class AAMethods:

'''Authorship attribution has been a long argued and sought after ability.
Chapter three focused on the mathematical methods involved with linguistics.
Language models are the main construct of spoken language. There are three
major models commonly used; Context-free grammars, Markov chains, and a “bag of
words”. CRGs allow symbols to represent specific strings and do a good job
obtaining long-distance dependencies. Markov chains models a word as a function
of several fixed adjacent words depending on the context and obtained
short-range dependencies but does not handle long-range. The most simple model
is a list of words acquired from a document. The chapter also comments on
distributions in language and the Zipf distribution approximation. Markedness is
the idea that the most common expressions are the shortest if a common word is
the shortest. Information theory is also covered with entropy and cross-entropy,
where entropy measured the amount of information available in text and compares
it and cross-entropy estimates the distribution of text. The Kolmogorov
complexity is used to attempt to solve the issue of identifying the area of
interest in a text. It tells you how much information is in a text based on how
complicated the algorithm was to describe it. A “Theoretical Framework” is given
in three divisions; Canonization, the idea that two areas of text will never be
identicle, determining the area of text to analyze, and analyzing the text with
statistics. Chapter five covers different analysis methods. Supervised analysis
is analysis on documents that are ordered while unsupervised analysis is on
unordered documents. Vector spaces and PCA are unsupervised and analyze authors
by determining word frequencies. Multidimensional Scaling is also unsupervised
and calculates distances through text differences. MDS defines a dimension and
places object in this dimension so that the objects can be analyzed. Cluster
analysis is the last unsupervised analysis mentioned. It is similar to MDS in
calculating distances but groups pairs together that closest and combines them
into a new object. Linear discriminant analysis is a supervised analysis and
attempts to determine the differences in categories or identify where to
separate categories. Both chapters cover a basic understanding of methods used
for authorship attribution, and the analysis of documents.'''
