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Workshop: Corpus (1) What might a corpus of spoken data tell us about language?

Workshop: Corpus (1) What might a corpus of spoken data tell us about language?. OLINCO 2014 Olomouc, Czech Republic, June 7. Sean Wallis Survey of English Usage University College London s.wallis@ucl.ac.uk. Outline. What can a corpus tell us? The 3A cycle

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Workshop: Corpus (1) What might a corpus of spoken data tell us about language?

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  1. Workshop: Corpus (1)What might a corpus of spoken data tell us about language? OLINCO 2014 Olomouc, Czech Republic, June 7 Sean Wallis Survey of English Usage University College London s.wallis@ucl.ac.uk

  2. Outline • What can a corpus tell us? • The 3A cycle • What can a parsed corpus tell us? • ICE-GB and DCPSE • Diachronic changes • Modal shall/will over time • Intra-structural priming • NP premodification • The value of interaction evidence

  3. What can a corpus tell us? • Three kinds of evidence may be obtained from a corpus • Frequency(distribution) evidence of a particular known linguistic event • Coverage (discovery) evidence of new events • Interaction evidence of the relationshipbetween events • But if these ‘events’ are lexical, this evidence can only really tell us about lexis • So corpus linguistics has always involved annotation

  4. The 3A cycle • Plain text corpora • evidence of lexical phenomena Text

  5. The 3A cycle • Plain text corpora • evidence of lexical phenomena • Need to annotate • add knowledge of frameworks • classify and relate phenomena • general annotation scheme • not focused on particular research goals Corpus Annotation Text

  6. The 3A cycle • Plain text corpora • evidence of lexical phenomena • Need to annotate • add knowledge of frameworks • classify and relate phenomena • general annotation scheme • not focused on particular research goals • Corpus research = the ‘3A’ cycle • Annotation Corpus Annotation Text

  7. The 3A cycle • Plain text corpora • evidence of lexical phenomena • Need to annotate • add knowledge of frameworks • classify and relate phenomena • general annotation scheme • not focused on particular research goals • Corpus research = the ‘3A’ cycle • AnnotationAbstraction Dataset Abstraction Corpus Annotation Text data transformation (“operationalisation”)

  8. The 3A cycle Hypotheses • Plain text corpora • evidence of lexical phenomena • Need to annotate • add knowledge of frameworks • classify and relate phenomena • general annotation scheme • not focused on particular research goals • Corpus research = the ‘3A’ cycle • AnnotationAbstractionAnalysis Analysis Dataset Abstraction Corpus Annotation Text data transformation (“operationalisation”)

  9. Annotation  Abstraction • Abstraction • selects data from annotated corpus • maps it to a regular dataset for statistical analysis • bi-directional (“concretisation”) • allows us to interpret statistically significant results

  10. Annotation  Abstraction • Abstraction • selects data from annotated corpus • maps it to a regular dataset for statistical analysis • bi-directional (“concretisation”) • allows us to interpret statistically significant results • Even ‘lexical’ questions need annotation: • 1st person declarative modal verbshall/will abstraction relies on annotation

  11. What can a parsedcorpus tell us? • Three kinds of evidence may be obtained from a parsed corpus • Frequencyevidence of a particular known rule, structure or linguistic event • Coverage evidence of new rules, etc. • Interaction evidence of the relationshipbetween rules, structures and events • BUT evidence is necessarily framed within a particular grammatical scheme • So… (an obvious question) how might we evaluate this grammar?

  12. What can a parsed corpus tell us? • Parsed corpora contain (lots of) trees • Use Fuzzy Tree Fragment queries to get data • An FTF

  13. What can a parsed corpus tell us? • Parsed corpora contain (lots of) trees • Use Fuzzy Tree Fragment queries to get data • An FTF • A matchingcase in a tree • UsingICECUP(Nelson et al, 2002)

  14. What can a parsed corpus tell us? • Trees as handle on data • make useful distinctions • retrieve cases reliably • not necessary to “agree” to framework used • provided distinctions are meaningful

  15. What can a parsed corpus tell us? • Trees as handle on data • make useful distinctions • retrieve cases reliably • not necessary to “agree” to framework used • provided distinctions are meaningful • Trees as trace of language production process • interaction between decisions leave a probabilistic effect on overall performance • not simple to distinguish between source • depends on the framework • but may also validate it

  16. Why spoken corpora? • Speech predates writing • historically – literacy growth and spread • child development – internal speech during writing

  17. Why spoken corpora? • Speech predates writing • historically – literacy growth and spread • child development – internal speech during writing • Scale • professional authors recommend 1,000 words/day • 1 hour of speech  8,000 words (DCPSE)

  18. Why spoken corpora? • Speech predates writing • historically – literacy growth and spread • child development – internal speech during writing • Scale • professional authors recommend 1,000 words/day • 1 hour of speech  8,000 words (DCPSE) • Spontaneity • production process lost: many written sources edited

  19. Why spoken corpora? • Speech predates writing • historically – literacy growth and spread • child development – internal speech during writing • Scale • professional authors recommend 1,000 words/day • 1 hour of speech  8,000 words (DCPSE) • Spontaneity • production process lost: many written sources edited • Dialogue • interaction between speakers

  20. ICE-GB and DCPSE • British Component of the International Corpus of English (1990-92) • 1 million words (nominal) • 60% spoken, 40% written • speech component is orthographically transcribed • fully parsed • marked up, POS-tagged, parsed, hand-corrected • Diachronic Corpus of Present-day Spoken English • 800,000 words (nominal) • orthographically transcribed and fully parsed • created from subsamples of LLC and ICE-GB • Matching numbers of texts in text categories • Not sampled over equal duration • LLC (1958-1977) – ICE-GB (1990-1992)

