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<Paper uid="P06-2076">
  <Title>Machine-Learning-Based Transformation of Passive Japanese Sentences into Active by Separating Training Data into Each Input Particle</Title>
  <Section position="3" start_page="0" end_page="587" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> This paper describes how passive Japanese sentences can be automatically transformed into active. There is an example of a passive Japanese sentence in Figure 1. The Japanese suffix reta functions as an auxiliary verb indicating the passive voice. There is a corresponding active-voice sentence in Figure 2. When the sentence in Figure 1 is transformed into an active sentence, (i) ni (by), which is a case postpositional particle with the meaning of &amp;quot;by&amp;quot;, is changed into ga, which is a case postpositional particle indicating the subjective case, and (ii) ga (subject), which is a case postpositional particle indicating the subjective case, is changed into wo (object), which is a case postpositional particle indicating the objective case. In this paper, we discuss the transformation of Japanese case particles (i.e., ni - ga) through machine learning.</Paragraph>
    <Paragraph position="1">  The transformation of passive sentences into active is useful in many research areas including generation, knowledge extraction from databases written in natural languages, information extraction, and answering questions. For example, when the answer is in the passive voice and the question is in the active voice, a question-answering system cannot match the answer with the question because the sentence structures are different and it is thus difficult to find the answer to the question. Methods of transforming passive sentences into active are important in natural language processing. null The transformation of case particles in transforming passive sentences into active is not easy because particles depend on verbs and their use.</Paragraph>
    <Paragraph position="2"> We developed a new method of transforming Japanese case particles when transforming passive Japanese sentences into active in this study. Our method separates training data into each input particle and uses machine learning for each input particle. We also used numerous rich features for learning. Our experiments confirmed that our method was effective.</Paragraph>
    <Paragraph position="3">  In this study, we did not handle the transformation of auxiliary verbs and the inflection change of verbs because these can be transformed based on Japanese grammar.</Paragraph>
    <Paragraph position="4">  inu ni watashi ga kama- reta.</Paragraph>
    <Paragraph position="5"> (dog) (by) (I) subjective-case postpositional particle (bite) passive voice (I was bitten by a dog.)  We used the Kyoto University corpus (Kurohashi and Nagao, 1997) to construct a corpus tagged for the transformation of case particles. It has approximately 20,000 sentences (16 editions of the Mainichi Newspaper, from January 1st to 17th, 1995). We extracted case particles in passive-voice sentences from the Kyoto University corpus. There were 3,576 particles. We assigned a corresponding case particle for the active voice to each case particle. There is an example in Figure 3. The two underlined particles, &amp;quot;ga&amp;quot; and &amp;quot;wo&amp;quot; that are given for &amp;quot;ni&amp;quot; and &amp;quot;ga&amp;quot; are tags for case particles in the active voice. We called the given case particles for the active voice target case particles, and the original case particles in passive-voice sentences source case particles. We created tags for target case particles in the corpus. If we can determine the target case particles in a given sentence, we can transform the case particles in passive-voice sentences into case particles for the active voice. Therefore, our goal was to determine the target case particles.</Paragraph>
  </Section>
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