A Corpus Worker`s Toolkit：语料库工具箱-0908 更新
- 主题发起人 动态语法
- 时间
Thanks very much for including the aspect annotation scheme in the toolkit.
Here is my more recent research on using for and in-adverbials as telicity tests.
http://forum.corpus4u.org/upload/forum/2005091105172779.pdf
Here is my more recent research on using for and in-adverbials as telicity tests.
http://forum.corpus4u.org/upload/forum/2005091105172779.pdf
清风出袖
高级会员
I am soory to trouble you all with the question that in my latest uptodated version of ACWT there is no function of An Aspect Analyzer under the 03_DisTagas shown in the screenshot given by 动态语法. Do I miss any clips? Or the function is only available to those super members or the members interested in the function? Secondly, frankly speaking I haven't cleared up all my downloaded files for a couple of days. Today I sorted out one PERL PL file named as FORMAT. I don't know which directory it should go, C:\perl\bin or C:\perl\lib, and what does it concern? By the way the file was downloaded from September 14 seemingly, since the zip file from which I decompressed is read like 20050914 and so on. Thanks a lot for your help! Please extend my sincere wishes to your families over the occasion of tradtional Chinese Moon-Cake Festival! Thanks a lot!
回复：A Corpus Worker`s Toolkit：语料库工具箱-0908 更新
The best way to make sure that your files are up to date is to check page
9 of this thread, post #88. The files there are the current ones.
No, these files are not open to some users only. they are to be shared by everyone.
It's not my file but it is safe for you to put it in the \bin dir since that's
where the binary file resides.
Thank you for your kind message. Same to you and everyone visiting
this forum.
The best way to make sure that your files are up to date is to check page
9 of this thread, post #88. The files there are the current ones.
No, these files are not open to some users only. they are to be shared by everyone.
It's not my file but it is safe for you to put it in the \bin dir since that's
where the binary file resides.
Thank you for your kind message. Same to you and everyone visiting
this forum.
清风出袖
高级会员
just now I tried to figure out how to use the function of T-score calculation with ACWT. I tried two methods of comparing the likelihoods of association of words "strong" and "thick" with word "smell" by calculating them in the function of compute t-ccore and compute t-score by the ele method. yet the differences in the results often come from the order of the input, i.e. if I input strong in the box then , strong is more likely to asscoiate with word smell, if i input thick first then thick is more so! what is wrong with my calculation?
Result1(this is the most of the result I got when I input the strong first on the top box after I click the module compute t-score by ele method):
* 1st node word 'strong', N=7809
* 2nd node word 'thick', N=1984
* Collocate word 'smell', N=13428
* Frequency of 'strong smell'=175
* Frequency of 'thick smell'=2
* No. of words following either 'strong' or 'thick'=1841
* Corpus size = 44300000 words
Results( if I reverse the order of input, then the result is difeerent)
* 1st node word 'thick ', N=7809
* 2nd node word 'strong', N=1984
* Collocate word 'smell', N=13428
* Frequency of 'thick smell'=175
* Frequency of 'strong smell'=2
* No. of words following either 'thick ' or 'strong'=1841
* Corpus size = 44300000 words
T-Score by the (ELE) method: t=11.94
Hints: 'thick smell' is 11.94 standard deviations more likely than 'strong smell',
or,
'strong smell' is 11.94 standard deviations less likely than 'thick smell'.
According to Church et al, the confidence threshold should be at least 2.15 instead of 1.65.
T-Score by the (ELE) method: t=11.94
Hints: 'strong smell' is 11.94 standard deviations more likely than 'thick smell',
or,
'thick smell' is 11.94 standard deviations less likely than 'strong smell'.
Result1(this is the most of the result I got when I input the strong first on the top box after I click the module compute t-score by ele method):
* 1st node word 'strong', N=7809
* 2nd node word 'thick', N=1984
* Collocate word 'smell', N=13428
* Frequency of 'strong smell'=175
* Frequency of 'thick smell'=2
* No. of words following either 'strong' or 'thick'=1841
* Corpus size = 44300000 words
Results( if I reverse the order of input, then the result is difeerent)
* 1st node word 'thick ', N=7809
* 2nd node word 'strong', N=1984
* Collocate word 'smell', N=13428
* Frequency of 'thick smell'=175
* Frequency of 'strong smell'=2
* No. of words following either 'thick ' or 'strong'=1841
* Corpus size = 44300000 words
T-Score by the (ELE) method: t=11.94
Hints: 'thick smell' is 11.94 standard deviations more likely than 'strong smell',
or,
'strong smell' is 11.94 standard deviations less likely than 'thick smell'.
According to Church et al, the confidence threshold should be at least 2.15 instead of 1.65.
T-Score by the (ELE) method: t=11.94
Hints: 'strong smell' is 11.94 standard deviations more likely than 'thick smell',
or,
'thick smell' is 11.94 standard deviations less likely than 'strong smell'.
[本贴已被 作者 于 2005年09月20日 14时54分42秒 编辑过]