  21. Modal shall vs. will over time • Plotting modal shall/will over time (DCPSE) • Small amounts of data / year 1.0 p(shall | {shall, will}) 0.8 0.6 0.4 0.2 0.0 1955 1960 1965 1970 1975 1980 1985 1990 1995

  22. Modal shall vs. will over time • Plotting modal shall/will over time (DCPSE) • Small amounts of data / year • Confidence intervals identify the degree of certainty in our results 1.0 p(shall | {shall, will}) 0.8 0.6 0.4 0.2 0.0 1955 1960 1965 1970 1975 1980 1985 1990 1995

  23. 1.0 p(shall | {shall, will}) 0.8 0.6 0.4 0.2 0.0 1955 1960 1965 1970 1975 1980 1985 1990 1995 Modal shall vs. will over time • Plotting modal shall/will over time (DCPSE) • Small amounts of data / year • Confidence intervals identify the degree of certainty in our results • Highly skewed p in some cases • p = 0 or 1 (circled)

  24. 1.0 p(shall | {shall, will}) 0.8 0.6 0.4 0.2 0.0 1955 1960 1965 1970 1975 1980 1985 1990 1995 Modal shall vs. will over time • Plotting modal shall/will over time (DCPSE) • Small amounts of data / year • Confidence intervals identify the degree of certainty in our results • We can now estimate an approximate downwards curve (Aarts et al., 2013)

  25. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head N

  26. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head • a single additive step applied to this structure • e.g. add an attributive AJP before the head N AJP

  27. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head • a single additive step applied to this structure • e.g. add an attributive AJP before the head • Q. What is the effect of repeatedly applying this operation to the structure? N AJP N ship

  28. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head • a single additive step applied to this structure • e.g. add an attributive AJP before the head • Q. What is the effect of repeatedly applying this operation to the structure? N AJP AJP N tall ship

  29. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head • a single additive step applied to this structure • e.g. add an attributive AJP before the head • Q. What is the effect of repeatedly applying this operation to the structure? N AJP AJP AJP N tall very green ship

  30. Intra-structural priming • Priming effects within a structure • Study repeating an additive step in structures • Consider • a phrase or clause that may (in principle) be extended ad infinitum • e.g. an NP with a noun head • a single additive step applied to this structure • e.g. add an attributive AJP before the head • Q. What is the effect of repeatedly applying this operation to the structure? N AJP AJP AJP N AJP tall very green ship old

  31. probability 0.20 0.15 0.10 0.05 0.00 0 1 2 3 4 5 NP premodification • Sequential probability analysis • calculate probability of adding each AJP • error bars: Wilson intervals • probabilityfalls • second < first • third < second • decisions interact • Every AJP addedmakes it harderto add another

  32. NP premodification: explanations? • Feedback loop: for each successive AJP, it is more difficult to add a further AJP • Possible explanations include: • logical and semantic constraints • tend to say the tall green ship • do not tend to say tall short shipor green tall ship • communicative economy • once speaker said tall green ship, tends to only say ship • memory/processing constraints • unlikely: this is a small structure, as are AJPs

  33. 0.25 0.20 0.15 0.10 0.05 0.00 0 1 2 3 4 5 NP premod’n: speech vs. writing • Spoken vs. written subcorpora • Same overall pattern • Spoken data tends to have fewer attributive AJPs • Support for communicative economy or memory/processing hypotheses? • Significance tests • Paired 2x1 Wilson tests (Wallis 2011) • first and secondobserved spokenprobabilities are significantly smallerthan written probability written spoken

  34. Potential sources of interaction • shared context • topic or ‘content words’ (Noriega) • idiomatic conventions • semantic ordering of attributive adjectives (tall green ship) • logical-semantic constraints • exclusion of incompatible adjectives (?tall short ship) • communicative constraints • brevity on repetition (just say ship next time) • psycholinguistic processing constraints • attention and memory of speakers

  35. What use is interaction evidence? • Corpus linguistics • Optimising existing grammar • e.g. co-ordination, compound nouns • Theoretical linguistics • Comparing different grammars, same language • Comparing different languages or periods • Psycholinguistics • Search for evidence of language production constraints in spontaneous speech corpora • speech and language therapy • language acquisition and development

  36. What can a parsed corpus tell us? • Trees as handle on data • make useful distinctions • retrieve cases reliably • not necessary to “agree” to framework used • provided distinctions are meaningful • Trees as trace of language production process • interaction between decisions leave a probabilistic effect on overall performance • not simple to distinguish between source • results enabled by the framework • but may also validate it

  37. The importance of annotation • Key element of a ‘3A cycle’ • AnnotationAbstractionAnalysis • Richer annotation • more effective abstraction • deeper research questions? • Multiple layers of annotation • new research questions • studying interaction between layers • Algorithmic vs. human annotation

  38. More information • References Aarts, B. Close, J. and Wallis, S.A. (2013) Choices over time: methodological issues in current change. In Aarts, Close, Leech and Wallis (eds)The Verb Phrase in English. Cambridge University Press. Nelson, G., Wallis, S.A. and Aarts, B. (2002) Exploring Natural Language. Amsterdam: John Benjamins. Wallis, S.A. (2011) Comparing χ2 tests for separability. London: Survey of English Usage. • Useful links • Survey of English Usage • www.ucl.ac.uk/english-usage • Fuzzy Tree Fragments • www.ucl.ac.uk/english-usage/resources/ftfs • Statistics and methodology research blog • http://corplingstats.wordpress.com

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