Ambiguity Management in Natural Language Generation

In natural language generation (NLG) it can be desirable to preserve some ambiguities rather than try to eliminate them. The question is, which ones are so liable to misinterpretation that they should be removed, and which can be allowed to remain. Our approach to the problem is to notify the user of an NLG system of ambiguities that might be present in generated text, and to advise him/her as to how serious these might be for readers of the text. The degree to which each ambiguity is considered serious is adjusted by the responses of users upon seeing instances of that ambiguity. The system learns from these interactions, and an ambiguity tolerance level is thereby established. The ultimate goal of this research is to build an adaptive ambiguity notification tool that incorporates these features and can be used with an NLG system.