浙江大学肖忠华语料库session5(外语学习).ppt

Practice Use both the UCREL/Xu’s LL calculator / SPSS to determine if the difference in the frequencies of passives in the CLEC and LOCNESS corpora is statistically significant CLEC: 7,911 instances in 1,070,602 words LOCNESS: 5,465 instances in 324,304 words Collocation statistics Collocation: the habitual or characteristic co-occurrence patterns of words Can be identified using a statistical approach in CL, e.g. Mutual Information (MI), t test, z score Can be computed using tools like SPSS, Wordsmith, AntConc, Xaira Only a brief introduction here More discussions of collocation statistics to be followed Mutual information Computed by dividing the observed frequency of the co-occurring word in the defined span for the search string (so-called node word), e.g. a 4:4 window, by the expected frequency of the co-occurring word in that span and then taking the logarithm to the base 2 of the result Mutual information A measure of collocational strength The higher the MI score, the stronger the link between two items MI score of 3.0 or higher to be taken as evidence that two items are collocates The closer to 0 the MI score gets, the more likely it is that the two items co-occur by chance A negative MI score indicates that the two items tend to shun each other The t test Computed by subtracting the expected frequency from the observed frequency and then dividing the result by the standard deviation A t score of 2 or higher is normally considered to be statistically significant The specific probability level can be looked up in a table of t distribution The z score The z score is the number of standard deviations from the mean frequency The z test compares the observed frequency with the frequency expected if only chance is affecting the distribution A higher z score indicates a greater degree of collocability of an item with the node word LG3204 Corpus Linguistics 07-08 Making statistic claims Corpus Linguistics Richard Xiao lancsxiaoz@ Update on assignments Deadline