The Implications of Revisions for Natural Language Generation
Analysis of naturally occurring data is essential for developing computational models of language processes. In this paper, I describe the results of my analysis of revised texts and show how generalizations of the data can contribute to the development of an architecture for natural language generation. In particular, I look at how identifying classes of revisions can help in the design of intermediate levels of representation by determining the decision points necessary to permit the variations between the original and revised versions of the same passage to be generated. The work presented here summarizes the analysis of over 500 instances of revisions of technical papers made by professional editors.
KeywordsNoun Phrase Relative Clause Lexical Item Function Word Subordinate Clause
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