Information Technology Reference
In-Depth Information
ID Topic
Traditional wavelet
MLW
1
application
just for signals I
any text II
2 type of
transformation
mathematical
heuristic/statistical
3
goal
highlight, reinforce and obtain
further information that is not
readily available in the raw signal
extraction of information that
highlights and reinforce
knowledge that is not readily
available in the raw text
4 time-domain
signals
measured as a function of time.
They have not undergone any
transformation
they are analogous to the
knowledge structure model
(Hisgen, 2010). The sequence of
the sentences is essential for
contextualizing spoken/written
words
5 frequency-
domain signals
processed to transform then into a
useful representation
are the E ci , that represent sentence
content and retain its main
features
6
unit
Frequency: the number of the
oscillations per seconds in a signal,
measured in Hertz (Hz, cycles per
second)
E ci symbolizes morphosyntactic
representations of sentences
7
domain
any type of data, even with sharp
discontinuities III
any text
8 type of
information
can represent signal in both the
frequency and time domains III
also represents the time and
frequency dimensions IV
9 scaling role
important. Can process at different
scales and resolutions
represents knowledge at different
levels of abstraction and detail
10 data
decomposition
result
decompose data x(t) into a two-
dimensional function of time and
frequency
decompose data into E ci
(representation of
concrete/specific knowledge) and
E ce (abstract knowledge) V
decompose using morphosyntactic
rules and “mother sequence” of
filters
I. Detectable physical quantity or impulse by which information may be sent
II. Although this theory is explained in general, it has only been proved in Spanish
III. This is an advantage over the FFT alternative
IV. This is true within the MLW context, given the statements in rows 4 and 5
V. The knowledge derived from the filtering processing is called E ce in the MLW context
Table 1. Traditional wavelets versus MLW
11 data
decomposition
procedure
decompose x(t) using a “mother”
wavelet W(x)
Figure 2 shows a graphical comparison between a signal and its FFT. Figure 3 is a linguistic
version: E ci and ER. The graphics in Figure 2 represent the original signal (time-domain) and
the resulting FFT decomposition (Lahm, 2002). The images in Figure 3 represent a translated
original Spanish text (content from wikipedia.org, topic Topacio) transformed into an E ci
(López De Luise, 2007) that models dialog knowledge. (Hisgen, 2010) Statistical modeling of
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