Embedded machine learning systems for natural language processing: a general framework

This paper presents Kenmore, a general framework for knowledge acquisition for natural language processing (NLP) systems. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using robust sentence analysis and embedded symbolic machine learning techniques while requiring only minimal human intervention. By treating all problems in ambiguity resolution as classiication tasks, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. In a series of experiments, we demonstrate the successful use of Kenmore for learning solutions to several problems in lexical and structural ambiguity resolution. We argue that the learning and knowledge acquisition components should be embedded components of the NLP system in that (1) learning should take place within the larger natural language understanding system as it processes text, and (2) the learning components should be evaluated in the context of practical language-processing tasks.