<?xml version="1.0" standalone="yes"?>
<Paper uid="C96-1018">
  <Title>Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules</Title>
  <Section position="2" start_page="0" end_page="95" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> This paper shows how machine lem'ning techniques can be used to induce linguistically relevant rules and categories fl'om data. Statistical, connectionist, and machine learning induction (dataoriented approaches) are currently nsed mainly in language, engineering at)t)lications in order to alleviate the. linguistic knowledge acquisition bottleneck (the fact that lexical an(t grammatical knowledge usually has to be reformulated t'i'()iii scratch whenever a new application has to be built or an existing application ported to a new domain), and to solve problems with robustness and coverage inherent in knowledge-based (the.ory-oriente.d, hand-crafting) approaches. Linguistic relevance.</Paragraph>
    <Paragraph position="1"> or inspectability of the induced knowledge is usually not an issue in this type of research. \]n linguistics, on the other hand, it is usually agreed that while computer modeling is a useful (or essential) tool for enforcing internal consistency, completeness, and empirical validity of the linguistic theory being modeled, its role in formulating or evaluating linguistic theories is minimal.</Paragraph>
    <Paragraph position="2"> In this paper, we argue that machine learning techniques can also assist in linguistic theory for*Visiting fl'.llow at NIAS (Netherlands Instituee for Advanced Studies), Wassenaar, The. Netherlands.</Paragraph>
    <Paragraph position="4"> steven, gillis@uia, ua. ac. be peter, berck@uia, ua. ac. be mation by providing a new tool for the evaluation of linguistic hypotheses, for the extraction of rules front corpora, and for the discovery (if useflll linguistic categories. As a case. study, we apply Quinlan's C4.5 inductive machine learning me.thod (Quinlan, 1993) to a particular linguistic task (diminutive fi)rmation in Dutch) and show that it; can be use(l (i) to test linguistic hypotheses about this process, (ii) to discover interesting morphological rules, and (iii) discover interesting phonological categories. Nothing hinges on our choic.e of (\]4.5 as a rule induction mechanism. Wc chose it because it is an easily available and sophisticated instance of the class of rule induction algorithms.</Paragraph>
    <Paragraph position="5"> A second focus of this paper is the interaction between supervised and unsulmrvised machine learning me.thods in linguistic discovery, in supervised learning, the. learner is presented a set of examples (the experience of the system). These examples consist of an inImt outtmt association (in our case, e.g., a representation of a llotln as input, and the corresponding dimilmtive sul\[ix as output). Unsupervised learning methods do not 1)rovide the h',m'ner with inforlnatioil at)out the outf)ut to be generated; only the inputs ar(; I)resented to the learner as experience, not the target outputs.</Paragraph>
    <Paragraph position="6"> Unsupervised learning is necessarily more limited t, hm~ supervised learning; the only information it has to construct categories is the similarity between inputs. Unsupervised learning has been successflflly applied e.g. for the discovery of syntactic categories from corpora on the basis of distributional inforlnation about words (Finch and Chalet 1992, tIughes 1994, Schiitze 1995). We will show that it, is possible and useful to make use of unsupervised learning relative to a particular task which is being learned in a supervised way. In our experinmnt, phonological categories are discovered in an unsupervised way, as a side-effect of the supervised learning of a morphological problem. We will also show that this raises interesl;ing questions about, the. task-dependence of linguistic category systems.</Paragraph>
  </Section>
class="xml-element"></Paper>