Information Technology Reference
In-Depth Information
characteristic
morphosyntactic
traditional wavelet
goal
content description and
classification with granularities
scaled decomposition
scaling
concept abstraction and
ontology classification
reduced and representative signal
Uses
Extract the main concept of a
signal
extract the main features of a
signal
summarization
compression
manage spelling and some
grammatical errors
De-noising
Complement knowledge
Reconstruct portions of
a corrupted signal
Types of wavelets
Depends on the specific
sequence of filters
depends on the functions
used as mother function
auto-fitting
Must be manually detected
according to results
Table 2. Characteristics of traditional wavelets and MLW
2.3 Linguistic cloud model and MLW
LCM models linguistic knowledge (Li, 2000) using a set of predefined, customized fuzzy
linguistic variables. These variables are generated in accordance with two rules:
1.
The atom generation rule
specifies the manner in which a linguistic “atom” may be
generated. An atom is a variable that cannot be sliced into smaller parts.
2.
The semantic rule
specifies the procedure by which composite linguistic terms are
computed from linguistic atoms. In addition, there are connecting operators (“and”
“or”, etc.), modifiers (“very” “quite”, etc.) and negatives that are treated as soft
operators that modify an operand's (atom's) meaning to produce linguistic “terms”.
The MSW and the LCM share a common goal. However, the MSW replaces the manual
procedure used to obtain linguistic atoms with automated processing that determines an
atom's linguistic category (e.g., noun or verb) (López De Luise, 2007d, 2008c). The result is
not an atom or a term but is a structure named E
ci
(an acronym from the Spanish, Estructura
de Composición Interna). The E
ci
is used to model the morphosyntactic configuration within
sentences (López De Luise, 2007; Hisgen, 2010). Thus, the core processing is based on E
ci
structures instead of linguistic variables. An E
ci
is a plastic representation that can evolve to
reflect more detailed information regarding the represented portion of text. While atoms
cannot be sliced, any E
ci
can be partitioned as required during the learning process. Further
differences between the LCM and the MSW are shown in Table 3.
2.4 Morphosyntactics as a goal
Most morphological and syntactical processing is intended for information retrieval, while
alignment supports automatic translation. Those approaches are mainly descriptive and are
defined by cross-classifying different varieties of features (Harley, 1994) such as number and
person. When morphological operations are an autonomous subpart of the derivation, they
acquire a status beyond descriptive convenience. They become linguistic primitives,
manipulated by the rules of word formation.
