Blasting open a choice space: Learning inflectional morphology for NLP
This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each, design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A running example throughout the text is taken from the paradigmatic morphology elicitation module of a system called Boas, which elicits knowledge to support a machine translation system. The main point of the article is an argument about the necessity to analyze the design choice space for complex natural language processing (NLP) systems early, comprehensively, and overtly.
morphology knowledge elicitation machine translation natural language processing system architectures