Decoding natural-language text

The main problem in natural-language text decoding is dealing with homographs, whether tiiey be accidental, part-of-speech or abbreviation homographs. Many systems choose to handle each of these separately, and in particular abbreviation homographs are often dealt with at the same time or in the same module as semiotic classification. 

The task of text decoding is to take a tokenised sentence and determine the best sequence of words. In many situations this is a classical disambiguation problem there is one, and only one correct sequence of words which gave rise to the text, and it is our job to determine this. In other situations, especially where we are dealing with non-natural language text such as numbers and dates and so on, there may be a few different acceptable word sequences. 

The final step in handling non-natural language text is to convert it into words and this process is often called verbalisation. If we take our decoded date example, we see that we have values for... 

We argue that there are a number of advantages to the text decoding model used here. Taking the issue of natural language decoding first, we see that this follows the information theoretic... 

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