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<Paper uid="C96-2145">
  <Title>Error-tolerant Tree Matching</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Recent approaches in machine translation known as example-based translation rely on searching a database of previous translations of sentences or fragments of sentences, and composing a translation from the translations of any matching examples (Sato and Nagao, 1!)90; Nirenburg, Beale and l)omasnhev, 1994). The example database may consist, of paired text fragments, or trees as in Sat() and Nagao (1990). Most often, exact matches for new sentences or fragments will not be in the database, and one has to consider exampies that are &amp;quot;similar&amp;quot; to the sentence or fragment in question. This involves associatively searching through the database, tbr trees that are &amp;quot;close&amp;quot; to the query tree. This paper addresses the computational problem o\[ retrieving trees that are close to a given query tree in terms of a certain distance metric.</Paragraph>
    <Paragraph position="1"> The paper first presents the approximate tree matching problem in an abstract setting and presents an algorithm for approximate associative tree matching. The Mgorithm relies on linearizing the trees and then representing the complete database of trees as atrie structure which can be efficiently searched. The problem then reduces to sequence correction problem akin to standard spelling correction problem. The trie is then used with an approximate finite state recognition algorithm close to a query tree. Following some experimental results from a number of synthetic tree databases, the paper ends with conclusions.</Paragraph>
  </Section>
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