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Knowledge-based natural language processing systems learn by reading, i.e., they process texts to extract knowledge. The performance of these systems crucially depends on knowledge about the domain of language itself, such as lexicons and ontologies to ground the semantics of the texts. In this paper we describe the architecture of the GIBRALTAR system, which is based on the OntoSem semantic analyzer, which learns by reading by learning to read. That is, while processing texts GIBRALTAR extracts both knowledge about the topics of the texts and knowledge about language (e.g., new ontological concepts and semantic mappings from previously unknown words to ontological concepts) that enables improved text processing. We present the results of initial experiments with GIBRALTAR and directions for future research.
Nirenburg et al. (Sat,) studied this question.
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