<?xml version="1.0" standalone="yes"?>
<Paper uid="P98-1075">
  <Title>Growing Semantic Grammars</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> A critical path in the development of natural language understanding (NLU) modules lies in the difficulty of defining a mapping from words to semantics: Usually it takes in the order of years of highly-skilled labor to develop a semantic mapping, e.g., in the form of a semantic grammar, that is comprehensive enough for a given domain. Yet, due to the very nature of human language, such mappings invariably fail to achieve full coverage on unseen data. Acknowledging the impossibility of stating a priori all the surface forms by which a concept can be expressed, we present GsG: an empathic computer system for the rapid deployment of NLU front-ends and their dynamic customization by non-expert end-users.</Paragraph>
    <Paragraph position="1"> Given a new domain for which an NLU front-end is to be developed, two stages are involved. In the authoring stage, GSQ aids the developer in the construction of a simple domain model and a kernel analysis grammar. Then, in the run-time stage, GSG provides the end-user with an interactive environment in which the kernel grammar is dynamically extended. Three learning methods are employed in the acquisition of semantic mappings from unseen data: (i) parser predictions, (ii) hidden understanding model, and (iii) end-user paraphrases. A baseline version of GsG has been implemented and prellminary experiments show promising results.</Paragraph>
  </Section>
class="xml-element"></Paper>