Exploring Model Building for Natural Language Understanding

In this paper the use of model building for natural language understanding in practical systems is investigated. After outlining several interpretation tasks where model construction would be useful, we perform experiments using two state-of-the-art model builders on the interpretation of imperatives in discourse. The results are acceptable for small discourses and small domains, but don’t scale up for larger domains or longer discouses: the complexity of model generation rises sharply in relation to the difficulty of a task and the size of the dialogue. Several suggestions are provided for future research to improve the performance of model building for larger domains and richer texts.