Corpus-based lexical choice in natural language generation

Abstract

Choosing the best lexeme to realize a meaning in natural language generation is a hard task. We investigate different tree-based stochastic models for lexical choice. Because of the difficulty of obtaining a sense-tagged corpus, we generalize the notion of synonymy. We show that a tree-based model can achieve a word-bag based accuracy of 90%, representing an improvement over the baseline.