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【正文】 Making statistic claims Corpus Linguistics Richard Xiao Update on assignments ? Deadline for submission ( submission) – TBA ? The Harvard referencing style ? Assignment A – Corpus study: introduction。 synopsis / overview, critical review of data, method of analysis, conclusion etc。 conclusions, bibliography ? CL2021: ? CL2021: ? UCCTS2021: ? UCCTS2021: – Corpus tool: Introduction。 description of the tool, its main features and functions。 your critical evaluation of the tool: how well it does the jobs it is supposed to do。 user interface, powerfulness, etc。 conclusions。 bibliography ? Assignment B – Introduction。 literature review。 methodology。 results and discussions。 conclusions。 bibliography ? Option B: A 3,500word essay, similar to Assignment B Outline of the session ? Lecture – Raw and normalised frequency – Descriptive statistics (mean, mode, media, measure of dispersion) –Inferential statistics (chi squared, LL, Fisher’s Exact tests) – Collocation statistics ? Lab – UCREL online LL calculator –Xu’s LL calculator – SPSS Quantitative analysis ? Corpus analysis is both qualitative and quantitative ? One of the advantages of corpora is that they can readily provide quantitative data which intuitions cannot provide reliably ? “The use of quantification in corpus linguistics typically goes well beyond simple counting” (McEnery and Wilson 2021: 81) – What can we do with those numbers and counts? Raw frequency ? The arithmetic count of the number of linguistic feature (a word, a structure etc) ? The most direct quantitative data provided by a corpus ? Frequency itself does NOT tell you much in terms of the validity of a hypothesis – There are 250 instances of the f**k swearword in the spoken BNC, so what? ? Does this mean that people swear frequently – or infrequently – when they speak? Normalized frequency ? …in relation to what? – Corpus analysis is inherently parative ? There are 250 instances of the swearword in the spoken BNC and 500 instances in the written BNC – Do people swear twice as often in writing as in speech? ? Remember the written BNC is 9 times as large as the spoken BNC ? When paring corpora of different sizes, we need to normalize the frequencies to a mon base (. per million tokens) – Normalised freq = raw freq / token number * mon base – The swearword is 4 times as frequent in
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