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.
No takes yet. Share an insight, caveat, or question.
Bangalore et al. (2000) studied this question.