Semiotic analysis

The limitations related to the applicability of Greimas model are twofold. First, as an anal5 tical tool, although Greimas model may appear clear and simple, it may prove to be rather complicated as there are no definitions to identify the actants. The construction of the relationships between the actantial units of the text is always relative, and depends on the researchers definitions. The semiotic analysis requires close assessment and a clear interpretation of the revealed meanings. However, despite the present limitations, the present results support the proposal that Greimas model can be used as a theoretical framework and as practical semiotic tool describing the corporate sustainability . 

There are two main ways to deal with this problem. The first is the text-normalisation approach, which sees the text as the input to the S5mthesiser and tries to rewrite any non-standard text as proper linguistic text. The second is to classify each section of text according to one of the known semiotic classes. From there, a parser specific to each classes is used to analyse that section of text and uncover the underlying form. For natural language the text analysis job is now done but for the other systems an additional stage is needed, where the underlying form is translated into words. 

The first stage in text analysis is therefore to perform semiotic classification which assigns a class to each token. This is performed by one of the above mentioned classification algorithms... 

Rgure 5.3 The flow of an entire text-analysis system (only four semiotic decoders/translators are shown for snccinctness). 

In coimnercial TTS, text analysis is seen as vitally important since any error can cause an enourmous negative impression witii a hstener. Despite this, the problem has received only sporadic attention amongst academic researchers. Notable exceptions include various works by Richard Sproat [410], [411], [412], [413], who tackles nearly all the problems in text analysis (and many in prosody, explained in file next chapter). The Bell Labs system tiiat Sproat worked on is particularly well known for its very high accuracy with regard to text processing. In a separate line of work Sproat et al. investigated a number of machine-learning approaches to semiotic classification and verbahsation. 

A Critical Analysis of the Semiotic Engineering Evaluation Methods... 

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