. The Neotenous Nineties • WinWord or WP5? the choice is yours • On your desk … a 386 with 50 Mb diskspace (just about enough to run Windows 3) • In your lab ... a VAX or a Sparc for serious work • On the WWW (maybe) ... Mosaic for X

  6. Intellectual currents • corpus linguistics • the LOB school • the Birmingham school • the LDC view • text encoding theory • language engineering • the JFIT mentality, or Reconciling Town and Gown

  7. Stated Project Goals • A synchronic (1990-4) corpus of samples both spoken and written from the full range of British English language production • of non-opportunistic design, for generic applicability • with word class annotation • and contextual information

  8. Actual (?) project goals • Better ELT dictionaries • authoritative • both speech and writing • A model for European corpus work • design, and encoding • Industrial-academic co-operation • A REALLY BIG corpus

  9. Consequences • industrial scale text production system • compromises in design and execution • IPR and profitability The BNC looks back to Brown and LOB in its design and markup, and forward to the Web in its scope and indeterminacy

  10. Selection, clearance, and capture OUP Spoken (Longman) Written (OUP/Chambers) Enrichment and encoding Word Class Annotation (UCREL) Initial CDIF Conversion and Validation (OUCS) Header generation and final validation (OUCS) Documentation, distribution, maintenance The BNC “sausage machine”

  11. Task groups • permissions • selection, design criteria • encoding and markup • enrichment and annotation • retrieval software

  12. Through-put (million words/quarter)

  13. Tensions • desire to test annotation scheme • requirement to meet deliverables • slipping goal posts • quantity above quality • … an interesting learning experience for both sides!

  14. BNC Selection Criteria • Written selection criteria • predefined proportions of • different media (books, newspapers, unpublished…) • different domains (informative, entertaining…) • maximum sample size 45000 words • all texts incomplete • Spoken selection criteria • context-governed • demographically-sampled

  15. Word tagging • word-pos pair • white space problems • validation problems <s n=00011> <w AT0>The <w NP0>Queen<w POS>‘s <w AJ0>real <w NN1>annus horribilis <w VVD>began <w PRP> <w NN0>Sunday<c PUN>.</s>

  16. Sample written text <text complete=Y decls='CN000 HN001 QN000 SN000'> <div1 complete=Y org=SEQ> <head type=MAIN> <s n=001> <w NP0>CAMRA <w NN1>FACT <w NN1>SHEET <w AT0>No <w CRD>1 </head> <head r=it type=SUB> <s n=002><w AVQ>How <w NN1>beer <w VBZ>is <w AJ0-VVN>brewed </head> <p><s n=003> <w NN1>Beer <w VVZ>seems <w DT0>such <w AT0>a <w AJ0>simple <w NN1>drink <w CJT>that <w PNP>we <w VVB>tend <w TO0>to <w VVI>take <w PNP>it <w CJS-PRP>for <w VVD-VVN>granted <c PUN>.

  17. Transcription practice • Regionalised typists • Markup makes explicit • changes of speaker and overlap • words as perceived by transcriber • plus indications of false starts, truncation, uncertainty • some performance features e.g. pausing, stage directions etc. • speaker details where available (always for respondents, sometimes for others)

  18. Sample spoken text <u who=PS04Y> <s n=01296><w ITJ>Mm <pause> <w ITJ>yes <pause dur=7> <w PNP>I <w VVD>told <w NP0>Paul <pause> <w CJT>that <w PNP>he <w VM0>can <w VVI>bring <w AT0>a <w NN1>lady <w AVP>up <pause> <w PRP>at <w NN1>Christmas-time<c PUN>.</u> <u who=PS04U> <s n=01297><w VBZ>Is <w PNP>he <w XX0>not <w VVG>going <w AV0>home <w AV0>then<c PUN>?</u> <u who=PS04Y> <s n=01298><w ITJ>No <pause dur=8> <w CJC>and <w UNC>erm <pause dur=7> <w PNP>I<w VBB>'m <w VVG>leaving <w AT0>a <w NN1>turkey <w PRP>in <w AT0>the <w NN1>freezer<c PUN> <s n=01299><w NP0>Paul <w VBZ>is <w AV0>quite <w AJ0>good <w PRP>at <w NN1-VVG>cooking <pause> <w AJ0>standard <w NN1>cooking<c PUN>.</u>

  19. Metadata • each text has a TEI header • identification and classification • specific details (e.g. speakers) • housekeeping information • all common data in the corpus header • classification(s) in header pointed to by individual texts

  20. Text classifications • spoken texts • age, sex, class (of respondent) • domain, region, type • written texts • author age, sex, type • audience, circulation, status • medium, domain • Intention was to improve coverage, not accessibility

  21. In retrospect… • Some classifications were poorly defined and only partially populated • Domain or text-type? • Dating • date of copy? first publication? • Author age • when? • Author ethnic origin, domicile

  22. That famous BNC balance BNC-1

  23. That famous BNC balance BNC-W

  24. Written Domains BNC-1

  25. Written Domains BNC-2

  26. Written Domains

  27. Spoken domains

  28. Availability • BNC end-user licence • commercial exploitation of the corpus is forbidden • commercial exploitation of derived works is permitted • OUCS is sole agent for licensing, reporting to Consortium • Original restriction to EU has been lifted

  29. Distribution methods • 100 million words is (still) a lot of data • IPR agreements imply not-for-profit distribution • (which has its downsides too) • The options are... • install it yourself • online access • the sampler

  30. Install it yourself (version 1) • You need... • £220 for a licence and 3 CDs • £2000 for a Unix box with min 6 Gb disk • some Unix expertise • You get... • access to the whole corpus • using the tools of your choice • configurable for a local network Version 2 will be delivered to run “standalone” on a suitably configured PC

  31. BNC Online service • You need... • access to the Internet • You get... • free (but limited) access using any web browser • free (temporary) access using SARA (PC only) • for an annual fee, SARA plus documentation http://sara.natcorp.ox.ac.uk

  32. Accesses per month

  33. The BNC Sampler • You need... • $50 for a CD • A PC with a CD drive and (preferably) 90 Mb disk space • You get... • 2% sample, half written, half spoken • four different search engines • documentation Available at this conference, at a special price !!

  34. The BNC World Edition (aka BNC2) • has IPR clearance for world usage (we lose about 50 texts) • extensive set of revisions and corrections • catching up with the standards • accompanied with new enhanced version of SARA … and it’s nearly ready (honest)

  35. Error correction issues • Nothing can be added • Catching up with the standards • CDIF … TEI … EAGLES… CES … • headers are now in TEI-conformant XML • Indeterminacy of any transcription • On the scale of the BNC, especially • If seven maids with seven mops…

  36. Error Corrections in BNC2 • POS correction • Systematic • uses improved rules derived from BNC Sampler • significantly reduced error rate and indeterminacy • Major production errors fixed • Semi-systematic • duplicate texts • wrongly labelled texts • participant details • classification errors and lacunae • Typos remain... and will do so!

  37. The BNC as an Open Corpus • We chose SGML to encourage development of other tools • This is coming more slowly than we expected,e.g. the Sampler • But people still think the BNC and SARA are the same thing

  38. New features in SARA • POS code searches • Collocation searches • Subcorpora • Lemmatization rules • Usable with any TEI conformant corpus

  39. What lessons have we learned? • know your audience • technological blindspots • missed opportunities

  40. Know your audience • Everyone knows you should research the market first... • small, specialist research community, lexicographers • The actual market is immense: • language learners • applied linguists • cultural historians • and technically unsophisticated • hence often misled or disappointed

  41. Technological blind spots • we didn't expect the XML revolution! • so we wasted time in format conversion and compromises • we didnt foresee pcs with 8Gb disks and sound cards! • so we didn’t try to get rights to the audio • and we focussed efforts on developing a client/server application

  42. Missed opportunities: the R-word • Original design talks of Representativeness • This shifted to the idea of the BNC as a "fonds" : a source of specialist corpora • This implies • a clearer and agreed taxonomy of text types • better access facilities for subcorpora

  43. Missed opportunities: watching the river flow • The BNC as a monitor corpus • Diachronic sampling • But this implies a constant ability to fund and integrate • How long will we want to study the language of the nineties? • Will the web provide?

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