A Lexical Knowledge Base from a Machine Readable Dictionary

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Machine-readable dictionaries (MRD's) contain knowledge about language and the world essential for large-scale tasks in natural language processing (NLP). This knowledge, however, collected and recorded by lexicographers for human readers, in not presented in a principled enough manner for MRD's to be used directly as tools for NLP tasks. What the NLP research community needs is machine tractable dictionaries (MTD's); that is MRD's transformed into a format for NLP tasks. Previous work has developed a range of tools for extracting information from MRD's for NLP tasks in general. This work will combine these methods based on their respective strengths. It will define the SPIRAL methodology which combines various elements of the three methods into a single coherent procedure. Different forms of knowledge are derived from each individual method and each is incomplete in certain respects; yet the combination of the three yields better results than could any individual method. In particular, the goal is to construct an MTD as a data-base of lexical facts, together with a network of statistically-related word sense. The MTD will support the automatic construction of lexicons for many types of NLP systems, form those using traditional methods based on syntax and semantics to those based wholly on statistical methods.