<?xml version="1.0" standalone="yes"?>
<Paper uid="C04-1196">
  <Title>Understanding Students' Explanations in Geometry Tutoring</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 The System's Architecture
</SectionTitle>
    <Paragraph position="0"> The system's overall architecture is presented in Figure 1 below. The interface module takes the input sentence from the tutor, word by word, in real time, and after some preprocessing and spelling checking, it passes it to the chart parser.</Paragraph>
    <Paragraph position="1"> It also passes the results back to the tutor. The chart parser is the main engine of the system. It uses linguistic knowledge about the target natural language from the unification grammar and the lexicon. The parser used currently is LCFlex, a left-corner active-chart parser developed at the University of Pittsburgh (Rose and Lavie 1999).</Paragraph>
    <Paragraph position="2"> The parser calls the feature structure unifier in order to process restrictions attached to grammar rules and build feature structures for each phrase successfully recognized. These feature structures store lexical, syntactic, and semantic properties of corresponding words and phrases. The parser uses an active chart that serves as a storage area for all valid phrases that could be built from the word sequence it received up to each point in the  Some of the restrictions in the grammar are directives to the description logic system, currently Loom (MacGregor 1991). The logics system relies on a model of the domain of discourse, encoded as concepts, relations, and production rules, in the two knowledge bases.</Paragraph>
    <Paragraph position="3"> Concepts and relations stand for predicates in the underlying logic. Production rules perform additional inferences that are harder to encode into concepts and/or relations.</Paragraph>
    <Paragraph position="4"> The linguistic inference module mediates the interaction between the feature structure unifier and the description logics system. This module is responsible for performing semantic processing that is specific to natural language understanding, like compositional semantics, resolving metonymies and references, and performing semantic repairs.</Paragraph>
    <Paragraph position="5"> Based on this knowledge base, the logic system builds compositionally a model-theoretic semantic representation for the sentence, as a set of instances of various concepts connected through various relations. An instance corresponds to a discourse referent in the sentence. The logic system performs forward-chaining classification of resulting instances, and also ensures semantic coherence of the semantic representation.</Paragraph>
    <Paragraph position="6"> The logic system then uses a classifier to evaluate the semantic representation against a classification hierarchy of valid representations</Paragraph>
  </Section>
class="xml-element"></Paper>