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.
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