Information Technology Reference
In-Depth Information
Morphosyntactic Linguistic Wavelets
for Knowledge Management
Daniela López De Luise
Universidad de Palermo
1. Introduction
Morphosyntactics studies grammatical categories and linguistic units that have both
morphological and syntactic properties. In its proscriptive form, morphosyntactics describes
the set of rules that govern linguistic units whose properties are definable by both
morphological and syntactic paradigms.
Thus, morphosyntactics establishes a commons framework for oral and written language
that guides the process of externally encoding ideas produced in the mind. Speech is an
important vehicle for exchanging thoughts, and phonetics also has a significant influence on
oral communication. Hearing deficiency causes a leveling and distortion of phonetic
processes and hinders morphosyntactic development, particularly when present during the
second and third years of life (Kampen, 2005).
Fundamental semantic and ontologic elements of speech become apparent though word
usage. For example, the distance between successive occurrences of a word has a distinctive
Poisson distribution that is well characterized by a stretched exponential scaling (Altmann,
2004). The variance in this analysis depends strongly on semantic type, a measure of the
abstractness of each word, and only weakly on frequency.
Distribution characteristics are related to the semantics and functions of words. The use of
words provides a uniquely precise and powerful lens into human thought and activity
(Altmann, 2004). As a consequence, word usage is likely to affect other manifestations of
collective human dynamics.
1.1 Words may follow Zipf's empirical law
Zipf's empirical law was formulated using mathematical statistics. It refers to the fact that
many types of data studied in the physical and social sciences can be approximated with a
Zipfian distribution, one of a family of related discrete power law probability distributions
(Figure 1) 1 (Wolfram, 2011).
1 In the English language, the probability of encountering the r th most common word is given roughly
by P(r)=0.1/r (r>1000).
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