<?xml version="1.0" standalone="yes"?>
<Paper uid="C96-2132">
  <Title>Zero Pronouns and Conditionals in Japanese Instruction Manuals</Title>
  <Section position="2" start_page="0" end_page="782" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> From simple electrical appliances to complex computer systems, almost all machines are accompanied by instruction manuals. Since recently there are many machines whose operating procedures are complicated, we have much trouble in many cases including translating their manuals into other languages, maintaining consistency between the description in manuals and the actual behavior of the machines. To solve these problems, we have to have a computer assisted system tbr processing Japanese manual sentences, especially tbr understanding manual sentences.</Paragraph>
    <Paragraph position="1"> A large number of researchers have gotten to grip with the method of understanding some types of text inehlding instruction nlanuals(Abe et al., 1988; Nomura, 1992; Eugenio, 1992). One of the most important matters of concern in tliese types of system is how we can fix ambiguities in semantic representations and fill uuderspecified parts of them. Generally speaking, almost all systems described above take the following scheme, l&amp;quot;irstly, each sentence in'a text is translated into a semantic representation, hi this process, the system uses only non-defeasible syntactic and semantic collstraints. Most of pragmatic information and colnrnousense knowledge are not used here, because the result of these knowledge would be overridden by some other information such as contextual intbrmation. Therefore the semantic representation would include some undetermined parts which would be fixed by other kind of information including context. This way of analysis is known as the Noudcfeasibility Thesis(Kameyama, 1995).</Paragraph>
    <Paragraph position="2"> Secondly, all of undetermined parts of the semantic representation are filled or settled by some kind of inferences based on ttie donlain knowledge.</Paragraph>
    <Paragraph position="3"> This type of method, which uses a, large ~%lnollut of domain knowledge, seems to be dominant froni the viewpoint of disambiguation. Moreover it scarcely depends on the language in use becmlsc the way of disambiguation is based oil the inference with a certain knowledge base. On the otlLer hand, ill order to use this method, we have to prepa.re the amount of knowledge being large euough to cope with various type of described objects.</Paragraph>
    <Paragraph position="4"> Unfortunately, so far we have not had such a commonsense knowledge base.</Paragraph>
    <Paragraph position="5"> One of ways to get rid of this situation is to adopt some knowledge which hardly depends on some particular domain. As such a kind of knowledge, we pay our attention to pragmatic constraints, which haw.' not been used sufficiently in the former methods. We expect that by pragmatic constraints the ambiguity in manual sentences would be resolw;d to some extent not in the process of inference but in the process of the translation of manual sentences into semantic representations. null We do not commit ourselves to the domain specitlc knowledge, but use some ontological knowledge in general manuals. For example, the col respondence of objects in the mamlal sentences to the objects in linguistic coustra.ints, like the speaker, the hearer, and so on. Note tha.t tile ontology in this paper does not refer to all of objects in the world described by manuals, like a certain part of machine. Aiming at iridependence from the doniain knowledge of objects, we adopt erie of general ontologies which is applicable to almost all manuals. In short, our scheme consists of tile following three parts: 1) a parser based on tim nondefeasiblity thesis, 2) pragmatic constraints specific to linguistic expressions, and 3) the general ontology of the worhl described by tnanuals.</Paragraph>
    <Paragraph position="6"> In the rest of this paper, we will focns on the zero pronoun resolution. In Jal)anese , zero pronouns frequently make a sentence ambiguous.</Paragraph>
    <Paragraph position="7"> Zero pronouns are ellipsis of obligatory ca.ses, which very frequently appear in Japanese sen- null tences. F, specially, subjects are omitted very o1: ten. It is called &amp;quot;zero subject.&amp;quot; In some sense, the resolution of zero pronouns' referents, especially the resolution of &amp;quot;zero subject&amp;quot;, is the essential part of the knowledge extraction fi'om JaI)atmse illanuals~ becanse once referents of zero prOllOtlllS are identified, we can use w~rious methods already been l)roposed to recognize the structure of sentence and to map it into the suitable knowledge representation. To capture pragmatic constraints, we have paid our attention to conditionals, which occur very frequently in instruction manuals. In this paper, we will show that in instruction manuals, the constraint of conditionals can I)e used to identify the referents of zero subjects. Although, of course, not all the zero pronomls can t)e solved with the constraints shown in the paper, our examination for a lot of manual sentences shows that the constraints work very effectively and accurately in sentences with conditionals.</Paragraph>
    <Paragraph position="8"> Now we have to deline the term 'subject' we used in this paper. Generally, the term 'subject' d('notes a nominative from the grammatical point of view. In this paper, however, we will use the term SUBJECT to denote a main participant of the sentence. Roughly speaking, in tile active voice, the SUBJECT is the nominative., on the other hand, in the passive voice, the SUBJECT is the nominative of the corresponding sentence in the active voice.</Paragraph>
  </Section>
class="xml-element"></Paper>