An integrated, dual learner for grammars and ontologies

摘要

We introduce a dual-use methodology for automating the maintenance and growth of two types of knowledge sources, which are crucial for natural language text understanding—background knowledge of the underlying domain and linguistic knowledge about the lexicon and the grammar of the underlying natural language. A particularity of this approach is that learning occurs simultaneously with the on-going text understanding process. The knowledge assimilation process is centered around the linguistic and conceptual `quality' of various forms of evidence underlying the generation, assessment and on-going refinement of lexical and concept hypotheses. On the basis of the strength of evidence, hypotheses are ranked according to qualitative plausibility criteria, and the most reasonable ones are selected for assimilation into the already given lexical class hierarchy and domain ontology.