Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules

Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules Walter Daelemans* CL & AI, Tilburg Uniw'xsity P.().Box 90153, 5000 LE Tilburg The Netherlands walter, dae-I emans@kub, nl Abstract We describe a case study in tit(', ap- plication of symbolic machinc learning techniques for the discow;ry of linguis- tic rules and categories. A supervised rule induction algorithm is used to learn to predict the. correct dimilmtive suffix given the phonological representation of Dutch nouns. The system produces rules which are comparable, to rules proposed by linguists, l,Slrthermore, in the process of learning this morphological task, the phonemes used are grouped into phono- logically relevant categories. We discuss the relevance of our method for linguis- tics attd language technology. 1 Introduction This paper shows how machine lem'ning tech- niques can be used to induce linguistically relevant rules and categories fl'om data. Statistical, con- nectionist, and machine learning induction (data- oriented approaches) are currently nsed mainly in language, engineering at)t)lications in order to alle- viate the. linguistic knowledge acquisition bottle- neck (the fact that lexical an(t grammatical knowl- edge 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 cover- age inherent in knowledge-based (the.ory-oriente.d, hand-crafting) approaches. Linguistic relevance. or inspectability of the induced knowledge is usu- ally not an issue in this type of research. ]n lin- guistics, on the other hand, it is usually agreed that while computer modeling is a useful (or essen- tial) tool for enforcing internal consistency, com- pleteness, and empirical validity of the linguistic theory being modeled, its role in formulating or evaluating linguistic theories is minimal. 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. Peter Berck and Steven Gillis Linguistics, University of Antwerp Unive.rsiteitsplein 1, 2610 WiMjk l ~elgium steven, gillis@uia, ua. ac. be peter, berck@uia, ua. ac. be mation by providing a new tool for the evalua- tion 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 hypothe- ses 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 so- phisticated instance of the class of rule induction algorithms. A second focus of this paper is the interac- tion between supervised and unsulmrvised ma- chine 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 …

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