This paper describes experiments for testing the power of large-scale resources for lexical selection in machine translation (MT) and cross-language information retrieval (CLIR). We adopt the view that verbs with similar argument structure share certain meaning components, but that those meaning components are more relevant to argument realization than to idiosyncratic verb meaning. We verify this by demonstrating that verbs with similar argument structure as encoded in Lexical Conceptual Structure (LCS) are rarely synonymous in WordNet. We then use the results of this work to guide our implementation of an algorithm for cross-language selection of lexical items, exploiting the strengths of each resource: LCS for semantic structure and WordNet for semantic content. We use the Parka Knowledge-Based System to encode LCS representations and WordNet synonym sets and we implement our lexical-selection algorithm as Parka-based queries into a knowledge base containing both information types.