Introduction : corpora, corpus use, and the British National Corpus Dr. Ylva Berglund Prytz ylva. berglund@oucs. ox. ac. uk http: //www. natcorp. ox. ac. uk/
Outline Presentation: Corpora, corpus use, and the BNC Demonstration: How to use BNC with Xaira Hands-on: BNC with Xaira Presentation: Using the BNC for teaching and research More hands-on: exploring more Questions and answers
At the end of today you should have a basic working knowledge about corpora and corpus use the BNC Xaira feel confident using Xaira be able to explore area on your own know where to turn for help and advice
Approaches to linguistic study Intuition • “Feel” what is right/wrong/possible • One person’s language • Subjective Study of usage • Examine what is actually said/written • Several people • Objective
How do you study usage? Examine naturally occurring language Draw conclusions Need a sample of language, produced by different people in various contexts Find a corpus!
What is a corpus? A collection of naturally occurring language data compiled to mirror a language/language variety (Usually) computer-readable (Usually) contains more than text (annotation, meta-data)
What is a corpus? – some definitions A corpus is a collection of naturally-occurring language text, chosen to characterise a state or variety of language. (Sinclair 1991: 171) A corpus can be defined as a collection of texts assumed to be representative of a given language. (Tognini-Bonelli 2001: 2) All the material included in a corpus, whether spoken, written […] is assumed to be taken from genuine communications of people going about their normal business. (ibid: 55)
How can a corpus help? Look for patterns to see regularities Quantify See several examples Real language – language in use Based on a variety of sources
Types of corpora • Balanced corpora (= Reference or general corpora) • Specialised corpora ® Genre-specific, LSP (e. g. English for Academic Purposes) … ® Varieties (dialectal, social, historical) ® Learner language, English as a Lingua Franca • Multilingual corpora ® Parallel corpora (translations; alignable) ® Comparable corpora (similar texts) • Fixed size / monitor corpora • Mode and medium ® Written, spoken and transcribed, spoken with audio, video
Famous corpora Brown family (Brown, LOB, FLOB) 1 million words, different text categories Bank of English Monitor corpus, grows with time International Corpus of English (ICE) Different national varieties of English. 1 million words written and spoken British National Corpus Reference corpus, fixed, 100 million words, written and spoken
British National Corpus (BNC)
What is the BNC? A snapshot of British English, taken at the end of the 20 th century 100 million words in approx 4, 000 different text samples, both spoken (10%) and written (90%) Synchronic (1960 -93), sampled, general purpose corpus Available under licence; latest edition is BNC XML edition (March 2007)
More than text Metadata About text, author/speaker, audience Structural & typographical information Paragraph, sentence, heading, list, bolds Extra-linguistic information Voice quality, noise, pauses, overlap Linguistic information Part-of-speech
Who produced the BNC and why? a consortium of dictionary publishers and academic researchers OUP, Longman, Chambers OUCS, UCREL, BL R&D with funding from DTI/ SERC under JFIT 19901994 Lexicographers, NLP researchers, But not language teachers!
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
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
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
Project consequences industrial-scale text production system necessary compromises? technically over-ambitious? 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
How was the corpus created?
How was the corpus created? 1. 2. 3. 4. 5. 6. 7. 8. Corpus design Text selection Clearance Capture Add additional information Merge (documentation) Distribution
The BNC “sausage machine” Written OUP (OUP/Chambers ) Spoken (Longman) Selection, clearance, and capture Enrichment and encoding Initial CDIF Conversion and Validation (OUCS) Word Class Annotation (UCREL) Header generation and final validation (OUCS) Documentation, distribution, maintenance
Text selection 1. Design criteria 2. Types of texts Sources Number of samples Size of samples Descriptive criteria Additional information where available
Selection criteria: written texts Domain imaginative (c 25%) informative Medium Book, periodicals, misc. published, unpublished, written to be spoken Time 1985 -1993 (1960 -75, 1975 -84)
“Descriptive” criteria: written texts Sample size (number of words) and extent (start and end points) Topic or subject of the text Author's name, age, gender, region of origin, and domicile Target age group and gender "Level" of writing (reading difficulty) : the more literary or technical a text, the "higher" its level
Selection criteria: spoken texts demographic (spoken conversation) transcriptions of spontaneous natural conversations made by recruited volunteers original recordings are available from British Library context-governed (other spoken material) transcriptions of recordings made at specific types of meeting and event.
Spoken texts: context-governed Four broad categories of social context: • Educational and informative events, such as lectures, news broadcasts, classroom discussion, tutorials • Business events such as sales demonstrations, trades union meetings, consultations, interviews • Institutional and public events, such as sermons, political speeches, council meetings • Leisure events, such as sports commentaries, afterdinner speeches, club meetings, radio phone-ins
Descriptive criteria: spoken texts Features relating to the speaker (age, sex, social class, dialect) Context of recording (place, time) Features of the recording (non-verbal events, paralinguistic phenomena, unclear instances) Included when known Sometimes provided by respondent
What is the result?
