Learning the Meaning of Unknown Words

Lexical acquisition is one of the most difficult problems in building up operational natural language processing systems. Automatic learning of new words (morphological, syntactic and semantic properties) is even harder. The paper discusses our solution for overcoming the lexical gaps. Learning what some unknown words might mean is abductively driven by the world knowledge and the local context of the unknowns, according to the pragmatic principle stating that abductive inference is inference to the best explanation. A generalization of the weight assignment for weighted abduction inference strategy is proposed in order to overcome some inherent difficulties existing in a standard cost-based abductive processing.