From Theory to Practice: Frame Semantics and the Design of FrameNet

Many of the major research efforts in Natural Language Processing (NLP) over the past two decades has focused on the design of systems for information extraction, question answering, and machine translation. Due to the combination of sophisticated statistical software with template-based algorithms tailored to particular domains, significant progress has been made in these areas of research. A large part of this progress has been made possible because of more refined models that rely on abstract grammatical rules responsible for parsing and generating sentences. As these systems became more sophisticated, it soon became obvious that the lexicon was a major bottleneck in NLP. For example, it was unclear how the vast amount of seemingly unstructured lexical information should be organized in such a way that it would be useful for a wide range of NLP applications. Fellbaum (1998c) summarizes the three main challenges underlying the design of lexical databases in terms of the following three questions: