NETL: A System for Representing and Using Real-World Knowledge 
 
 Computer programs so far have not fared well in modeling language acquisition . For one thing, learning methodology applicable in general domains does not readily lend itself in the linguistic domain . For another, linguistic representation used by language processing systems is not geared to learning . We introduced a new linguistic representation , the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitate language acquisition . From this, a language learning model was implemented in the program RINA , which enhances its own lexical hierarchy by processing examples in context. We identified two tasks: First, how linguistic concepts are acquired from training examples and organized in a hierarchy ; this task was discussed in previous papers [Zernik87]. Second, we show in this paper how a lexical hierarchy is used in predicting new linguistic concepts . Thus, a program does not stall even in the presence of a lexical unknown , and a hypothesis can be produced for covering that lexical gap . 