Information Technology Reference
In-Depth Information
4.11 The representation in MLW
We do not expect E
ce
content to be understood from the human point of view, but it should
be considered a tool to condense and potentially regenerate knowledge from textual
sources. This is a first step in the study of this type of tool that uses mathematical and
statistical extraction of knowledge to automatically decompose text and represent it in a self-
organizational approach.
For instance, the following sentence from the dataset,
“Dactylorhiza incarnata es orquídea de especies Europeas”
(Dactylorhiza incarnata is an European orchid species)
corresponds to the EBH number 04, and can be found (after MLW) as the sequence E
ce
1-
E
ce
1,2-E
ci
4.
If there is an interest in understanding the topic, the main entry of the set of E
ci
s in the
cluster can be used as a brief description. To regenerate the concepts saved in the structure
for human understanding, it is only necessary to use the symbolic representation of the E
ci
(López De Luise, 2007).
5. Conclusion
MLW is a new approach that attempts to model natural language automatically, without the
use of dictionaries, special languages, tagging, external information, adaptation for new
changes in the languages, or other supports. It differs from traditional wavelets in that it
depends on previous usage, but it does not require human activities to produce definitions
or provide specific adaptations to regional settings. In addition, it compresses the original
text into the final E
ci
. However, the long-term results require further testing, both to further
evaluate MLW and to evaluate the correspondence between human ontology and
conceptualization and the E
ce
s sequence .
This approach can be completed with the use of a p
o
weighting to filter the results of any
query or browsing activity according to quality and to detect additional source types
automatically.
It will also be important to test the use of categorical metrics for fuzzy filters and to evaluate
MLW with alternate distances, filter sequences and cohesiveness parameters.
6. References
(Altmann,2004) E.G. Altmann, J.B. Pierrehumbert & A.E. Motter. Beyond word frequency:
Bursts, lulls, and scaling in the temporal distributions of words.
PLoS ONE
4(11):
e7678. ISSN 1932-6203.
(Brillouin, 2004) L. Brillouin. La science et la théorie de l'information. Masson, Paris.
Open
Library.
ISBN 10-2876470365.
(Chen, 2008) K. Chen, J. Li. Research on Fuzzy MCDM Method based on Wavelet Neural
Network Model.
Information Sci. And Eng
. ISISE'08. 2008. ISBN: 978-0-7695-3494-7.
(Clements, 1985) G.N. Clements. The Geometry of Phonological Features.
Phonology Yearbook
2. pp. 225 - 252. ISBN 9780521332323 . USA.
