<?xml version="1.0" standalone="yes"?>
<Paper uid="P85-1009">
  <Title>REVERSIBLE AUTOMATA AND INDUCTION OF THE ENGLISH AUXILIARY SYSTEM</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> Formal inductive inference methods have rarely been applied to actual natural language systems. Linguists generally suppose that languages axe easy to learn because grarnmars axe highly constrained; no ~gener,d purpose&amp;quot; inductive inference methods are required. This assumption has generally led to fruitful insights on the nature of grammars. Yet it remains to determine whether ~ll of a language is learned in a granHnar-specilic manner. In this paper we show how to successfully apply one computationally emcient inductive inference algorithm to the acquisition of a domain of English sy'nca.x. Our results suggest that particular language subsystems can be learned by general induction procedures, given certain general constraints.</Paragraph>
    <Paragraph position="1"> The problem is that these methods are in general compntationally intractablc. Even for regular languages induction can be exponentially diiTicult (Gold, 1978). This suggests that there may be general constraints on the design of ce~ain linguistic subsystems to make them easy to learn by general inductive inference methods. We propose the constraint of k-reversibilit V as one such restriction. This constraint guarantees polynomial time inference (Angluin, 1982). In the remainder of this paper, we also show, by an explicit computer model, that the English auxiliary verb system meets this constraint, and so is easily inferred from a corpus. The theory gives one precise characterization of just whcre we may expect general inductive inference methods to be of v,~,lue in language acquisition.</Paragraph>
  </Section>
class="xml-element"></Paper>