Dear Stefan and Bill,
Since Bill asked about wavelet in NLP applications, I don't think he has
coding in mind particularly. Correct me if I am wrong. I would also be
interested in knowing what Bill and others on the list might have in mind
for using wavelet in NLP.
Regards,
Pascale
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Prof. Pascale Fung
Department of Electronic & Computer Engineering
University of Science & Technology
Clear Water Bay, Kowloon
Hong Kong
http://www.ee.ust.hk/~pascale
pascale@ee.ust.hk
tel:+852 2358 8537
fax:+852 2358 1485
sec:+852 2358 7087
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> Thanks a lot for the detailed clarification. I've always been
> thinking of wavelet transforms as a "variant" of Fourier
> transformation, which is also (at least supposed to be) invertible in
> the continuous case.
>
> My impression from the original query was that the author is more
> interested in using for coding or data manipulation rather than just
> analysis, but this may be purely due to my Fourier-based
> perspective. :-)
>
> Best,
> Stefan
>
> On 10 Jun 2006, at 19:18, Pascale Fung wrote:
>
>> "Time frequency transformation" is basically wavelet transform.
>>
>> I think you are talking about discrete wavelet transform, which is
>> bijective, and used for source coding purposes. I used continuous
>> wavelet
>> transform, which is injective, and used for recognition (or analysis)
>> purposes.
>>
>> Discrete wavelet transform is used for coding purposes where you'd be
>> concerned with recovering the original signal. Whereas in the
>> application
>> of bilingual word translation, I was interested in recognizing the
>> patterns. I would say most NLP tasks are recognition rather than
>> coding
>> tasks.
>>
>> Nevertheless, in this particular recognition application (of bilingual
>> word pair extraction) you can still recover the orginal "signal"
>> from the
>> output of the transformation because the output can only correspond
>> to one
>> and only one input.
>>
>> regards,
>> Pascale
>>
>
>
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