Key points are not available for this paper at this time.
We consider the problem of extracting specified types of information from natural language text. To properly analyze the text, we wish to apply semantic (selectional) constraints whenever possible; however, we cannot expect to have semantic patterns for all the input we may encounter in real texts. We therefore use preference semantics: selecting the analysis which maximizes the number of semantic patterns matched. We describe a specific information extraction task, and report on the benefits of using preference semantics for this task.
Grishman et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: