TAILIEUCHUNG - Báo cáo khoa học: "AUTOMATIC LEARNING OF WORD TRANSDUCERS FROM EXAMPLES"

These two tasks are in fact closely related in that {I) the correct phoneme transcription may mirror an underlying morphological structure, like for "asoc/a/" whose phonemic form is "asos j a l " rather than " a . | AUTOMATIC LEARNING OF WORD TRANSDUCERS FROM EXAMPLES Michel Gilloux Centre National d Etudes des Télécommunlcations LAA SLC AIA Route de Trégastel BP 40 F-22301 Lannlon Cedex FRANCE e-mail gHloux@ ABSTRACT This paper describes the application of markovlan learning methods to the inference of word transducers. We show how the proposed method dispenses from the dtfficult design of hand-crafted rules allows the use of weighed non deterministic transducers and Is able to translate words by taking Into account their whole rather than by making decisions locally. These arguments are Illustrated on two examples morphological analysis and grapheme-to-phoneme transcription. INTRODUCTION Several tasks associated with electronic lexicons may be viewed as transductions between character strings. This may be the decomposition of words Into morphemes In morphology or the grapheme-to-phoneme transcription in phonology. In the first case one has for example to decompose the French word chronometrage Into the sequence of affixes chrono mètre er -age . In the second abstentr should be translated Into absteniR or apsteniR 1. Most of the proposed methods In the 1These two tasks are In fact closely related In that 1 the correct phoneme transcription may mirror an underlying morphological structure like for asocial whose phonemic form is aaos jal rather than azos jal due to the decomposition a social and 2 the surface form of a derived word may depend on the pronunciation of its component morphemes like for dé hamacher which results in déhamacher and not déshamache f. domain Catach 1984 Danlos et al. 1986 Koskenrfieml 1983 Laporte 1988 Ritchie et al. 1987 Tufis 1989 Véronls 1988 are based on the availability of local rules whose combination either through direct Interpretation or by being compiled form the target transducer. Although these methods make It possible - at least In theory - to design suitable transducers provided that the rule description language has the .

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• AUTOMATIC LEARNING
• WORD TRANSDUCERS FROM EXAMPLES
• Michel Gilloux
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