Statistical NLP has only recently begun to be concerned with deriving interpretations usable for inference and question answering (and as pointed out in the previous subsection, some of the literature in this area assumes that the NL text itself can and should be used as the basis for inference). However, there have been some noteworthy efforts to build statistical semantic parsers that learn to produce LFs after training on a corpus of LF-annotated sentences, or a corpus of questions and answers (or other exchanges) where the learning is “grounded” in a database or other supplementary models. We will mention examples of this type of work, and comment on its prospects, in section 8.