What is the BNC? 4, 000+ texts Ca. 100, 000 words 10% spoken Information about the texts the speakers/writers the words Delivered with a search tool: XAIRA
What's in the BNC?
What topics?
Post-hoc text-type classification
Format Corpus header (1) Corpus texts (4, 000+) Text header Text <corpus> <corpus. Header></corpus. Header> <corpus. Text> <text. Header></text. Header> <text></text> </corpus. Text> … </corpus>
Annotation, encoding, markup • A means of making explicit, and thus processable: structure • texts, sections, paragraphs, turns, sentences, words. . . metadata • text-type, situational parameters, context analysis • morphology, syntactic function, translation
Word class annotation CLAWS (Leech, Garside et al) approach What counts as a word? This isn't prima facie obvious, in spite of spelling conventions. In BNC-XML, each word is explicitly marked annotated with a root form or lemma an automatically assigned C 5 word class code a simplified POS code
Example: word class annotation <s n="11"><w c 5="NN 1" hw="difficulty" pos="SUBST">Difficulty </w><w c 5="VBZ" hw="be" pos="VERB">is </w><w c 5="VBG" hw="be" pos="VERB">being </w><w c 5="VVN" hw="express" pos="VERB">expressed </w><w c 5="PRP" hw="with" pos="PREP">with </w><w c 5="AT 0" hw="the" pos="ART">the </w><w c 5="NN 1" hw="method" pos="SUBST">method </w><w c 5="TO 0" hw="to" pos="PREP">to </w><w c 5="VBI" hw="be" pos="VERB">be </w><w c 5="VVN" hw="use" pos="VERB">used </w><w c 5="TO 0" hw="to" pos="PREP">to </w><w c 5="VVI" hw="launch" pos="VERB">launch </w><w c 5="AT 0" hw="the" pos="ART">the </w><w c 5="NN 1" hw="scheme" pos="SUBST">scheme</w><c c 5="PUN">. </c></s>
<s n="11"> <w c 5="NN 1" hw="difficulty" pos="SUBST">Difficulty </w> <w c 5="VBZ" hw="be" pos="VERB">is </w> <w c 5="VBG" hw="be" pos="VERB">being </w> <w c 5="VVN" hw="express" pos="VERB">expressed </w> <w c 5="PRP" hw="with" pos="PREP">with </w> <w c 5="AT 0" hw="the" pos="ART">the </w> <w c 5="NN 1" hw="method" pos="SUBST">method </w> <w c 5="TO 0" hw="to" pos="PREP">to </w> <w c 5="VBI" hw="be" pos="VERB">be </w> <w c 5="VVN" hw="use" pos="VERB">used </w> <w c 5="TO 0" hw="to" pos="PREP">to </w> <w c 5="VVI" hw="launch" pos="VERB">launch </w> <w c 5="AT 0" hw="the" pos="ART">the </w> <w c 5="NN 1" hw="scheme" pos="SUBST">scheme</w> <c c 5="PUN">. </c> </s> c 5 = detailed part-of-speech hw = head word (new) pos = simple part-of-speech (new)
Some BNC-XML elements <wtext> or <stext> <div> = section <p> = paragraph or <u> = utterance <s> = “sentence” <w> = word and <c> = punctuation <mw> = multiword unit
What is the markup for? It makes it possible for you to distinguish aids=SUBST from aids=VERB distinguish occurrences in writing from ones in speech distinguish occurrences in headings from ones in paragraphs identify contextual units like sentences and paragraphs FACTSHEET WHAT IS AIDS? AIDS (Acquired Immune Deficiency Syndrome) is a condition caused by a virus called HIV (Human Immuno Deficiency Virus).
Who uses the BNC (and how? ) Linguists Research on (English) language Teachers Lexicographers Publishers. NLP researchers Reference, Generate teaching materials, In classroom Dictionaries, EFL text books Language engineers Language + computer tools, AI, NLP Students/language learners Computer scientists Information retrieval Psychologists/neurologists General ‘norm’ or reference
What makes the BNC so special? Size Design General availability Standardized markup system Structural annotation Word class annotation Contextual information Model for other projects. . . in these respects, the BNC remains distinctive, twenty years on!
How to use the BNC (with Xaira)
The BNC can be used in different ways and with different tools User needs to know What information is available Where/how is information coded XAIRA can help
Search for Words or phrases Word class information Annotation/mark-up or a combination of them
Display Search term with context with or without mark-up Information about text Collocations (co-occurring words) Distribution across parts of the corpus and much more
XAIRA – XML-aware retrieval application Searches an index of the corpus Uses information in the headers and the texts Often more than one way to make a search Can be used with other corpora (if they are indexed first)
Introduction : corpora, corpus use, and the British National Corpus Dr. Ylva Berglund Prytz ylva. berglund@oucs. ox. ac. uk http: //www. natcorp. ox. ac. uk/
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